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docs(data-wrangling-with-pandas): fix audits format

DEV-4049-remove-alcohol-terminology
eslopfer 2 years ago
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  1. 30
      subjects/ai/data-wrangling/audit/README.md

30
subjects/ai/data-wrangling/audit/README.md
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@ -1,6 +1,6 @@
#### Exercise 0: Environment and libraries
##### The exercise is validated is all questions of the exercise are validated.
##### The exercise is validated if all questions of the exercise are validated.
##### Activate the virtual environment. If you used `conda` run `conda activate your_env`.
@ -8,7 +8,7 @@
###### Does it print `Python 3.x`? x >= 8
##### Does `import jupyter`, `import numpy` and `import pandas` run without any error?
###### Does `import jupyter`, `import numpy` and `import pandas` run without any error?
---
@ -16,7 +16,7 @@
#### Exercise 1: Concatenate
##### This question is validated if the outputted DataFrame is:
###### Is the outputted DataFrame as below for question 1?
| | letter | number |
|---:|:---------|---------:|
@ -31,16 +31,16 @@
#### Exercise 2: Merge
##### The exercise is validated is all questions of the exercise are validated.
##### The exercise is validated if all questions of the exercise are validated.
##### The question 1 is validated if the output is:
###### Does the output for question 1 look as below?
| | id | Feature1_x | Feature2_x | Feature1_y | Feature2_y |
|---:|-----:|:-------------|:-------------|:-------------|:-------------|
| 0 | 1 | A | B | K | L |
| 1 | 2 | C | D | M | N |
##### The question 2 is validated if the output is:
###### Does the output for question 2 look as below?
| | id | Feature1_df1 | Feature2_df1 | Feature1_df2 | Feature2_df2 |
|---:|-----:|:---------------|:---------------|:---------------|:---------------|
@ -52,7 +52,7 @@
| 5 | 6 | nan | nan | O | P |
| 6 | 7 | nan | nan | Q | R |
| 7 | 8 | nan | nan | S | T |
Note: Check that the suffixes are set using the suffix parameters rather than manually changing the columns' name.
---
@ -61,9 +61,9 @@
#### Exercise 3: Merge MultiIndex
##### The exercice is validated is all questions of the exercice are validated.
##### The exercise is validated if all questions of the exercise are validated.
##### The question 1 is validated if the outputted DataFrame's shape is `(1305, 5)` and if `merged.head()` returns a table as below. One of the answers that returns the correct DataFrame is `market_data.merge(alternative_data, how='left', left_index=True, right_index=True)`
###### Is the outputted DataFrame's shape `(1305, 5)` and `merged.head()` returns a table as below for question 1? One of the answers that returns the correct DataFrame is `market_data.merge(alternative_data, how='left', left_index=True, right_index=True)`
| | Open | Close | Close_Adjusted | Twitter | Reddit |
| :--------------------------------------------------- | --------: | -------: | -------------: | ----------: | --------: |
@ -73,7 +73,7 @@
| (Timestamp('2021-01-01 00:00:00', freq='B'), 'AMZN') | 1.06324 | 0.841241 | -0.799481 | -0.805677 | 0.511769 |
| (Timestamp('2021-01-01 00:00:00', freq='B'), 'DAI') | -0.603453 | -2.06141 | -0.969064 | 1.49817 | 0.730055 |
##### The question 2 is validated if the numbers that are missing in the DataFrame are equal to 0 and if `filled_df.sum().sum() == merged_df.sum().sum()` gives: `True`
###### For question 2, are the numbers that are missing in the DataFrame equal to 0 and `filled_df.sum().sum() == merged_df.sum().sum()` gives: `True`?
---
@ -83,7 +83,7 @@
##### The exercise is validated is all questions of the exercise are validated and if the for loop hasn't been used. The goal is to use `groupby` and `apply`.
##### The question 1 is validated if the output is:
###### Is the output for question 1 the following?
```python
df = pd.DataFrame(range(1,11), columns=['sequence'])
@ -103,7 +103,7 @@
| 8 | 8.2 |
| 9 | 8.2 |
##### The question 2 is validated if the output is a Pandas Series or DataFrame with the first 11 rows equal to the output below. The code below give a solution.
###### Is the output for question 2 a Pandas Series or DataFrame with the first 11 rows equal to the output below? The code below gives a solution.
| | sequence |
|---:|-----------:|
@ -143,7 +143,7 @@
#### Exercise 5: Groupby Agg
##### The question is validated if the output is as below. The columns don't have to be MultiIndex. A solution could be `df.groupby('product').agg({'value':['min','max','mean']})`
###### Is the output for question 1 as below? The columns don't have to be MultiIndex. A solution could be `df.groupby('product').agg({'value':['min','max','mean']})`
| product | ('value', 'min') | ('value', 'max') | ('value', 'mean') |
| :----------- | ---------------: | ---------------: | ----------------: |
@ -157,7 +157,7 @@
#### Exercise 6: Unstack
##### The question 1 is validated if the output is similar (as the values are generated randomly, it's obvious the audit doesn't require to match the values below) to what `unstacked_df.head()`returns:
###### Is the output similar (as the values are generated randomly, it's obvious the audit doesn't require to match the values below) to what `unstacked_df.head()`returns for question 1?
| Date | ('Prediction', 'AAPL') | ('Prediction', 'AMZN') | ('Prediction', 'DAI') | ('Prediction', 'FB') | ('Prediction', 'GE') |
|:--------------------|-------------------------:|-------------------------:|------------------------:|-----------------------:|-----------------------:|
@ -165,4 +165,4 @@
| 2021-01-04 00:00:00 | -0.560953 | 0.503199 | -0.79517 | -3.23136 | 1.50271 |
| 2021-01-05 00:00:00 | 0.211489 | 1.84867 | 0.287906 | -1.81119 | 1.20321 |
##### The question 2 is validated if the answer is: `unstacked.plot(title = 'Stocks 2021')`. The title can be anything else.
###### Is the answer for question 2: `unstacked.plot(title = 'Stocks 2021')`? The title can be anything else.

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