Pandas数据帧fillna（）只有一些列到位

I am trying to fill none values in a Pandas dataframe with 0's for only some subset of columns.

我试图在Pandas数据帧中填充任何值，只有一些列的子集为0。

When I do:

当我做：

import pandas as pd
df = pd.DataFrame(data={'a':[1,2,3,None],'b':[4,5,None,6],'c':[None,None,7,8]})
print df
df.fillna(value=0, inplace=True)
print df

The output:

输出：

     a    b    c
0  1.0  4.0  NaN
1  2.0  5.0  NaN
2  3.0  NaN  7.0
3  NaN  6.0  8.0
     a    b    c
0  1.0  4.0  0.0
1  2.0  5.0  0.0
2  3.0  0.0  7.0
3  0.0  6.0  8.0

It replaces every None with 0's. What I want to do is, only replace Nones in columns a and b, but not c.

它用0代替每个None。我想要做的是，只替换a和b列中的Nones，而不是c。

What is the best way of doing this?

这样做的最佳方式是什么？

3 个解决方案

#1

You can select your desired columns and do it by assignment:

您可以选择所需的列并按分配执行：

df[['a', 'b']] = df[['a','b']].fillna(value=0)

The resulting output is as expected:

结果输出符合预期：

     a    b    c
0  1.0  4.0  NaN
1  2.0  5.0  NaN
2  3.0  0.0  7.0
3  0.0  6.0  8.0

#2

You can using dict , fillna with different value for different column

您可以使用不同列的不同值的dict，fillna

df.fillna({'a':0,'b':0})
Out[829]: 
     a    b    c
0  1.0  4.0  NaN
1  2.0  5.0  NaN
2  3.0  0.0  7.0
3  0.0  6.0  8.0

After assign it back

分配后

df=df.fillna({'a':0,'b':0})
df
Out[831]: 
     a    b    c
0  1.0  4.0  NaN
1  2.0  5.0  NaN
2  3.0  0.0  7.0
3  0.0  6.0  8.0

#3

You can avoid making a copy of the object using Wen's solution and inplace=True:

您可以使用Wen的解决方案和inplace = True来避免复制对象：

df.fillna({'a':0, 'b':0}, inplace=True)
print(df)

Which yields:

产量：

     a    b    c
0  1.0  4.0  NaN
1  2.0  5.0  NaN
2  3.0  0.0  7.0
3  0.0  6.0  8.0

#1