I need to get sum of couple DataFrames and append each result to a target DataFrames(df_sum)

df_sum = pd.DataFrame(columns = ['Source', 'Column2_SUM', 'Column3_SUM'])

I have 4 dataframe as

import pandas as pd

data_A = {'Column1': ['2023-06-16','2023-08-24','2023-04-24'],
          'Column2': [4, 5, 6],
          'Column3': [7, 8, 9]}

data_B = {'Column1': ['2023-07-19','2023-08-24','2023-03-18'],
          'Column2': [4, 96, 6],
          'Column3': [12, 8, 9]}

data_C = {'Column1': ['2023-06-22','2023-04-20','2023-09-12'],
          'Column2': [4, 88, 6],
          'Column3': [7, 8, 12]}

data_D = {'Column1': ['2023-08-27','2023-11-24','2023-04-08'],
          'Column2': [4, 32, 6],
          'Column3': [66, 8, 80]}


df_A = pd.DataFrame(data_A)
df_B = pd.DataFrame(data_B)
df_C = pd.DataFrame(data_C)
df_D = pd.DataFrame(data_D)

now what I need is to get something like, lo be loaded up to df_sum

>Solution :

Create dictionary for specify names of DataFrames, use concat and aggregate sum, add DataFrame.add_suffix with DataFrame.rename_axis and DataFrame.reset_index for column SOURCE:

d = {'df_A':df_A, 'df_B':df_B, 'df_C':df_C, 'df_D':df_D}

df = (pd.concat(d)
        .groupby(level=0)[['Column2','Column3']].sum()
        .add_suffix('_SUM')
        .rename_axis('SOURCE')
        .reset_index())
print (df)
  SOURCE  Column2_SUM  Column3_SUM
0   df_A           15           24
1   df_B          106           29
2   df_C           98           27
3   df_D           42          154