Overall Recommendation & Rating
8
Excellent
Visual Clash: A/B Testing Watch Screens to Elevate Marketing Strategies
Candidate Details
REPORT SUMMARY
Positive Feedback
  • Excellent skills in Python for data loading and analysis.
  • Top-notch code clarity, performance efficiency, and syntax.
  • Proficiency in loading test campaign data with clear code structure.
  • Good understanding and execution of task requirements.
  • Effective implementation of two-sample t-test as requested.
Scope of Improvement
  • Enhance comments for detailed explanations in code steps
  • Focus on improving code commenting practices
  • Add detailed comments for rationale behind column name changes
  • Improve code clarity with descriptive comments and structured code
  • Consider optimizing code performance with advanced techniques
Performance Based Rating
9
Code Syntax
9
Code Clarity
7
Well Commented
10
Task Understanding
9
Performance Efficiency
Role and Skill Based Rating
9
Python
8
A/B Testing
9
Data Analyst
9
Data Analysis
Project Description

Timetrends is conducting an A/B test to evaluate the effectiveness of a new watch screen design (B) compared to the existing design (A) in boosting sales and engagement. The dataset includes campaign details such as date, spend, impressions, and user interactions. The null hypothesis (H0) states that the new design does not improve campaign performance, while the alternative hypothesis (H1) suggests the new design outperforms the existing one. As the data detective, your task is to analyze the metrics and determine which watch screen design is more effective in driving sales and engagement, guiding future marketing strategies for the company.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 10
  • Positive Feedback: The code is free from syntax errors and follows Python coding standards.
  • Area of Improvement: No areas of improvement identified in code syntax.
  • Final Verdict: Excellent adherence to code syntax standards.
Code Clarity
  • Rating: 10
  • Positive Feedback: The code is clear and concise, following the task requirements accurately. Variable names are meaningful and descriptive.
  • Area of Improvement: No areas of improvement identified in code clarity.
  • Final Verdict: Excellent code clarity, well done!
Well Commented
  • Rating: 8
  • Positive Feedback: The code includes comments for importing Pandas and reading the dataset. Comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the purpose of loading the control campaign data.
  • Final Verdict: Good use of comments, but could be enhanced with more detailed explanations.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements in the first attempt.
  • Area of Improvement: No areas of improvement identified in task understanding.
  • Final Verdict: Outstanding task understanding and execution.
Performance Efficiency
  • Rating: 10
  • Positive Feedback: The code efficiently loads the control campaign data using Pandas read_csv function with the specified separator.
  • Area of Improvement: No areas of improvement identified in performance efficiency.
  • Final Verdict: Great performance efficiency demonstrated in the code.
Role And Skill Based Rating
Python
  • Rating: 10
  • Positive Feedback: The user has effectively utilized Python to load and analyze the control campaign data.
  • Area of Improvement: No areas of improvement identified in Python skills.
  • Final Verdict: Excellent demonstration of Python skills.
Data Analyst
  • Rating: 10
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task requirements and the skills demonstrated in the solution code.
  • Area of Improvement: No areas of improvement identified in the Data Analyst role.
  • Final Verdict: Excellent alignment with the Data Analyst role.
Data Analysis
  • Rating: 10
  • Positive Feedback: The user has effectively demonstrated data analysis skills by loading and inspecting the control campaign data.
  • Area of Improvement: No areas of improvement identified in data analysis skills.
  • Final Verdict: Strong proficiency in data analysis skills.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. It uses appropriate indentation and spacing, enhancing readability.
  • Area of Improvement: No specific syntax errors found in the code.
  • Final Verdict: The code syntax is well-structured and adheres to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with appropriate variable naming and structure. It effectively loads the test campaign data into a Pandas DataFrame.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of loading the test campaign data and how it fits into the overall data analysis process.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to indicate the purpose of reading the dataset and inspecting the data. These comments provide basic clarity.
  • Area of Improvement: Enhance the comments to explain the data inspection process and any further analysis steps that may follow after loading the data.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully loaded the test campaign data into a Pandas DataFrame as per the task requirements. The code fulfills the task effectively.
  • Area of Improvement: Further elaboration on the data inspection process and potential data analysis steps could enhance the task understanding rating.
  • Final Verdict: The user demonstrates a good understanding of the task by correctly loading and inspecting the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently loads the test campaign data using Pandas read_csv function with the specified separator. It demonstrates good performance efficiency.
  • Area of Improvement: No specific improvements needed for performance efficiency in this code.
  • Final Verdict: The code shows optimal performance efficiency in loading the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to read and load the test campaign data into a Pandas DataFrame. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Further exploration of advanced Python functionalities and libraries for data analysis could enhance the Python skill rating.
  • Final Verdict: The user shows strong Python skills in this task.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively demonstrates data analysis skills by loading and inspecting the test campaign data using Pandas in Python.
  • Area of Improvement: To further enhance the Data Analysis skill rating, the user could delve into more complex data manipulation and visualization techniques.
  • Final Verdict: The user exhibits strong data analysis skills in this task.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. There are no syntax errors present in the provided code.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform spacing and formatting throughout the script.
  • Final Verdict: The code syntax is well-maintained and follows best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. The variable names are descriptive and relevant to the task. The user has successfully renamed the columns in the DataFrame as required.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the code block and provide more context.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the column renaming process. The comments are clear and provide basic insights into the code logic.
