How to access pandas groupby dataframe by key


How do I access the corresponding groupby dataframe in a groupby object by the key?

With the following groupby:

rand = np.random.RandomState(1)
df = pd.DataFrame({'A': ['foo', 'bar'] * 3,
                   'B': rand.randn(6),
                   'C': rand.randint(0, 20, 6)})
gb = df.groupby(['A'])

I can iterate through it to get the keys and groups:

In [11]: for k, gp in gb:
             print 'key=' + str(k)
             print gp
     A         B   C
1  bar -0.611756  18
3  bar -1.072969  10
5  bar -2.301539  18
     A         B   C
0  foo  1.624345   5
2  foo -0.528172  11
4  foo  0.865408  14

I would like to be able to access a group by its key:

In [12]: gb['foo']
     A         B   C
0  foo  1.624345   5
2  foo -0.528172  11
4  foo  0.865408  14

But when I try doing that with gb[('foo',)] I get this weird pandas.core.groupby.DataFrameGroupBy object thing which doesn’t seem to have any methods that correspond to the DataFrame I want.

The best I could think of is:

In [13]: def gb_df_key(gb, key, orig_df):
             ix = gb.indices[key]
             return orig_df.ix[ix]

         gb_df_key(gb, 'foo', df)
     A         B   C
0  foo  1.624345   5
2  foo -0.528172  11
4  foo  0.865408  14  

but this is kind of nasty, considering how nice pandas usually is at these things.
What’s the built-in way of doing this?

Asked By: beardc



You can use the get_group method:

In [21]: gb.get_group('foo')
     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")
     A         B
0  foo  1.624345
2  foo -0.528172
4  foo  0.865408

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

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.


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
Answered By: JD Margulici

Rather than


I prefer using gb.groups


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

Answered By: LegitMe

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')
Answered By: meyerson
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..


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

for key, values in gb_groups.items():
    if key in key_list:
        print(df.ix[values], "n")
Answered By: Surya

is equivalent to:

df[df['A'] == 'foo']
Answered By: Mykola Zotko