一次更改 pandas DataFrame的多个列中的某些值 [英] Changing certain values in multiple columns of a pandas DataFrame at once

问题描述

假设我具有以下DataFrame:

In [1]: df
Out[1]:
  apple banana cherry
0     0      3   good
1     1      4    bad
2     2      5   good

这可以按预期工作:

In [2]: df['apple'][df.cherry == 'bad'] = np.nan
In [3]: df
Out[3]:
  apple banana cherry
0     0      3   good
1   NaN      4    bad
2     2      5   good

但这不是:

In [2]: df[['apple', 'banana']][df.cherry == 'bad'] = np.nan
In [3]: df
Out[3]:
  apple banana cherry
0     0      3   good
1     1      4    bad
2     2      5   good

为什么?我如何在不必写出两行的情况下实现苹果"和香蕉"值的转换，如

In [2]: df['apple'][df.cherry == 'bad'] = np.nan
In [3]: df['banana'][df.cherry == 'bad'] = np.nan

解决方案

您应该使用loc并执行无需链接:

In [11]: df.loc[df.cherry == 'bad', ['apple', 'banana']] = np.nan

In [12]: df
Out[12]: 
   apple  banana cherry
0      0       3   good
1    NaN     NaN    bad
2      2       5   good

请参阅返回文档视图与副本，如果将链接分配给副本(然后扔掉)，但是如果将它分配给一个位置，那么熊猫就会聪明地意识到您想分配给原始副本.

Suppose I have the following DataFrame:

In [1]: df
Out[1]:
  apple banana cherry
0     0      3   good
1     1      4    bad
2     2      5   good

This works as expected:

In [2]: df['apple'][df.cherry == 'bad'] = np.nan
In [3]: df
Out[3]:
  apple banana cherry
0     0      3   good
1   NaN      4    bad
2     2      5   good

But this doesn't:

In [2]: df[['apple', 'banana']][df.cherry == 'bad'] = np.nan
In [3]: df
Out[3]:
  apple banana cherry
0     0      3   good
1     1      4    bad
2     2      5   good

Why? How can I achieve the conversion of both the 'apple' and 'banana' values without having to write out two lines, as in

In [2]: df['apple'][df.cherry == 'bad'] = np.nan
In [3]: df['banana'][df.cherry == 'bad'] = np.nan

解决方案

You should use loc and do this without chaining:

In [11]: df.loc[df.cherry == 'bad', ['apple', 'banana']] = np.nan

In [12]: df
Out[12]: 
   apple  banana cherry
0      0       3   good
1    NaN     NaN    bad
2      2       5   good

See the docs on returning a view vs a copy, if you chain the assignment is made to the copy (and thrown away) but if you do it in one loc then pandas cleverly realises you want to assign to the original.

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