python - 了解 Pandas 数据帧索引

Question

总结: 这不起作用:

df[df.key==1]['D'] = 1

但这确实:

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

这行不通:

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

但这确实:

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

Link to notebook

我的问题是:

Why does only the 2nd way work? I can't seem to see a difference in selection/indexing logic.

版本是0.10.0

Edit: This should not be done like this anymore. Since version 0.11, there is .loc . See here: http://pandas.pydata.org/pandas-docs/stable/indexing.html

Answer 1

Pandas 文档说:

Returning a view versus a copy

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 bool 切片(生成 Dataframe 的副本)，然后选择一列 ['D']。

在 df.D[df.key==1] = 3.4 中，您首先选择一列，然后对生成的 Series 进行 bool 切片。

这似乎有所不同，尽管我必须承认这有点违反直觉。

编辑:差异由 Dougal 识别，请参阅他的评论:对于版本 1，复制是作为 __getitem__ 方法进行的要求 bool 切片。对于版本 2，仅访问 __setitem__ 方法 - 因此不返回副本而只是分配。