pandas :如何将函数应用于不同的列 [英] Pandas: How to apply a function to different columns

问题描述

假设这是我的功能:

def function(x):
    return x.str.lower()

这是我的DataFrame(df)

And this is my DataFrame (df)

   A         B     C       D 
0  1.67430   BAR  0.34380  FOO 
1  2.16323   FOO -2.04643  BAR
2  0.19911   BAR -0.45805  FOO
3  0.91864   BAR -0.00718  BAR
4  1.33683   FOO  0.53429  FOO
5  0.97684   BAR -0.77363  BAR

我想将该功能仅应用于列B和D. (将其应用于完整的DataFrame并不是答案，因为这会在数字列中产生NaN值.)

I want to apply the function to just columns B and D. (Applying it to the full DataFrame isn't the answer as that produces NaN values in the numeric columns).

这是我的基本想法:df.apply(function, axis=1)

但是我无法理解如何选择不同的列来应用该功能.我尝试了通过数字位置，名称等进行所有索引的方式.

But I cannot fathom how to select distinct columns to apply the function to. I've tried all manner of indexing by numeric position, name, etc.

我花了很多时间阅读有关此内容的信息.这不是以下任何一项的直接副本:

I've spent quite a bit of time reading around this. This isn't a direct duplicate of any of these:

如何申请对两列Pandas数据框的功能

熊猫:如何使用将函数应用于多列

熊猫:将不同的函数应用于不同的列

Python熊猫:使用"apply"将1个功能应用于多列

推荐答案

通过忽略axis参数，只需从df中选择列，我们将按列而不是按行进行操作，这将随着您拥有的数量的增加而变大.行比列多:

Just subselect the columns from the df, by neglecting the axis param we operate column-wise rather than row-wise which will be significantly as you have more rows than columns here:

df[['B','D']].apply(function)

这将针对每个列运行函数

this will run your func against each column

In [186]:
df[['B','D']].apply(function)

Out[186]:
     B    D
0  bar  foo
1  foo  bar
2  bar  foo
3  bar  bar
4  foo  foo
5  bar  bar

您还可以过滤df以仅获取字符串dtype列:

You can also filter the df to just get the string dtype columns:

In [189]:
df.select_dtypes(include=['object']).apply(function)

Out[189]:
     B    D
0  bar  foo
1  foo  bar
2  bar  foo
3  bar  bar
4  foo  foo
5  bar  bar

时间

按列与按列:

In [194]:    
%timeit df.select_dtypes(include=['object']).apply(function, axis=1)
%timeit df.select_dtypes(include=['object']).apply(function)

100 loops, best of 3: 3.42 ms per loop
100 loops, best of 3: 2.37 ms per loop

但是对于较大的dfs(逐行)，第一种方法的伸缩性会好得多

However for significantly larger dfs (row-wise) the first method will scale much better

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