Would because maybe I did’n sleep this night but, given:
df1 = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'], index=['rank1','rank2','rank3','rank4']}
df2 = {'Age':[28,34,29,42],'bubu':[98,33,3,30], index=['rank1','rank2','rank3','rank4']}
df_merged = = pd.concat([ df1, df2 ], axis = 1)
now, for dynamically manage further arithmetic operation on the columns from a more column dense df:
a = ['Age'] #List
b = ['bubu'] #List
the arithmetic operation:
df_merged['newCol'] = df_merged[a]-df_merged[b]
or both
df_merged.assign(newCol = df_merged[a] - df_merged[b])
I can’t subtract the 2 columns because return such error
ValueError: Expected a 1D array, got an array with shape (556, 2)
>Solution :
Not sure if I understand, but try this:
a = 'Age'
b = 'bubu'
then
df_merged['newCol'] = df_merged[a] - df_merged[b]
produces:
Name Age bubu newCol
rank1 Tom 28 98 -70
rank2 Jack 34 33 1
rank3 Steve 29 3 26
rank4 Ricky 42 30 12
is that what you were after?