How (improve) updating multiple columns of a pandas dataframe, when the columns are datetime?

I have a DataFrame that has columns as datetime objects, and I would like to update specific values for a specific index based on a list of days.

Here is an MRE that works:

import pandas as pd
start, stop = "2023-12-01", "2023-12-31"
dates: pd.Index =  pd.date_range(start, stop, freq="D")
december = pd.DataFrame(columns = dates, index = ['ft', 'pt'])
december.loc['ft', pd.to_datetime(['2023-12-04', '2023-12-06', '2023-12-11', '2023-12-12', '2023-12-27', '2023-12-28'])] = 'X'

In this example, I update the columns for the following dates:

['2023-12-04', '2023-12-06', '2023-12-11', '2023-12-12', '2023-12-27', '2023-12-28']

But ideally I would pass a list of days like [4, 6, 11, 12, 27, 28] to the .loc call.

>Solution :

Just do that using the day attribute and isin:

december.loc['ft', december.columns.day.isin([4, 6, 11, 12, 27, 28])] = 'X'

If you also only want to target december (or a list of months):

december.loc['ft', (december.columns.day.isin([4, 6, 11, 12, 27, 28])
                   &december.columns.month.isin([12])
                   )] = 'X'

Output:

   2023-12-01 2023-12-02 2023-12-03 2023-12-04 2023-12-05 2023-12-06 2023-12-07 2023-12-08 2023-12-09 2023-12-10 2023-12-11 2023-12-12 2023-12-13 2023-12-14 2023-12-15 2023-12-16 2023-12-17 2023-12-18 2023-12-19 2023-12-20 2023-12-21 2023-12-22 2023-12-23 2023-12-24 2023-12-25 2023-12-26 2023-12-27 2023-12-28 2023-12-29 2023-12-30 2023-12-31
ft        NaN        NaN        NaN          X        NaN          X        NaN        NaN        NaN        NaN          X          X        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN          X          X        NaN        NaN        NaN
pt        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN