Is there a way to get the nlargest items per group

I have the following dataset:

location  category    percent
A         5           100.0
B         3           100.0
C         2            50.0
          4            13.0
D         2            75.0
          3            59.0
          4            13.0
          5             4.0

And I'm trying to get the nlargest items of category in dataframe grouped by location. i.e. If I want the top 2 largest percentages for each group the output should be:

location  category    percent
A         5           100.0
B         3           100.0
C         2            50.0
          4            13.0
D         2            75.0
          3            59.0

It looks like in pandas this is relatively straight forward using pandas.core.groupby.SeriesGroupBy.nlargest but dask doesn't have an nlargest function for groupby. Have been playing around with apply but can't seem to get it to work properly.

df.groupby(['location'].apply(lambda x: x['percent'].nlargest(2)).compute()

But I just get the error ValueError: Wrong number of items passed 0, placement implies 8

The apply should work, but your syntax is a little off:

In [11]: df
Out[11]:
Dask DataFrame Structure:
              Unnamed: 0 location category  percent
npartitions=1
                   int64   object    int64  float64
                     ...      ...      ...      ...
Dask Name: from-delayed, 3 tasks

In [12]: df.groupby("location")["percent"].apply(lambda x: x.nlargest(2), meta=('x', 'f8')).compute()
Out[12]:
location
A         0    100.0
B         1    100.0
C         2     50.0
          3     13.0
D         4     75.0
          5     59.0
Name: x, dtype: float64

In pandas you'd have .nlargest and .rank as groupby methods which would let you do this without the apply:

In [21]: df1
Out[21]:
  location  category  percent
0        A         5    100.0
1        B         3    100.0
2        C         2     50.0
3        C         4     13.0
4        D         2     75.0
5        D         3     59.0
6        D         4     13.0
7        D         5      4.0

In [22]: df1.groupby("location")["percent"].nlargest(2)
Out[22]:
location
A         0    100.0
B         1    100.0
C         2     50.0
          3     13.0
D         4     75.0
          5     59.0
Name: percent, dtype: float64

The dask documentation notes:

Dask.dataframe covers a small but well-used portion of the pandas API.
This limitation is for two reasons:

The pandas API is huge

Some operations are genuinely hard to do in parallel (for example sort).