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6.9 KiB
6.9 KiB
Exercise 0: Environment and libraries
The exercise is validated is all questions of the exercise are validated.
Activate the virtual environment. If you used conda run conda activate your_env.
Run python --version.
Does it print Python 3.x? x >= 8
Does import jupyter, import numpy and import pandas run without any error?
Exercise 1: Concatenate
This question is validated if the outputted DataFrame is:
| | letter | number |
|---:|:---------|---------:|
| 0 | a | 1 |
| 1 | b | 2 |
| 2 | c | 1 |
| 3 | d | 2 |
Exercise 2: Merge
The exercise is validated is all questions of the exercise are validated.
The question 1 is validated if the output is:
| | 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:
| | id | Feature1_df1 | Feature2_df1 | Feature1_df2 | Feature2_df2 |
|---:|-----:|:---------------|:---------------|:---------------|:---------------|
| 0 | 1 | A | B | K | L |
| 1 | 2 | C | D | M | N |
| 2 | 3 | E | F | nan | nan |
| 3 | 4 | G | H | nan | nan |
| 4 | 5 | I | J | nan | nan |
| 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.
Exercise 3: Merge MultiIndex
The exercice is validated is all questions of the exercice 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)
| Open | Close | Close_Adjusted | |||
|---|---|---|---|---|---|
| (Timestamp('2021-01-01 00:00:00', freq='B'), 'AAPL') | 0.0991792 | -0.31603 | 0.634787 | -0.00159041 | 1.06053 |
| (Timestamp('2021-01-01 00:00:00', freq='B'), 'FB') | -0.123753 | 1.00269 | 0.713264 | 0.0142127 | -0.487028 |
| (Timestamp('2021-01-01 00:00:00', freq='B'), 'GE') | -1.37775 | -1.01504 | 1.2858 | 0.109835 | 0.04273 |
| (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
Exercise 4: Groupby Apply
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:
df = pd.DataFrame(range(1,11), columns=['sequence'])
print(winsorize(df, [0.20, 0.80]).to_markdown())
| | sequence |
|---:|-----------:|
| 0 | 2.8 |
| 1 | 2.8 |
| 2 | 3 |
| 3 | 4 |
| 4 | 5 |
| 5 | 6 |
| 6 | 7 |
| 7 | 8 |
| 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.
| | sequence |
|---:|-----------:|
| 0 | 1.45 |
| 1 | 2 |
| 2 | 3 |
| 3 | 4 |
| 4 | 5 |
| 5 | 6 |
| 6 | 7 |
| 7 | 8 |
| 8 | 9 |
| 9 | 9.55 |
| 10 | 11.45 |
def winsorize(df_series, quantiles):
"""
df: pd.DataFrame or pd.Series
quantiles: list [0.05, 0.95]
"""
min_value = np.quantile(df_series, quantiles[0])
max_value = np.quantile(df_series, quantiles[1])
return df_series.clip(lower = min_value, upper = max_value)
df.groupby("group")[['sequence']].apply(winsorize, [0.05,0.95])
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']})
| product | ('value', 'min') | ('value', 'max') | ('value', 'mean') |
|---|---|---|---|
| chair | 22.89 | 32.12 | 27.505 |
| mobile phone | 100 | 111.22 | 105.61 |
| table | 20.45 | 99.99 | 51.22 |
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:
| Date | ('Prediction', 'AAPL') | ('Prediction', 'AMZN') | ('Prediction', 'DAI') | ('Prediction', 'FB') | ('Prediction', 'GE') |
|:--------------------|-------------------------:|-------------------------:|------------------------:|-----------------------:|-----------------------:|
| 2021-01-01 00:00:00 | 0.382312 | -0.072392 | -0.551167 | -0.0585555 | 1.05955 |
| 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 |