Best to illustrate by example:
I would like to aggregate a DataFrame by col1 and col2, summing results on col3 and col4 and averaging results on col5
If I just wanted to sum on col3-5 I'd use df.groupby(['col1','col2']).sum()
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Would be good to have sample data and expected result?Zero– Zero2015-10-19 15:00:45 +00:00Commented Oct 19, 2015 at 15:00
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1 Answer
You can use the Groupby.agg() (or Groupby.aggregate()) method for this.
aggregate() function can accept a dictionary as argument, in which case it treats the keys as the column names and the value as the function to use for aggregating. As given in the documentation -
By passing a dict to aggregate you can apply a different aggregation to the columns of a DataFrame.
Example -
import numpy as np
result = df.groupby(['col1','col2']).agg({'col3':'sum','col4':'sum','col5':np.average})
Demo -
In [50]: df = pd.DataFrame([[1,2,3,4,5],[1,2,6,7,8],[2,3,4,5,6]],columns=list('ABCDE'))
In [51]: df
Out[51]:
A B C D E
0 1 2 3 4 5
1 1 2 6 7 8
2 2 3 4 5 6
In [52]: df.groupby(['A','B']).aggregate({'C':np.sum,'D':np.sum,'E':np.average})
Out[52]:
C E D
A B
1 2 9 6.5 11
2 3 4 6.0 5
5 Comments
eran
Thanks, is there a default type for all columns not mentioned?
Anand S Kumar
I am sorry didn't get your question.
eran
Say I want to sum over two specific columns, and average over all the rest, without specifically naming them
Anand S Kumar
I don't think you can do that, but you can use dictionary comprehension to create the dictionary , example -
{k:np.sum if k in {'col3','col4'} else k:np.average for k in df.columns if k not in {'col1','col2'} .eran
Great. Thank you very much.