问题
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.
回答1:
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'})
回答2:
Another generic solution is
df.groupby(['col1','col2']).agg({'col3':'sum','col4':'sum'}).reset_index()
This will give you the required output.
回答3:
The issue is likely that df.col3.dtype is likely not an int or a numeric datatype. Try df.col3 = df.col3.astype(int) before doing your groupby
Additionally, select your columns after the groupby to see if the columns are even being aggregated:
df_new = df.groupby(['col1', 'col2']).sum()[["col3", "col4"]]
回答4:
The above answer didn't work for me.
df_new = df.groupby(['col1', 'col2']).sum()[["col3", "col4"]]
I was grouping by single group by and sum columns.
Here is the one worked for me.
D1.groupby(['col1'])['col2'].sum() << The sum at the end not the middle.
来源:https://stackoverflow.com/questions/46431243/pandas-dataframe-groupby-how-to-get-sum-of-multiple-columns