["python","pandas","dataframe","group-by"]
How to access subdataframes of pandas groupby 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 key=bar A B C 1 bar -0.611756 18 3 bar -1.072969 10 5 bar -2.301539 18 key=foo 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'] Out[12]: 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) Out[13]: 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?
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