trying to sum rows for specific columns in pandas.
have:
df =
name age gender sales commissions
joe 25 m 100 10
jane 55 f 40 4
want:
df =
name age gender sales commissions
joe 25 m 100 10
jane 55 f 40 4
Total 140 14
I've tried this option but it's aggregating everything:
df.loc['Total'] = df.sum()
You can sum the columns of interest only:
## recreate your data
df = pd.DataFrame({'name':['joe','jane'],'age':[25,55],'sales':[100,40],'commissions':[10,4]})
df.loc['Total'] = df[['sales','commissions']].sum()
Result:
>>> df
name age sales commissions
0 joe 25.0 100.0 10.0
1 jane 55.0 40.0 4.0
Total NaN NaN 140.0 14.0
If you don't want the NaN to appear, you can replace them with an empty string: df = df.fillna('')
Result:
>>> df
name age sales commissions
0 joe 25.0 100.0 10.0
1 jane 55.0 40.0 4.0
Total 140.0 14.0