Python 如何一步重置所有组的DataFrame索引？
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How to reset a DataFrame's indexes for all groups in one step?
提问by Meloun
I've tried to split my dataframe to groups
我试图将我的数据框拆分为组
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')
I get 2 groups
我有 2 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
Now I want to reset indexes for each group separately
现在我想分别为每个组重置索引
print grouped.get_group('foo').reset_index()
print grouped.get_group('bar').reset_index()
Finally I get the result
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
Is there better way how to do this? (For example: automatically call some method for each group)
有没有更好的方法来做到这一点？（例如：为每个组自动调用一些方法）
回答by Greg
Something like this would work:
像这样的事情会起作用：
for group, index in grouped.indices.iteritems():
grouped.indices[group] = range(0, len(index))
You could probably make it less verbose if you wanted to.
如果你愿意，你可以让它不那么冗长。
回答by Andy Hayden
Pass in as_index=Falseto the groupby, then you don't need to reset_indexto make the groupby-d columns columns again:
传入as_index=False到GROUPBY，那么你就需要reset_index再次进行GROUPBY-d列列：
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
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...
注意：正如所指出的（并在上面的例子中看到）上面的索引不是[0, 1, 2, ...]，我声称这在实践中永远不会重要 - 如果是这样，你将不得不通过一些奇怪的箍 - 它会更多冗长，可读性差，效率低......
回答by BAC83
Isn't this just grouped = grouped.apply(lambda x: x.reset_index())?
这不只是grouped = grouped.apply(lambda x: x.reset_index())吗？
回答by Songhua Hu
df=df.groupby('A').apply(lambda x: x.reset_index(drop=True)).drop('A',axis=1).reset_index()
回答by yogitha jaya reddy gari
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',as_index = False)
we get two groups
我们有两组
grouped_index = grouped.apply(lambda x: x.reset_index(drop = True)).reset_index()
Result in two new columns level_0 and level_1 getting added and the index is reset
导致添加两个新列 level_0 和 level_1 并重置索引
level_0level_1 A B
0 0 0 bar 2
1 0 1 bar 4
2 0 2 bar 6
3 1 0 foo 1
4 1 1 foo 3
5 1 2 foo 5
6 1 3 foo 7
7 1 4 foo 8
result = grouped_index.drop('level_0',axis = 1).set_index('level_1')
Creates an index within each group of "A"
在每组“A”中创建一个索引
A B
level_1
0 bar 2
1 bar 4
2 bar 6
0 foo 1
1 foo 3
2 foo 5
3 foo 7
4 foo 8
