pandas groupby, then sort within groups

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

What you want to do is actually again a groupby (on the result of the first groupby): sort and take the first three elements per group.

Starting from the result of the first groupby:

In [60]: df_agg = df.groupby(['job','source']).agg({'count':sum})

We group by the first level of the index:

In [63]: g = df_agg['count'].groupby('job', group_keys=False)

Then we want to sort ('order') each group and take the first three elements:

In [64]: res = g.apply(lambda x: x.sort_values(ascending=False).head(3))

However, for this, there is a shortcut function to do this, nlargest:

In [65]: g.nlargest(3)
Out[65]:
job     source
market  A         5
        D         4
        B         3
sales   E         7
        C         6
        B         4
dtype: int64

So in one go, this looks like:

df_agg['count'].groupby('job', group_keys=False).nlargest(3)

Here's other example of taking top 3 on sorted order, and sorting within the groups:

In [43]: import pandas as pd                                                                                                                                                       

In [44]:  df = pd.DataFrame({"name":["Foo", "Foo", "Baar", "Foo", "Baar", "Foo", "Baar", "Baar"], "count_1":[5,10,12,15,20,25,30,35], "count_2" :[100,150,100,25,250,300,400,500]})

In [45]: df                                                                                                                                                                        
Out[45]: 
   count_1  count_2  name
0        5      100   Foo
1       10      150   Foo
2       12      100  Baar
3       15       25   Foo
4       20      250  Baar
5       25      300   Foo
6       30      400  Baar
7       35      500  Baar


### Top 3 on sorted order:
In [46]: df.groupby(["name"])["count_1"].nlargest(3)                                                                                                                               
Out[46]: 
name   
Baar  7    35
      6    30
      4    20
Foo   5    25
      3    15
      1    10
dtype: int64


### Sorting within groups based on column "count_1":
In [48]: df.groupby(["name"]).apply(lambda x: x.sort_values(["count_1"], ascending = False)).reset_index(drop=True)
Out[48]: 
   count_1  count_2  name
0       35      500  Baar
1       30      400  Baar
2       20      250  Baar
3       12      100  Baar
4       25      300   Foo
5       15       25   Foo
6       10      150   Foo
7        5      100   Foo

If you don't need to sum a column, then use @tvashtar's answer. If you do need to sum, then you can use @joris' answer or this one which is very similar to it.

df.groupby(['job']).apply(lambda x: (x.groupby('source')
                                      .sum()
                                      .sort_values('count', ascending=False))
                                     .head(3))