How to access pandas groupby dataframe by key

Question

You can use the get_group method:

In [21]: gb.get_group('foo')
Out[21]: 
     A         B   C
0  foo  1.624345   5
2  foo -0.528172  11
4  foo  0.865408  14

Note: This doesn't require creating an intermediary dictionary / copy of every subdataframe for every group, so will be much more memory-efficient than creating the naive dictionary with dict(iter(gb)). This is because it uses data-structures already available in the groupby object.

---

You can select different columns using the groupby slicing:

In [22]: gb[["A", "B"]].get_group("foo")
Out[22]:
     A         B
0  foo  1.624345
2  foo -0.528172
4  foo  0.865408

In [23]: gb["C"].get_group("foo")
Out[23]:
0     5
2    11
4    14
Name: C, dtype: int64

Wes McKinney (pandas' author) in Python for Data Analysis provides the following recipe:

groups = dict(list(gb))

which returns a dictionary whose keys are your group labels and whose values are DataFrames, i.e.

groups['foo']

will yield what you are looking for:

     A         B   C
0  foo  1.624345   5
2  foo -0.528172  11
4  foo  0.865408  14

Rather than

gb.get_group('foo')

I prefer using gb.groups

df.loc[gb.groups['foo']]

Because in this way you can choose multiple columns as well. for example:

df.loc[gb.groups['foo'],('A','B')]

gb = df.groupby(['A'])

gb_groups = grouped_df.groups

If you are looking for selective groupby objects then, do: gb_groups.keys(), and input desired key into the following key_list..

gb_groups.keys()

key_list = [key1, key2, key3 and so on...]

for key, values in gb_groups.iteritems():
    if key in key_list:
        print df.ix[values], "\n"

I was looking for a way to sample a few members of the GroupBy obj - had to address the posted question to get this done.

create groupby object based on some_key column

grouped = df.groupby('some_key')

pick N dataframes and grab their indices

sampled_df_i  = random.sample(grouped.indices, N)

grab the groups

df_list  = map(lambda df_i: grouped.get_group(df_i), sampled_df_i)

optionally - turn it all back into a single dataframe object

sampled_df = pd.concat(df_list, axis=0, join='outer')