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101 lines
4.7 KiB
101 lines
4.7 KiB
2 years ago
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##### The exercice is validated is all questions of the exercice are validated
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##### The solution of question 1 is accepted if you use `drop` with `axis=1`.`inplace=True` may be useful to avoid to affect the result to a variable. A solution that could be accepted too (even if it's not a solution I recommend is `del`.
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##### The solution of question 2 is accepted if the DataFrame returns the output below. If the type of the index is not `dtype='datetime64[ns]'` the solution is not accepted. I recommend to use `set_index` with `inplace=True` to do so.
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```python
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Input: df.head().index
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Output:
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DatetimeIndex(['2006-12-16', '2006-12-16','2006-12-16', '2006-12-16','2006-12-16'],
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dtype='datetime64[ns]', name='Date', freq=None)
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```
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##### The solution of question 3 is accepted if all the types are `float64` as below. The preferred solution is `pd.to_numeric` with `coerce=True`.
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```python
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Input: df.dtypes
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Output:
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Global_active_power float64
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Global_reactive_power float64
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Voltage float64
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Global_intensity float64
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Sub_metering_1 float64
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dtype: object
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```
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##### The solution of question 4 is accepted if you use `df.describe()`.
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##### The solution of question 5 is accepted if you used `dropna` and have the number of missing values equal to 0.You should have noticed that 25979 rows contain missing values (for a total of 129895). `df.isna().sum()` allows to check the number of missing values and `df.dropna()` with `inplace=True` allows to remove the rows with missing values.
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##### The solution of question 6 is accepted if one of the two approaches below were used:
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```python
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#solution 1
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df.loc[:,'A'] = (df['A'] + 1) * 0.06
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#solution 2
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df.loc[:,'A'] = df.loc[:,'A'].apply(lambda x: (x+1)*0.06)
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```
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You may wonder `df.loc[:,'A']` is required and if `df['A'] = ...` works too. **The answer is no**. This is important in Pandas. Depending on the version of Pandas, it may return a warning. The reason is that you are affecting a value to a **copy** of the DataFrame and not in the DataFrame.
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More details: https://stackoverflow.com/questions/20625582/how-to-deal-with-settingwithcopywarning-in-pandas
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##### The solution of question 7 is accepted as long as the output of `print(filtered_df.head().to_markdown())` is as below and if the number of rows is equal to **449667**.
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| Date | Global_active_power | Global_reactive_power |
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|:--------------------|----------------------:|------------------------:|
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| 2008-12-27 00:00:00 | 0.996 | 0.066 |
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| 2008-12-27 00:00:00 | 1.076 | 0.162 |
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| 2008-12-27 00:00:00 | 1.064 | 0.172 |
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| 2008-12-27 00:00:00 | 1.07 | 0.174 |
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| 2008-12-27 00:00:00 | 0.804 | 0.184 |
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##### The solution of question 8 is accepted if the output is
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```console
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Global_active_power 0.254
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Global_reactive_power 0.000
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Voltage 238.350
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Global_intensity 1.200
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Sub_metering_1 0.000
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Name: 2007-02-16 00:00:00, dtype: float64
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```
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##### The solution of question 9 if the output is `Timestamp('2009-02-22 00:00:00')`
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##### The solution of question 10 if the output of `print(sorted_df.tail().to_markdown())` is
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| Date | Global_active_power | Global_reactive_power | Voltage |
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|:--------------------|----------------------:|------------------------:|----------:|
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| 2008-08-28 00:00:00 | 0.076 | 0 | 234.88 |
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| 2008-08-28 00:00:00 | 0.076 | 0 | 235.18 |
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| 2008-08-28 00:00:00 | 0.076 | 0 | 235.4 |
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| 2008-08-28 00:00:00 | 0.076 | 0 | 235.64 |
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| 2008-12-08 00:00:00 | 0.076 | 0 | 236.5 |
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##### The solution of question 11 is accepted if the output is as below. The solution is based on `groupby` which creates groups based on the index `Date` and aggregates the groups using the `mean`.
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```console
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Date
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2006-12-16 3.053475
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2006-12-17 2.354486
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2006-12-18 1.530435
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2006-12-19 1.157079
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2006-12-20 1.545658
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...
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2010-12-07 0.770538
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2010-12-08 0.367846
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2010-12-09 1.119508
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2010-12-10 1.097008
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2010-12-11 1.275571
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Name: Global_active_power, Length: 1433, dtype: float64
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```
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