Creating nested dataframes with multiple dataframes - pandas

Question

I have multiple dataframes, the following are only 2 of them:
print(df1)
Date A B C
2019-10-01 00:00:00 2 3 1
2019-10-01 01:00:00 5 1 6
2019-10-01 02:00:00 8 2 4
2019-10-01 03:00:00 3 6 5
print(df2)
Date A B C
2019-10-01 00:00:00 9 4 2
2019-10-01 01:00:00 3 2 4
2019-10-01 02:00:00 6 5 2
2019-10-01 03:00:00 3 6 5
All of them have same index and columns. I want to create dataframe like this:
Date df1 df2
A 2019-10-01 00:00:00 2 9
2019-10-01 01:00:00 5 3
2019-10-01 02:00:00 8 6
2019-10-01 03:00:00 3 3
B 2019-10-01 00:00:00 3 4
2019-10-01 01:00:00 1 2
2019-10-01 02:00:00 2 5
2019-10-01 03:00:00 6 6
C 2019-10-01 00:00:00 1 2
2019-10-01 01:00:00 6 4
2019-10-01 02:00:00 4 2
2019-10-01 03:00:00 5 5
I have to apply this process to 30 dataframes(their index and columns are same), so I want to write a for loop in order to achieve this dataframe. How can I do that?

Answer 1

Reshape each DataFrame of list of DataFrames by DataFrame.set_index with DataFrame.unstack and then concat, last change columns names with lambda function:
dfs = [df1,df2]
df = (pd.concat([x.set_index('Date').unstack() for x in dfs], axis=1)
.rename(columns=lambda x: f'df{x+1}'))
print (df)
df1 df2
Date
A 2019-10-01 00:00:00 2 9
2019-10-01 01:00:00 5 3
2019-10-01 02:00:00 8 6
2019-10-01 03:00:00 3 3
B 2019-10-01 00:00:00 3 4
2019-10-01 01:00:00 1 2
2019-10-01 02:00:00 2 5
2019-10-01 03:00:00 6 6
C 2019-10-01 00:00:00 1 2
2019-10-01 01:00:00 6 4
2019-10-01 02:00:00 4 2
2019-10-01 03:00:00 5 5
If want some custom columns names in final DataFrame create list with same size like length of dfs and add parameter keys:
dfs = [df1,df2]
names = ['col1','col2']
df = pd.concat([x.set_index('Date').unstack() for x in dfs], keys=names, axis=1)
print (df)
col1 col2
Date
A 2019-10-01 00:00:00 2 9
2019-10-01 01:00:00 5 3
2019-10-01 02:00:00 8 6
2019-10-01 03:00:00 3 3
B 2019-10-01 00:00:00 3 4
2019-10-01 01:00:00 1 2
2019-10-01 02:00:00 2 5
2019-10-01 03:00:00 6 6
C 2019-10-01 00:00:00 1 2
2019-10-01 01:00:00 6 4
2019-10-01 02:00:00 4 2
2019-10-01 03:00:00 5 5