Separating A Dataframe By Date And Calculating Mathmetical Models Numpy Python

The data_list and the monthly_values array are in correlation with each other, so the data point '2019-09-01 00:00:00'= 15 , 2019-10-01 00:00:00'= 39.6... etc. The year_changes fun

Solution 1:

Try via groupby(),agg(),droplevel() and rename():

out=(data.groupby(data["Date"].dt.year)
     .agg(['mean','median','max','min'])
     .droplevel(0,1)
     .rename(columns=lambda x:'Average' if x=='mean' else x.title()))

OR

via pivot_table(),droplevel() and rename():

out=(data.pivot_table('Averages',data["Date"].dt.year,aggfunc=['mean','median','max','min'])
         .droplevel(1,1)
         .rename(columns=lambda x:'Average' if x=='mean' else x.title()))

output of out:

         Average    Median  Max     Min
Date                
2019    22.275000   24.65   39.6    0.2
2020    14.158333   14.05   26.8    2.3
2021    18.742857   5.00    93.9    -16.5