I want to group my dataframe by two columns and then sort the aggregated results within the groups.
In [167]:
df
Out[167]:
count job source
0 2 sales A
1 4 sales B
2 6 sales C
3 3 sales D
4 7 sales E
5 5 market A
6 3 market B
7 2 market C
8 4 market D
9 1 market E
In [168]:
df.groupby(['job','source']).agg({'count':sum})
Out[168]:
count
job source
market A 5
B 3
C 2
D 4
E 1
sales A 2
B 4
C 6
D 3
E 7
I would now like to sort the count column in descending order within each of the groups. And then take only the top three rows. To get something like:
count
job source
market A 5
D 4
B 3
sales E 7
C 6
B 4
You could also just do it in one go, by doing the sort first and using head to take the first 3 of each group.
In[34]: df.sort_values(['job','count'],ascending=False).groupby('job').head(3)
Out[35]:
count job source
4 7 sales E
2 6 sales C
1 4 sales B
5 5 market A
8 4 market D
6 3 market B