如何一步一步为所有组重置DataFrame的索引? [英] How to reset a DataFrame's indexes for all groups in one step?

问题描述

我试图将数据框分成几组

I've tried to split my dataframe to groups

df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
                       'foo', 'bar', 'foo', 'foo'],
                   'B' : ['1', '2', '3', '4',
                       '5', '6', '7', '8'],
                   })

grouped = df.groupby('A')

我有2个小组

     A  B
0  foo  1
2  foo  3
4  foo  5
6  foo  7
7  foo  8

     A  B
1  bar  2
3  bar  4
5  bar  6

现在我想分别重置每个组的索引

Now I want to reset indexes for each group separately

print grouped.get_group('foo').reset_index()
print grouped.get_group('bar').reset_index()

最后我得到了结果

     A  B
0  foo  1
1  foo  3
2  foo  5
3  foo  7
4  foo  8

     A  B
0  bar  2
1  bar  4
2  bar  6

是否有更好的方法来做到这一点? (例如:自动为每个组调用某些方法)

Is there better way how to do this? (For example: automatically call some method for each group)

推荐答案

将as_index=False传递给groupby，则无需reset_index再次将groupby-d列设置为列:

Pass in as_index=False to the groupby, then you don't need to reset_index to make the groupby-d columns columns again:

In [11]: grouped = df.groupby('A', as_index=False)

In [12]: grouped.get_group('foo')
Out[12]:
     A  B
0  foo  1
2  foo  3
4  foo  5
6  foo  7
7  foo  8

注意:正如指出的(并在上面的示例中看到的那样)，上面的索引不是 [0, 1, 2, ...]，我声称这在实践中将不再重要-如果这样做的话必须经过一些奇怪的圈-它将变得更加冗长，可读性和效率降低...

Note: As pointed out (and seen in the above example) the index above is not [0, 1, 2, ...], I claim that this will never matter in practice - if it does you're going to have to just through some strange hoops - it's going to be more verbose, less readable and less efficient...

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