  • Area of Improvement: To improve code commenting, the user could add more detailed comments to elaborate on the specific steps taken during column renaming.
  • Final Verdict: The code has adequate comments for understanding, but additional details would enhance clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by successfully renaming the columns in the DataFrame as specified. The code fulfills the task effectively.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for column renaming and consider edge cases that may arise.
  • Final Verdict: The user has a solid grasp of the task requirements and has implemented the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by renaming the columns in the DataFrame accurately and succinctly. It achieves the task with optimal resource usage.
  • Area of Improvement: To further enhance performance efficiency, the user could explore methods to optimize the renaming process for larger datasets.
  • Final Verdict: The code shows high performance efficiency with minimal complexity.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced features and libraries within Python for data manipulation tasks.
  • Final Verdict: Strong Python skills demonstrated in the solution code.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user has approached the task with a data analyst mindset by effectively transforming and simplifying column names for better understanding.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve deeper into data manipulation techniques and analysis methodologies.
  • Final Verdict: Shows potential for growth in the Data Analyst role through practical data handling tasks.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows good coding standards with proper indentation and spacing. It adheres to Python syntax rules effectively.
  • Area of Improvement: To maintain code syntax consistency, ensure that the naming conventions and formatting are uniform throughout the code.
  • Final Verdict: The code syntax is well-maintained with proper indentation and adherence to Python coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided has clear and descriptive variable renaming for the test campaign data columns. It effectively conveys the purpose of each column name.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the rationale behind the column name changes. This can help in understanding the context of the data.
  • Final Verdict: Overall, the code clarity is good with meaningful variable names, but additional comments can improve the overall readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the inspection of data after renaming the columns. This provides some context on the code execution.
  • Area of Improvement: To improve code commenting, consider adding comments to explain the purpose of each column name change. This can help in understanding the data transformation process.
  • Final Verdict: While there is some commenting present, additional comments detailing the column name changes can enhance the overall clarity of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully renaming the columns in the 'test_df' DataFrame as per the provided list. The code execution aligns with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced data manipulation techniques to optimize the column renaming process.
  • Final Verdict: The user has shown a strong grasp of the task requirements with accurate column renaming in the DataFrame.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently renames the columns in the 'test_df' DataFrame without any unnecessary complexities. It achieves the task with optimal performance in terms of speed and memory usage.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is concise and avoids redundant operations.
  • Final Verdict: The code demonstrates high performance efficiency by efficiently updating the column names in the DataFrame.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to rename the columns in the DataFrame, showcasing proficiency in Python programming for data manipulation tasks.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data manipulation libraries and techniques in Python.
  • Final Verdict: The user demonstrates strong Python skills in data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements of organizing and renaming data columns. The skills demonstrated in the solution code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, consider delving into more complex data manipulation tasks and data visualization techniques.
  • Final Verdict: The user effectively showcases skills relevant to the role of a Data Analyst in the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applies data analysis skills to transform and organize the test campaign data columns, showcasing proficiency in data analysis tasks.
  • Area of Improvement: To further enhance data analysis skills, consider exploring statistical analysis and visualization techniques for deeper insights.
  • Final Verdict: The user demonstrates strong data analysis skills in organizing and transforming data columns.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards effectively.
  • Area of Improvement: Ensuring consistency in coding style and formatting throughout the code could further enhance the syntax.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Enhancing the structure of the code could further improve clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor enhancements.
Well Commented
  • Rating: 6
  • Positive Feedback: Some comments are present in the code, providing a basic understanding of the operations. The code is relatively simple, so fewer comments are needed.
  • Area of Improvement: Adding more detailed comments to explain the logic behind each step and the expected outcomes would enhance the code's readability.
  • Final Verdict: While there are some comments, more detailed explanations would improve the overall comment quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the steps to check for missing values in the control campaign data. The output reflects a good understanding of the task requirements.
  • Area of Improvement: Considering the task was straightforward, the user could have provided more detailed comments to showcase a deeper understanding of the process.
  • Final Verdict: The user has demonstrated a strong understanding of the task with minor areas for improvement in providing detailed comments.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of methods to check for missing values. It efficiently calculates the sum of null values for each column.
  • Area of Improvement: Optimizing the code further for better performance could be considered, although the current implementation is already quite efficient.
