Subtract columns from two DFs based on matching condition

Question

Suppose I have the following two DFs:

DF A : First column is a date, and then there are columns that start with a year (2021, 2022...)

Date    2021.Water  2021.Gas    2022.Electricity
may-04      500       470         473
may-05      520       490         493
may-06      540       510         513

DF B : First column is a date, and then there are columns that start with a year (2021, 2022...)

Date    2021.Amount 2022.Amount
may-04       100         95
may-05       110        105
may-06       120        115

The expected result is a DF with the columns from DF A, but that have the rows divided by the values for the matching year in DF B. Such as:

Date    2021.Water  2021.Gas    2022.Electricity
may-04       5.0       4.7           5.0 
may-05       4.7       4.5           4.7 
may-06       4.5       4.3           4.5 

I am really struggling with this problem. Let me know if any clarifications are needed and will be glad to help.

Answer 1

Try this:

dfai = dfa.set_index('Date')
dfai.columns = dfai.columns.str.split('.', expand=True)

dfbi = dfb.set_index('Date').rename(columns = lambda x: x.split('.')[0])

df_out = dfai.div(dfbi, level=0).round(1)
df_out.columns = df_out.columns.map('.'.join)
df_out.reset_index()

Output:

     Date  2021.Water  2021.Gas  2022.Electricity
0  may-04         5.0       4.7               5.0
1  may-05         4.7       4.5               4.7
2  may-06         4.5       4.2               4.5

Details

First, move 'Date' into the index of both dataframes, then use string split to get years into a level in each dataframe.

Use, pd.DataFrame.div with level=0 to align operations on the top level index of each dataframe.

Flatten multiindex column header back to a single level and reset_index.