如何通过键访问 pandas 数据帧 [英] How to access pandas groupby dataframe by key

问题描述

如何通过密钥访问groupby对象中相应的groupby数据帧？使用以下组：

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'])

我可以迭代获取密钥和组：

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

我想要做一些像

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

但是当我这样做（实际上我必须做 gb [（'foo'，]] ），我得到这个奇怪的 pandas.core.groupby.DataFrameGroupBy 的东西似乎没有任何方法对应于我想要的DataFrame。

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

我可以想到的最好的是

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  

但是，这是一种令人讨厌的问题，考虑到这些东西通常是多么美丽。

这样做的内置方式是什么？

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

推荐答案

您可以使用 get_group 方法：

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

注意：这不需要为每个组创建每个子数据库的中间字典/副本，因此使用 dict（iter（gb））。这是因为它使用groupby对象中已经可用的数据结构。

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.

您可以选择不同的列使用groupby切片：

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

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