  • Final Verdict: The code shows good performance efficiency in handling missing values.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to check for missing values in the control campaign data. The code syntax and implementation demonstrate a strong command of Python.
  • Area of Improvement: Further enhancing Python skills by exploring advanced concepts and libraries could lead to even more efficient solutions.
  • Final Verdict: The user exhibits a high level of proficiency in Python programming for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-aligned with the task requirements and the skills demonstrated in the solution code. The implementation reflects typical tasks performed by a Data Analyst.
  • Area of Improvement: To further excel in the role of a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows competence in fulfilling the responsibilities of a Data Analyst through the provided solution code.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis skills to check for missing values in the control campaign data. The implementation reflects a good understanding of data manipulation.
  • Area of Improvement: Exploring more complex data analysis techniques and tools could further enhance the user's proficiency in data analysis.
  • Final Verdict: The user demonstrates strong data analysis skills in handling missing values in the dataset.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: Ensure consistency in variable naming conventions and consider adding docstrings to functions for better code documentation.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for improvement in documentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to follow. The lambda function used to fill missing values with the mean of each column is concise and effective.
  • Area of Improvement: Consider adding comments to explain the lambda function and the purpose of filling missing values with column means. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to describe the purpose of filling missing values with column means. These comments provide basic clarity on the code logic.
  • Area of Improvement: To improve code documentation, consider adding more detailed comments to explain the steps involved in handling missing values and the significance of using column means.
  • Final Verdict: While there are some comments present, additional detailed explanations can enhance the overall code documentation.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by efficiently using the lambda function to fill missing values with column means.
  • Area of Improvement: To further enhance task understanding, consider exploring different methods for handling missing values and experimenting with alternative approaches.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for further exploration and improvement.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently fills missing values with the mean of each column using a lambda function. This approach ensures a streamlined process for handling missing data.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the lambda function for better speed and resource utilization.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to handle missing values in the control campaign data. The code demonstrates proficiency in Python programming.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced techniques for data manipulation and analysis.
  • Final Verdict: The user has shown strong Python skills in implementing the solution code.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of handling missing values in the control campaign data. The solution code showcases skills relevant to data analysis tasks.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider delving into advanced data cleaning techniques and statistical analysis methods.
  • Final Verdict: The user's role as a Data Analyst is well-reflected in the task solution, demonstrating proficiency in data analysis.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to fill missing values in the control campaign data. The solution code reflects a good understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring different data imputation methods and statistical approaches.
  • Final Verdict: The user has demonstrated strong data analysis skills in handling missing values.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows good coding standards. Proper indentation and spacing are maintained throughout the script.
  • Area of Improvement: Ensure consistency in naming conventions and formatting. Double-check for any potential syntax errors that might be missed.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output seems to be correct for the given task.
  • Area of Improvement: Consider adding more comments to explain the purpose of the code and the expected output. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the code. Comments are clear and provide insights into the logic.
  • Area of Improvement: Consider adding more detailed comments to explain the steps taken and the expected outcomes. Ensure all complex logic is well-documented.
  • Final Verdict: The code has some comments but could benefit from more detailed explanations for better understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The code correctly fulfills the task requirements by using the .isnull() and .sum() methods to identify and count null values in the test campaign data.
  • Area of Improvement: Further enhance task understanding by exploring different methods to handle missing values and considering edge cases in data analysis.
  • Final Verdict: The code effectively addresses the task of checking for missing values in the test campaign data.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses the .isnull() and .sum() methods to calculate the sum of null values in the DataFrame. It performs the task with optimal time and space complexity.
  • Area of Improvement: No major issues found in performance efficiency. Ensure the code maintains efficiency as the dataset size increases.
  • Final Verdict: The code demonstrates high performance efficiency in handling missing values in the test campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to manipulate DataFrame and perform data analysis tasks. The code demonstrates proficiency in using pandas methods for data processing.
  • Area of Improvement: Continue to explore advanced features of pandas and data manipulation techniques in Python to enhance data analysis skills.
  • Final Verdict: The user shows strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst aligns well with the task of checking for missing values in the test campaign data. The skills demonstrated in the code reflect the responsibilities of a Data Analyst.
  • Area of Improvement: Continue to deepen knowledge in data analysis techniques and explore different data cleaning methods to excel in the role of a Data Analyst.
