This should be an easy one, but somehow I couldn't find a solution that works.
I have a pandas dataframe which looks like this:
index col1 col2 col3 col4 col5
0 a c 1 2 f
1 a c 1 2 f
2 a d 1 2 f
3 b d 1 2 g
4 b e 1 2 g
5 b e 1 2 g
I want to group by col1 and col2 and get the sum() of col3 and col4. Col5 can be dropped, since the data can not be aggregated.
Here is how the output should look like. I am interested in having both col3 and col4 in the resulting dataframe. It doesn't really matter if col1 and col2 are part of the index or not.
index col1 col2 col3 col4
0 a c 2 4
1 a d 1 2
2 b d 1 2
3 b e 2 4
Here is what I tried:
df_new = df.groupby(['col1', 'col2'])["col3", "col4"].sum()
That however only returns the aggregated results of col4.
I am lost here. Every example I found only aggregates one column, where the issue obviously doesn't occur.
By using apply
df.groupby(['col1', 'col2'])["col3", "col4"].apply(lambda x : x.astype(int).sum())
Out[1257]:
col3 col4
col1 col2
a c 2 4
d 1 2
b d 1 2
e 2 4
If you want to agg
df.groupby(['col1', 'col2']).agg({'col3':'sum','col4':'sum'})
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