# pandas groupby sort within groups

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
```

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(level=0, 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.order(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
```

From: stackoverflow.com/q/27842613

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