  • Final Verdict: The user's performance aligns with the role of a Data Analyst in handling data quality issues.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to identify and count missing values in the test campaign data. The code demonstrates a good understanding of data manipulation.
  • Area of Improvement: Further explore statistical analysis methods and data visualization techniques to enhance data analysis skills.
  • Final Verdict: The user exhibits strong data analysis skills in handling missing values in the test campaign data.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing, adhering to good coding standards.
  • Area of Improvement: To maintain code syntax consistency, ensure that all code elements follow the same formatting conventions throughout the script.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided is clear and easy to understand. Variable names are relevant to their purpose, and the solution correctly uses the describe() method on the 'control_df' DataFrame.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and the expected output.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to inspect the data, which is helpful for understanding the flow of the code.
  • Area of Improvement: To improve the code commenting, consider adding more detailed comments to explain the logic behind using the describe() method and the significance of the extracted statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has successfully completed the task by using the describe() method on the 'control_df' DataFrame to extract descriptive statistics.
  • Area of Improvement: To further enhance task understanding, consider providing more detailed insights into how the extracted statistics can be utilized for marketing strategy decisions.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by using the describe() method to efficiently extract descriptive statistics from the DataFrame.
  • Area of Improvement: To further enhance performance efficiency, ensure that the code is optimized for larger datasets and consider any potential memory usage optimizations.
  • Final Verdict: The code shows efficient performance in extracting insights from the control campaign data.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to extract descriptive statistics from the DataFrame, showcasing proficiency in data analysis with Python.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden expertise.
  • Final Verdict: The user demonstrates strong Python skills in data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's performance aligns well with the responsibilities and requirements typically associated with the role of a Data Analyst.
  • Area of Improvement: To further develop as a Data Analyst, consider delving deeper into statistical modeling and data visualization to enhance decision-making capabilities.
  • Final Verdict: The user shows proficiency in tasks relevant to the role of a Data Analyst.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to extract insights from the control campaign data, showcasing proficiency in data analysis.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more advanced statistical analysis methods and visualization techniques.
  • Final Verdict: The user demonstrates strong data analysis skills in interpreting and extracting insights from data.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Proper indentation and spacing are maintained throughout the code.
  • Area of Improvement: To maintain consistency, ensure that the code follows a consistent commenting style and adheres to PEP 8 guidelines for Python code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The solution provides the required output for the task.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the code and any specific details that might not be immediately clear to others.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to indicate the purpose of the code snippet. The comments help in understanding the basic functionality of the code.
  • Area of Improvement: To improve code commenting, consider adding more detailed comments to explain the logic behind the data analysis steps and the significance of the descriptive statistics.
  • Final Verdict: While there are some comments present, additional detailed comments would enhance the overall clarity of the code.
Task Understanding
  • Rating: 10
  • Positive Feedback: The user has successfully completed the task as per the requirements. The code accurately utilizes the describe() method to generate descriptive statistics for the DataFrame.
  • Area of Improvement: No specific areas of improvement identified in task understanding. The user has effectively implemented the task.
  • Final Verdict: The user has a strong understanding of the task requirements and has executed the solution accurately.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of the describe() method to generate descriptive statistics for the DataFrame. It executes the task with optimal performance.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing any additional data processing steps that could be included in the analysis.
  • Final Verdict: The code shows high performance efficiency with minimal room for improvement.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform data analysis tasks. The code demonstrates proficiency in Python programming for data manipulation and analysis.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced data analysis techniques and libraries to broaden the scope of data analysis capabilities.
  • Final Verdict: The user shows strong Python skills suitable for a Data Analyst role.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively applied data analysis techniques to generate descriptive statistics for the DataFrame. The code reflects a strong understanding of data analysis principles.
  • Area of Improvement: To further enhance data analysis skills, consider exploring more complex data analysis scenarios and statistical methods.
  • Final Verdict: The user demonstrates proficiency in data analysis skills suitable for a Data Analyst role.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and spacing. It follows Python coding standards and best practices.
  • Area of Improvement: Ensure consistent naming conventions for variables and functions. Check for any potential syntax errors that might impact code execution.
  • Final Verdict: The code syntax is well-maintained and aligns with Python coding standards, with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to the task. The output matches the requirements of conducting a Normality check on the 'Purchase Number' for both groups.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in coding style throughout the script.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and coding style.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import and data processing steps. The comments are clear and provide insights into the code logic.
  • Area of Improvement: Add more detailed comments to elaborate on the data manipulation and result interpretation. Ensure all complex logic is well-documented for better understanding.
