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 that 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