具有Nan支持的Pandas Lambda函数 [英] Pandas Lambda Function with Nan Support

问题描述

我正在尝试在Pandas中编写一个lambda函数，以检查Col1是否为Nan，如果是，则使用另一列的数据.我在获取代码(如下)以正确地编译/执行时遇到了麻烦.

I am trying to write a lambda function in Pandas that checks to see if Col1 is a Nan and if so, uses another column's data. I have having trouble getting code (below) to compile/execute correctly.

import pandas as pd
import numpy as np
df=pd.DataFrame({ 'Col1' : [1,2,3,np.NaN], 'Col2': [7, 8, 9, 10]})  
df2=df.apply(lambda x: x['Col2'] if x['Col1'].isnull() else x['Col1'], axis=1)

有人对如何编写具有lambda函数的解决方案有什么好主意吗?还是我超出了lambda的能力?如果没有，您还有其他解决方案吗?谢谢.

Does anyone have any good idea on how to write a solution like this with a lambda function or have I exceeded the abilities of lambda? If not, do you have another solution? Thanks.

推荐答案

您需要 pandas.isnull 检查标量是否为NaN:

df = pd.DataFrame({ 'Col1' : [1,2,3,np.NaN],
                 'Col2' : [8,9,7,10]})  

df2 = df.apply(lambda x: x['Col2'] if pd.isnull(x['Col1']) else x['Col1'], axis=1)

print (df)
   Col1  Col2
0   1.0     8
1   2.0     9
2   3.0     7
3   NaN    10

print (df2)
0     1.0
1     2.0
2     3.0
3    10.0
dtype: float64

但是更好的方法是使用 Series.combine_first :

But better is use Series.combine_first:

df['Col1'] = df['Col1'].combine_first(df['Col2'])

print (df)
   Col1  Col2
0   1.0     8
1   2.0     9
2   3.0     7
3  10.0    10

使用 Series.update 的另一种解决方案:

Another solution with Series.update:

df['Col1'].update(df['Col2'])
print (df)
   Col1  Col2
0   8.0     8
1   9.0     9
2   7.0     7
3  10.0    10

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