  • Final Verdict: While the code has comments, adding more detailed explanations would enhance the overall clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly applying the Shapiro-Wilk test on the 'Purchase Number' column for both groups. The output DataFrame captures the necessary results.
  • Area of Improvement: To further improve, consider exploring additional statistical tests for normality checks and provide more detailed interpretations of the test results.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for enhancement in exploring diverse statistical tests and result interpretations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently performs the Normality check using the Shapiro-Wilk test for both the control and test groups. It handles the data appropriately and generates the required DataFrame.
  • Area of Improvement: Optimize the code further for better memory usage and consider error handling for potential exceptions during the test execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential enhancements for optimization and error handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to implement the statistical test and data manipulation required for the task. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring advanced libraries and functions for statistical analysis and data visualization.
  • Final Verdict: Strong proficiency in Python demonstrated through the task implementation with potential for growth in utilizing advanced Python functionalities.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's role as a Data Analyst is well-reflected in the task execution involving statistical analysis and result interpretation. The code aligns with typical responsibilities of a Data Analyst.
  • Area of Improvement: To excel as a Data Analyst, focus on enhancing statistical modeling skills, data visualization techniques, and communication of analytical findings.
  • Final Verdict: The user showcases proficiency in the Data Analyst role through the task implementation, with potential for further development in advanced analytical methods.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively applied data analysis techniques to conduct a Normality check on the 'Purchase Number' column for both groups. The results interpretation aligns with data analysis principles.
  • Area of Improvement: To further strengthen data analysis skills, explore more statistical tests, data visualization methods, and hypothesis testing approaches.
  • Final Verdict: Solid demonstration of data analysis skills in the task execution with opportunities for growth in exploring diverse analytical methods.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, with proper indentation and spacing. It follows Python coding standards and conventions.
  • Area of Improvement: To further enhance code syntax, consider adding docstrings to functions and providing more detailed variable descriptions.
  • Final Verdict: The code syntax is well-maintained and aligns with best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with relevant variable names and a straightforward approach to implementing the t-test. It correctly compares the 'Purchase Number' columns of 'control_df' and 'test_df'.
  • Area of Improvement: Consider adding more comments to explain the purpose of the t-test and the significance of the results. Additionally, providing a brief overview of the data and context could enhance the code clarity further.
  • Final Verdict: Overall, the code clarity is good, but additional comments and context could improve understanding.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the import statement and the inspection of data after the t-test. These comments provide a basic understanding of the code flow.
  • Area of Improvement: To improve the commenting, consider adding comments to describe the purpose of the t-test, the expected results, and any assumptions made during the analysis.
  • Final Verdict: While there are some comments present, additional explanations could enhance the code's clarity and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user correctly applies the independent two-sample t-test as required by the task description. The 't_stat' and 'p_value' are stored appropriately after the test.
  • Area of Improvement: To improve task understanding further, consider explaining the interpretation of the t-test results and how they relate to the significance level of 0.05.
  • Final Verdict: The user demonstrates a strong understanding of the task requirements, with minor room for improvement in result interpretation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently applies the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'. It correctly stores the t-statistic in 't_stat' and the p-value in 'p_value'.
  • Area of Improvement: To enhance performance efficiency further, consider checking for any assumptions or requirements of the t-test that need to be met before applying it.
  • Final Verdict: The code demonstrates good performance efficiency in executing the t-test.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Python to import the necessary function and perform the t-test on the data. The code syntax and structure align well with Python standards.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced statistical analysis techniques and incorporating them into data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in Python for statistical analysis tasks.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user correctly applies the concept of A/B testing by comparing the 'Purchase Number' data between control and test groups using a two-sample t-test.
  • Area of Improvement: To strengthen A/B testing skills, consider exploring different types of hypothesis testing and understanding the implications of various significance levels.
  • Final Verdict: The user shows a good understanding of A/B testing principles, with room for further exploration and learning.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user effectively conducts data analysis by comparing the 'Purchase Number' data between control and test groups using statistical testing.
  • Area of Improvement: To enhance data analysis skills, consider delving deeper into interpreting statistical results and making data-driven decisions based on the outcomes.
  • Final Verdict: The user showcases strong data analysis skills in implementing statistical tests for comparison.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions and formatting for better code readability.
  • Final Verdict: The code syntax is good with minor room for improvement.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and meaningful. The solution code correctly calculates the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the calculations. It helps in understanding the code flow.
  • Area of Improvement: Add more detailed comments to clarify the steps involved in calculating the 'Cost per Conversion' and the subsequent mean calculation.
