Similar Columns Split Into Multiple Dataframes
Question:
Given that I have a pandas dataframe:
item# name value price item# name value price
1 testname testvalue 12 5 testname5 testvalue5 12
2 testname testvalue 12 7 testname7 testvalue7 21
3 testname testvalue 12 9 testname9 testvalue9 4
Where multiple dataframes are side-by-side each other how do I split them into different dataframes. In this case there would be two dataframes that can be taken.There’s also the case where this could reach 4 or 5 similar columns. Any way to parse them properly?
Answers:
IIUC, you can use:
l = [g for k,g in df.groupby(df.groupby(df.columns, axis=1).cumcount(), axis=1)]
output:
[ item# name value price
0 1 testname testvalue 12
1 2 testname testvalue 12
2 3 testname testvalue 12,
item# name value price
0 5 testname5 testvalue5 12
1 7 testname7 testvalue7 21
2 9 testname9 testvalue9 4]
If you would like to manually specify column indices for the new dataframes, you can use .iloc:
df1 = df.iloc[:, 0 : 4]
df2 = df.iloc[:, 4 : 8]
Given that I have a pandas dataframe:
item# name value price item# name value price
1 testname testvalue 12 5 testname5 testvalue5 12
2 testname testvalue 12 7 testname7 testvalue7 21
3 testname testvalue 12 9 testname9 testvalue9 4
Where multiple dataframes are side-by-side each other how do I split them into different dataframes. In this case there would be two dataframes that can be taken.There’s also the case where this could reach 4 or 5 similar columns. Any way to parse them properly?
IIUC, you can use:
l = [g for k,g in df.groupby(df.groupby(df.columns, axis=1).cumcount(), axis=1)]
output:
[ item# name value price
0 1 testname testvalue 12
1 2 testname testvalue 12
2 3 testname testvalue 12,
item# name value price
0 5 testname5 testvalue5 12
1 7 testname7 testvalue7 21
2 9 testname9 testvalue9 4]
If you would like to manually specify column indices for the new dataframes, you can use .iloc:
df1 = df.iloc[:, 0 : 4]
df2 = df.iloc[:, 4 : 8]