Aggregate sum of multiple columns by date in pandas

Question

My df looks like this

Date	Col	Col1
01/01/2022	A	500
01/01/2022	B	100
01/01/2022	C	400
02/01/2022	A	400
02/01/2022	B	150
02/01/2022	C	450

My desired output looks like

Date	Total
01/01/2022	1000
02/01/2022	1000

Please help. I wanna do it automatically (not manually-hardcoded)

I am trying this

df.groupby('Date')['Col1'].sum()

@enke I didn't get the output which I expected my output is still same as my df — Amit Kumar, Feb 22 '22 at 19:34
@BENY it gives me only two columns Date and Col1. What if I have say 10 columns ? — Amit Kumar, Feb 22 '22 at 19:41
So do you want to be able to sum across multiple columns and multiple rows for a specific date? — ShyGuyRyRyNewlyTheDataGuy, Feb 22 '22 at 20:15
I suggest you can change the titile to : Aggregate sum of multiple columns by date in pandas. — Denny Chen, Feb 24 '22 at 03:27

score 2 · Answer 1 · answered Feb 22 '22 at 20:19

If you need totals and the separate column values for a given date, follow this general format.

needed_columnms = ['List','Of','Needed','Columns']
df_sums = df.groupby('Date')[needed_columns].sum()
df_sums['Total'] = df_sums[needed_columns].sum(1)

df_sums will provide you with a column total and grand total for each of the dates within 'Date'.

score 1 · Accepted Answer · answered Feb 22 '22 at 19:53

1

Try just summing the entire group, rather than a specific column:

df.groupby('Date').sum()

answered Feb 22 '22 at 19:53

Aggregate sum of multiple columns by date in pandas

2 Answers2