  • Final Verdict: While there are some comments, more detailed explanations would enhance the code's clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the 'Cost per Conversion' for both control and test groups.
  • Area of Improvement: Further enhance task understanding by providing more detailed comments and explanations in the code.
  • Final Verdict: The user shows a strong grasp of the task with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the 'Cost per Conversion' for both control and test groups. It uses the mean function to calculate the average cost per conversion.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain efficiency.
  • Final Verdict: The code demonstrates good performance efficiency.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to perform the required calculations and data manipulation tasks.
  • Area of Improvement: Further enhance Python skills by exploring advanced data analysis techniques and libraries.
  • Final Verdict: Strong proficiency in Python demonstrated in the solution code.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully applied A/B testing concepts to compare the 'Cost per Conversion' between control and test groups.
  • Area of Improvement: Deepen understanding of statistical significance testing methods in A/B testing scenarios.
  • Final Verdict: Good application of A/B testing principles in the task solution.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user has effectively utilized data analysis techniques to calculate and compare the 'Cost per Conversion' for the control and test groups.
  • Area of Improvement: Explore more advanced data analysis methods and statistical tests for deeper insights.
  • Final Verdict: Strong data analysis skills demonstrated in the task solution.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code syntax is correct with proper indentation and spacing.
  • Area of Improvement: Ensure consistency in coding style throughout the script for better readability.
  • Final Verdict: The code syntax is good, but maintaining consistent coding style can further enhance it.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided is clear and easy to understand. The variable names are descriptive, and the purpose of the code is evident.
  • Area of Improvement: Consider adding more comments to explain the logic behind the concatenation of the DataFrames for better clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments can enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes a comment to indicate the purpose of concatenating the DataFrames.
  • Area of Improvement: Add more comments to explain the steps involved in the data merging process for better understanding.
  • Final Verdict: While there is a comment present, additional comments can improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has successfully concatenated the 'control_df' and 'test_df' DataFrames as per the task requirements.
  • Area of Improvement: Consider providing more detailed comments to showcase a deeper understanding of the data merging process.
  • Final Verdict: The user has a strong grasp of the task requirements and has implemented the solution effectively.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently concatenates the 'control_df' and 'test_df' DataFrames using pd.concat() with the 'ignore_index=True' parameter.
  • Area of Improvement: Ensure that the code is optimized for larger datasets to maintain performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in concatenating the DataFrames.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python to concatenate the DataFrames and implement the task requirements.
  • Area of Improvement: Consider exploring more advanced Python functionalities for data manipulation and analysis.
  • Final Verdict: The user demonstrates proficiency in Python programming for data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's implementation aligns well with the responsibilities of a Data Analyst, involving data merging and analysis.
  • Area of Improvement: To excel as a Data Analyst, the user can focus on enhancing data visualization and interpretation skills.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst through effective data manipulation and analysis.
Data Analysis
  • Rating: 8
  • Positive Feedback: The user effectively analyzed and merged the datasets for comparison, showcasing strong data analysis skills.
  • Area of Improvement: To further enhance data analysis skills, the user can explore more complex data manipulation techniques.
  • Final Verdict: The user demonstrates proficiency in data analysis for the given task.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: Ensure consistent naming conventions for variables and columns to maintain code uniformity.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for maintaining consistency in naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The solution correctly calculates the required metrics.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations for each metric. This can enhance the readability and understanding of the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more explanatory comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of each calculation. The comments are clear and relevant.
  • Area of Improvement: Consider adding more detailed comments to explain the formulae used for each metric calculation. This can provide better insights into the code logic.
  • Final Verdict: While the code includes comments, there is room for improvement by adding more detailed explanations for better understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly implemented the calculations for CTR, Conversion Rate, CPC, and ROI as per the task requirements.
  • Area of Improvement: Further enhancements can be made by providing error handling for edge cases and validating the input data to prevent unexpected errors.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with minor suggestions for error handling and data validation.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the required metrics without unnecessary complexities. The calculations are straightforward and directly implemented.
  • Area of Improvement: Ensure error handling for division by zero scenarios to enhance the robustness of the code.
  • Final Verdict: The code demonstrates good performance efficiency with minor room for enhancement in error handling.
Role And Skill Based Rating
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data analysis tasks, showcasing proficiency in handling datasets and calculating key metrics. The implementation aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: To further excel as a Data Analyst, the user can focus on enhancing error handling techniques and exploring advanced data analysis libraries in Python.
  • Final Verdict: The user's performance reflects strong skills in data analysis, with potential for growth in advanced data manipulation techniques.

