Branch-AI/one_exercise_per_file/day02/ex02/audit/readme.md

1. del works but it is not a solution I recommend. For this exercise it is accepted. It is expected to use drop with axis=1. inplace=True may be useful to avoid to affect the result to a variable.

2. The preferred solution is set_index with inplace=True. As long as the DataFrame returns the output below, the solution is accepted. If the type of the index is not dtype='datetime64[ns]' the solution is not accepted.

       Input: df.head().index
   
       Output: 
   
       DatetimeIndex(['2006-12-16', '2006-12-16','2006-12-16', '2006-12-16','2006-12-16'],
       dtype='datetime64[ns]', name='Date', freq=None)
   

3. The preferred solution is pd.to_numeric with coerce=True. The solution is accepted if all types are float64.

       Input: df.dtypes
   
       Output: 
   
           Global_active_power      float64
           Global_reactive_power    float64
           Voltage                  float64
           Global_intensity         float64
           Sub_metering_1           float64
           dtype: object
   
   

4. df.describe() is expected

5. 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. The solution is accepted if you used dropna and have the number of missing values as 0.

6. Two solutions are accepted:

   • df.loc[:,'A'] = (df['A'] + 1) * 0.06
   • Using apply: df.loc[:,'A'] = df.loc[:,'A'].apply(lambda x: (x+1)*0.06)

   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. More details: https://stackoverflow.com/questions/20625582/how-to-deal-with-settingwithcopywarning-in-pandas

7. The solution is accepted as long as the output of print(filtered_df.head().to_markdown()) is:

   Date	Global_active_power	Global_reactive_power
   2008-12-27 00:00:00	0.996	0.066
   2008-12-27 00:00:00	1.076	0.162
   2008-12-27 00:00:00	1.064	0.172
   2008-12-27 00:00:00	1.07	0.174
   2008-12-27 00:00:00	0.804	0.184

   Check that the number of rows is equal to 449667.

8. The solution is accepted if output is

       Global_active_power        0.254
       Global_reactive_power      0.000
       Voltage                  238.350
       Global_intensity           1.200
       Sub_metering_1             0.000
       Name: 2007-02-16 00:00:00, dtype: float64
   
   

9. The solution is accepted if the output is Timestamp('2009-02-22 00:00:00')

10. The solution is accepted if the output for print(sorted_df.tail().to_markdown()) is

    Date	Global_active_power	Global_reactive_power	Voltage
    2008-08-28 00:00:00	0.076	0	234.88
    2008-08-28 00:00:00	0.076	0	235.18
    2008-08-28 00:00:00	0.076	0	235.4
    2008-08-28 00:00:00	0.076	0	235.64
    2008-12-08 00:00:00	0.076	0	236.5

11. The solution is based on groupby which creates groups based on the index Date and aggregates the groups using the mean. The solution is accepted if the output is

    Date
    2006-12-16    3.053475
    2006-12-17    2.354486
    2006-12-18    1.530435
    2006-12-19    1.157079
    2006-12-20    1.545658
                    ...   
    2010-12-07    0.770538
    2010-12-08    0.367846
    2010-12-09    1.119508
    2010-12-10    1.097008
    2010-12-11    1.275571
    Name: Global_active_power, Length: 1433, dtype: float64