了解 pandas 数据框索引 [英] Understanding pandas dataframe indexing
问题描述
摘要: 这不起作用:
df[df.key==1]['D'] = 1
但是这样做:
df.D[df.key==1] = 1
为什么?
复制:
In [1]: import pandas as pd
In [2]: from numpy.random import randn
In [4]: df = pd.DataFrame(randn(6,3),columns=list('ABC'))
In [5]: df
Out[5]:
A B C
0 1.438161 -0.210454 -1.983704
1 -0.283780 -0.371773 0.017580
2 0.552564 -0.610548 0.257276
3 1.931332 0.649179 -1.349062
4 1.656010 -1.373263 1.333079
5 0.944862 -0.657849 1.526811
In [6]: df['D']=0.0
In [7]: df['key']=3*[1]+3*[2]
In [8]: df
Out[8]:
A B C D key
0 1.438161 -0.210454 -1.983704 0 1
1 -0.283780 -0.371773 0.017580 0 1
2 0.552564 -0.610548 0.257276 0 1
3 1.931332 0.649179 -1.349062 0 2
4 1.656010 -1.373263 1.333079 0 2
5 0.944862 -0.657849 1.526811 0 2
这不起作用:
In [9]: df[df.key==1]['D'] = 1
In [10]: df
Out[10]:
A B C D key
0 1.438161 -0.210454 -1.983704 0 1
1 -0.283780 -0.371773 0.017580 0 1
2 0.552564 -0.610548 0.257276 0 1
3 1.931332 0.649179 -1.349062 0 2
4 1.656010 -1.373263 1.333079 0 2
5 0.944862 -0.657849 1.526811 0 2
但是这样做:
In [11]: df.D[df.key==1] = 3.4
In [12]: df
Out[12]:
A B C D key
0 1.438161 -0.210454 -1.983704 3.4 1
1 -0.283780 -0.371773 0.017580 3.4 1
2 0.552564 -0.610548 0.257276 3.4 1
3 1.931332 0.649179 -1.349062 0.0 2
4 1.656010 -1.373263 1.333079 0.0 2
5 0.944862 -0.657849 1.526811 0.0 2
我的问题是:
为什么只有第二种方法起作用?我似乎看不到选择/索引逻辑的差异.
Why does only the 2nd way work? I can't seem to see a difference in selection/indexing logic.
版本为0.10.0
不应再像这样进行此操作.从版本0.11开始，存在
.loc.参见此处: http://pandas.pydata.org/pandas-docs/stable /indexing.html
推荐答案
熊猫文档说:
返回视图而不是副本
Returning a view versus a copy
关于何时返回数据视图的规则完全是 取决于NumPy.每当标签数组或布尔向量 参与索引操作，结果将是副本. 使用单标签/标量索引和切片，例如df.ix [3:6]或 df.ix [:,'A']，将返回一个视图.
The rules about when a view on the data is returned are entirely dependent on NumPy. Whenever an array of labels or a boolean vector are involved in the indexing operation, the result will be a copy. With single label / scalar indexing and slicing, e.g. df.ix[3:6] or df.ix[:, 'A'], a view will be returned.
在df[df.key==1]['D']中，您首先进行布尔切片(导致数据框的副本)，然后选择列['D'].
In df[df.key==1]['D'] you first do boolean slicing (leading to a copy of the Dataframe), then you choose a column ['D'].
在df.D[df.key==1] = 3.4中，您首先选择一列，然后对生成的系列进行布尔切片.
In df.D[df.key==1] = 3.4, you first choose a column, then do boolean slicing on the resulting Series.
这似乎有所不同，尽管我必须承认这有点违反直觉.
This seems to make the difference, although I must admit that it is a little counterintuitive.
编辑 :区别是由Dougal标识的，请参见他的评论:对于版本1，将复制__getitem__方法，以进行布尔切片.对于版本2，仅访问__setitem__方法-因此不返回副本而是仅进行分配.
Edit: The difference was identified by Dougal, see his comment: With version 1, the copy is made as the __getitem__ method is called for the boolean slicing. For version 2, only the __setitem__ method is accessed - thus not returning a copy but just assigning.
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