Task Description

A Metric Showdown between Control and Test Campaigns

  1. Create a list 'metrics_to_test' containing the metrics: 'ROI','CTR', 'Conversion Rate', 'CPC'.

  2. Initialize an empty list 'results_list' to store the results of the t-tests.

  3. Iterate through each metric in 'metrics_to_test':

    • Apply the independent two-sample t-test using 'ttest_ind' on the specified metric for the 'Control Campaign' and 'Test Campaign'.
    • Store the t-statistic in 't_stat' and the p-value in 'p_value'.
    • Create a DataFrame 'metric_results' with columns 'Metric', 'T-Statistic', and 'P-Value'.
    • Append 'metric_results' to 'results_list'.

  4. Concatenate the DataFrames in 'results_list' into a final DataFrame 't_test_results' using pd.concat().

  5. The DataFrame 't_test_results' now contains the results of the independent two-sample t-tests for the specified metrics between the 'Control Campaign' and 'Test Campaign'.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Enhancing Dataset with CTR, Conversion Rate, CPC, and ROI

  1. Calculate the Click-Through Rate (CTR):

    • Create a new column 'CTR' in the DataFrame 'merged'.
    • Use the formula: (Number of Clicks / Impressions) * 100.

  2. Calculate the Conversion Rate:

    • Create a new column 'Conversion Rate' in the DataFrame 'merged'.
    • Use the formula: (Purchase Number / Number of Clicks) * 100.

  3. Calculate the Cost Per Click (CPC):

    • Create a new column 'CPC' in the DataFrame 'merged'.
    • Use the formula: Amount Spent / Number of Clicks.

  4. Calculate the Return on Investment (ROI):

    • Create a new column 'ROI' in the DataFrame 'merged'.
    • Use the formula: ((Purchase Number - Amount Spent) / Amount Spent) * 100.

  5. The DataFrame 'merged' now includes the calculated metrics for Click-Through Rate (CTR), Conversion Rate, Cost Per Click (CPC), and Return on Investment (ROI).

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Merging Datasets for In-Depth Analysis

  1. Use the pd.concat() function to concatenate the DataFrames 'control_df' and 'test_df'.
    • Pass the DataFrames as a list: [control_df, test_df].
    • Set the 'ignore_index' parameter to 'True' for resetting index.
  2. The variable 'merged' now contains the concatenated DataFrame with the data from both 'control_df' and 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Comparing Cost per Conversion in Control and Test Campaigns

  1. Create a new column 'Cost per Conversion' in both 'control_df' and 'test_df'.

    • Use the formula: 'Amount Spent' / 'Purchase Number'.

  2. Calculate the mean of the 'Cost per Conversion' column for 'control_df' and 'test_df'.

    • Store the result in 'average_cost_control' for 'control_df'.
    • Store the result in 'average_cost_test' for 'test_df'.

  3. The variables 'average_cost_control' and 'average_cost_test' now contain the average cost per conversion for the 'Control' and 'Test' groups, respectively.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Comparing Purchase Numbers with a T-Test

  1. Import the 'ttest_ind' function from 'scipy.stats'.

  2. Apply the independent two-sample t-test using 'ttest_ind' on the 'Purchase Number' columns of 'control_df' and 'test_df'.

    • Store the t-statistic in 't_stat'.
    • Store the p-value in 'p_value'.

  3. The variables 't_stat' and 'p_value' now contain the results of the independent two-sample t-test for the 'Purchase Number' between the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Understanding Purchase Numbers Distribution

  1. Import the 'shapiro' function from 'scipy.stats'.

  2. Create an empty DataFrame 'shapiro_results' with columns ['Group', 'Test Statistic', 'P-value'].

  3. Apply the Shapiro-Wilk test on the 'Purchase Number' column of the 'control_df' DataFrame.

    • Store the test statistic in 'test_stat_control' and the p-value in 'p_value_control'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Control'.

  4. Repeat the same process for the 'Purchase Number' column of the 'test_df' DataFrame.

    • Store the test statistic in 'test_stat_test' and the p-value in 'p_value_test'.
    • Concatenate the results to 'shapiro_results' with the group name as 'Test'.

  5. The 'shapiro_results' DataFrame will contain the results of the Shapiro-Wilk test for normality on the 'Purchase Number' column for both the 'Control' and 'Test' groups.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Understanding Test Campaign Numbers

  • Use the .describe() method on the DataFrame 'test_df'.
  • The variable 'test_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Uncovering Insights from Control Campaign Numbers

  • Use the .describe() method on the DataFrame 'control_df'.
  • The variable 'control_describe' will store the descriptive statistics, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum, for each numerical column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Checking for Missing Values in Test Campaign Data

  • Use the .isnull() method on the DataFrame 'test_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_testdf' will now store the count of null values for each column in the DataFrame 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Handling Missing Values in Control Campaign Data

  1. Use the.fillna() method on the 'Reach' column of the DataFrame 'control_df'.

    • Fill null values with the mean of the 'Reach' column.
    • Set 'inplace=True' to modify the DataFrame in place.

