Branch-AI

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The exercice is validated is all questions of the exercice are validated

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.

The solution of question 1 is accepted if it contains 10000 entries and 14 columns. There many solutions based on: shape, info, describe.

The solution of question 2 is accepted if the answer is 50.34730200000025.

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

The solution of question 3 is accepted if the min is `0`and the max is `99.989999999999995`

The solution of question 4 is accepted if the answer is 1098

The solution of question 5 is accepted if the answer is 30

The solution of question 6 is accepted if the are `4932` people that made the purchase during the `AM` and `5068` people that made the purchase during `PM`. There many ways to the solution but the goal of this question was to make you use `value_counts`

The solution of question 7 is accepted if the answer is as below. There many ways to the solution but the goal of this question was to make you use `value_counts`

Interior and spatial designer    31

Lawyer                           30

Social researcher                28

Purchasing manager               27

Designer, jewellery              27

The solution of question 8 is accepted if the purchase price is 75.1

The solution of question 9 is accepted if the email adress is bondellen@williams-garza.com

The solution of question 10 is accepted if the answer is 39. The prefered solution is based on this: `df[(df['A'] == X) & (df['B'] > Y)]`

The solution of question 11 is accepted if the answer is 1033. The preferred solution is based on the usage of `apply` on a `lambda` function that slices the string that contains the expiration date.

The solution of question 12 is accepted if the answer is as below. 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.

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

2.5 KiB Raw Blame History

The exercice is validated is all questions of the exercice are validated

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.

The solution of question 1 is accepted if it contains 10000 entries and 14 columns. There many solutions based on: shape, info, describe.

The solution of question 2 is accepted if the answer is 50.34730200000025.

The solution of question 3 is accepted if the min is 0and the max is 99.989999999999995

The solution of question 4 is accepted if the answer is 1098

The solution of question 5 is accepted if the answer is 30

The solution of question 6 is accepted if the are 4932 people that made the purchase during the AM and 5068 people that made the purchase during PM. There many ways to the solution but the goal of this question was to make you use value_counts

The solution of question 7 is accepted if the answer is as below. There many ways to the solution but the goal of this question was to make you use value_counts

The solution of question 8 is accepted if the purchase price is 75.1

The solution of question 9 is accepted if the email adress is bondellen@williams-garza.com

The solution of question 10 is accepted if the answer is 39. The prefered solution is based on this: df[(df['A'] == X) & (df['B'] > Y)]

The solution of question 11 is accepted if the answer is 1033. The preferred solution is based on the usage of apply on a lambda function that slices the string that contains the expiration date.

2.5 KiB

Raw Blame History

The solution of question 3 is accepted if the min is `0`and the max is `99.989999999999995`

The solution of question 6 is accepted if the are `4932` people that made the purchase during the `AM` and `5068` people that made the purchase during `PM`. There many ways to the solution but the goal of this question was to make you use `value_counts`

The solution of question 7 is accepted if the answer is as below. There many ways to the solution but the goal of this question was to make you use `value_counts`

The solution of question 10 is accepted if the answer is 39. The prefered solution is based on this: `df[(df['A'] == X) & (df['B'] > Y)]`

The solution of question 11 is accepted if the answer is 1033. The preferred solution is based on the usage of `apply` on a `lambda` function that slices the string that contains the expiration date.