Select date columns in python based on specific date criteria-CodePudding

This is my sample code. My database contains columns for every date of the year, going back multiple years. Each column corresponds to a specific date.

import pandas as pd
df = pd.DataFrame([[10, 5, 25, 67,25,56], 
                   [20, 10, 26, 45, 56, 34], 
                   [30, 3, 27, 34, 78, 34], 
                   [40, 9, 28, 45, 34,76]], 
                  columns=[pd.to_datetime('2022-09-14'), pd.to_datetime('2022-08-14'), pd.to_datetime('2022-07-14'), pd.to_datetime('2021-09-14'),
                              pd.to_datetime('2020-09-14'), pd.to_datetime('2019-09-14')])

Is there a way to select only those columns which fit a particular criteria based on year, month or quarter.

For example, I was hoping to get only those columns which is the same date as today (any starting date) for every year. For example, today is Sep 14, 2022 and I need columns only for Sep 14, 2021, Sep 14, 2020 and so on. Another option could be to do the same on a month or quarter basis. How can this be done in pandas?

CodePudding user response：

Yes, you can do:

# day
df.loc[:, df.columns.day == 14]

   2022-09-14  2022-08-14  2022-07-14  2021-09-14  2020-09-14  2019-09-14
0          10           5          25          67          25          56
1          20          10          26          45          56          34
2          30           3          27          34          78          34
3          40           9          28          45          34          76


# month
df.loc[:, df.columns.month == 9]

   2022-09-14  2021-09-14  2020-09-14  2019-09-14
0          10          67          25          56
1          20          45          56          34
2          30          34          78          34
3          40          45          34          76


# quarter
df.loc[:, df.columns.quarter == 3]

   2022-09-14  2022-08-14  2022-07-14  2021-09-14  2020-09-14  2019-09-14
0          10           5          25          67          25          56
1          20          10          26          45          56          34
2          30           3          27          34          78          34
3          40           9          28          45          34          76