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Branch-AI/one_exercise_per_file/week01/day02/ex03/audit/readme.md

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To validate this exercise all answers should return the expected numerical value given in the correction AND uses Pandas. For example using NumPy to compute the mean doesn't respect the philosophy of the exercise which is to use Pandas.

  1. How many rows and columns are there?10000 entries and 14 columns

    There many solutions based on: shape, info, describe

  2. What is the average Purchase Price? 50.34730200000025

    Even if np.mean gives the solution, df['Purchase Price'].mean() is preferred

  3. What were the highest and lowest purchase prices?

    min: 0

    max: 99.989999999999995

  4. How many people have English 'en' as their Language of choice on the website? 1098

  5. How many people have the job title of "Lawyer" ? 30

  6. How many people made the purchase during the AM and how many people made the purchase during PM ?

    PM: 5068

    AM: 4932

    There many ways to the solution but the goal of this question was to make you use value_counts

  7. What are the 5 most common Job Titles?

    Interior and spatial designer 31

    Lawyer 30

    Social researcher 28

    Purchasing manager 27

    Designer, jewellery 27

    There many ways to the solution but the goal of this question was to make you use value_counts

  8. Someone made a purchase that came from Lot: "90 WT" , what was the Purchase Price for this transaction? 75.1

  9. What is the email of the person with the following Credit Card Number: 4926535242672853. bondellen@williams-garza.com

  10. How many people have American Express as their Credit Card Provider and made a purchase above $95 ? 39

    The prefered solution is based on this:

    df[(df['A'] == X) & (df['B'] > Y)]

  11. How many people have a credit card that expires in 2025? 1033

    The preferred solution is based on the usage of apply on a lambda function that slices the string that contains the expiration date.

  12. What are the top 5 most popular email providers/hosts (e.g. gmail.com, yahoo.com, etc...)

    • hotmail.com 1638
    • yahoo.com 1616
    • gmail.com 1605
    • smith.com 42
    • williams.com 37

    The preferred solution is based on the usage of apply on a lambda function that slices the string that contains the email. The lambda function uses split to split the string on @. Finally, value_counts is used to count the occurrences.