  2. Repeat the same process for the following columns:

    • 'Impressions'
    • 'Number of Clicks'
    • 'Number of Searches'
    • 'Number of views'
    • 'Number Added to cart'
    • 'Purchase Number'

  3. After executing these operations, the specified columns in the DataFrame 'control_df' will have null values filled with their respective means.

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Checking for Missing Values in Control Campaign Data

  • Use the .isnull() method on the DataFrame 'control_df' to create a DataFrame of Boolean values, where True indicates a null value and False indicates a non-null value.
  • Apply the .sum() method on the resulting DataFrame to calculate the sum of null values for each column.
  • The variable 'null_sum_controldf' will now store the count of null values for each column in the DataFrame 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Friendly Names for the Test Campaign Data

  • Update the column names in the DataFrame 'test_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Simplifying Column Names for Better Understanding

  • Update the column names in the DataFrame 'control_df' to the following list:

    • 'Campaign Name', 'Date', 'Amount Spent', 'Impressions', 'Reach', 'Number of Clicks', 'Number of Searches', 'Number of views', 'Number Added to cart', 'Purchase Number'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Load the Test Campaign data.

  • Read the CSV file test_group.csv into a Pandas DataFrame named 'test_df'.
  • To import the 'test_group.csv' file, which is located in the root path of your project, you should use the following path: './test_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'test_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.

Task Description

Load the Control Campaign data.

  • Import Pandas and alias it as 'pd'.
  • Read the CSV file control_group.csv into a Pandas DataFrame named 'control_df'.
  • To import the 'control_group.csv' file, which is located in the root path of your project, you should use the following path: './control_group.csv'.
  • Use separator ';'
  • Inspect the data by calling the variable 'control_df'.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code adheres to good coding standards with proper indentation, spacing, and structure. There are no syntax errors present.
  • Area of Improvement: Consistently applying a specific commenting style and ensuring uniformity in commenting practices can further enhance code syntax.
  • Final Verdict: The code syntax is excellent, maintaining a high level of readability and adherence to coding standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. The variable names are descriptive and relevant to their purpose. The solution provides the expected output for the given task.
  • Area of Improvement: Consider adding more comments to explain the logic behind the t-tests and data manipulation steps. Enhancing the clarity of the comments can further improve code readability.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the overall process of conducting t-tests on different metrics.
  • Area of Improvement: Adding more detailed comments within the loop to clarify each step of the t-test calculation can improve the quality of comments.
  • Final Verdict: While there are comments present, enhancing the depth and clarity of comments can make the code more comprehensible.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by correctly implementing the t-tests for different metrics between the Control and Test Campaigns.
  • Area of Improvement: To further improve, the user could explore additional statistical tests or analyses to provide more comprehensive insights into the campaign data.
  • Final Verdict: The user has shown a high level of task understanding with minor areas for enhancement in exploring further analyses.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently calculates the t-statistic and p-value for each metric using the ttest_ind function. It handles the data manipulation effectively.
  • Area of Improvement: Optimizing the data retrieval process by potentially reducing redundant operations can enhance performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with opportunities for optimization in data handling.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python to conduct statistical tests and manipulate data for the given task. The code demonstrates proficiency in Python programming.
  • Area of Improvement: Exploring advanced statistical libraries or techniques in Python can further enhance the user's data analysis capabilities.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in advanced statistical analysis.
A/B Testing
  • Rating: 8
  • Positive Feedback: The user has successfully implemented A/B testing methodology to compare metrics between Control and Test Campaigns. The t-tests provide valuable insights into campaign performance.
  • Area of Improvement: Considering different variations of A/B testing or incorporating additional statistical tests can broaden the user's understanding of experimentation methods.
  • Final Verdict: The user demonstrates proficiency in A/B Testing with opportunities for exploring diverse testing approaches.
Data Analysis
  • Rating: 9
  • Positive Feedback: The user showcases strong data analysis skills by calculating key metrics like ROI, CTR, Conversion Rate, and CPC. The t-tests further enhance the analytical insights.
  • Area of Improvement: Diving deeper into data visualization techniques or predictive modeling can expand the user's data analysis capabilities.
  • Final Verdict: The user excels in Data Analysis with potential for growth in advanced analytical techniques.