如何在 pandas 数据框的列中将所有NaN值替换为零 [英] How can I replace all the NaN values with Zeros in a column of a pandas dataframe

问题描述

我有一个如下数据框

      itm Date                  Amount 
67    420 2012-09-30 00:00:00   65211
68    421 2012-09-09 00:00:00   29424
69    421 2012-09-16 00:00:00   29877
70    421 2012-09-23 00:00:00   30990
71    421 2012-09-30 00:00:00   61303
72    485 2012-09-09 00:00:00   71781
73    485 2012-09-16 00:00:00     NaN
74    485 2012-09-23 00:00:00   11072
75    485 2012-09-30 00:00:00  113702
76    489 2012-09-09 00:00:00   64731
77    489 2012-09-16 00:00:00     NaN

当我尝试将一个函数应用于金额"列时，出现以下错误.

when I try to .apply a function to the Amount column I get the following error.

ValueError: cannot convert float NaN to integer

我尝试使用数学模块中的.isnan应用函数 我已经尝试过熊猫.replace属性 我尝试了熊猫0.9的.sparse数据属性 我也尝试过NaN == NaN语句在函数中. 我也看过这篇文章如何替换NA 在R数据帧中是否具有零值?，同时查看其他一些文章. 我尝试过的所有方法均无效或无法识别NaN. 任何提示或解决方案将不胜感激.

I have tried applying a function using .isnan from the Math Module I have tried the pandas .replace attribute I tried the .sparse data attribute from pandas 0.9 I have also tried if NaN == NaN statement in a function. I have also looked at this article How do I replace NA values with zeros in an R dataframe? whilst looking at some other articles. All the methods I have tried have not worked or do not recognise NaN. Any Hints or solutions would be appreciated.

推荐答案

我相信DataFrame.fillna()将为您做到这一点.

I believe DataFrame.fillna() will do this for you.

链接到数据框的文档以及系列.

示例:

In [7]: df
Out[7]: 
          0         1
0       NaN       NaN
1 -0.494375  0.570994
2       NaN       NaN
3  1.876360 -0.229738
4       NaN       NaN

In [8]: df.fillna(0)
Out[8]: 
          0         1
0  0.000000  0.000000
1 -0.494375  0.570994
2  0.000000  0.000000
3  1.876360 -0.229738
4  0.000000  0.000000

要仅在一列中填写NaN，请仅选择该列.在这种情况下，我使用inplace = True实际更改df的内容.

To fill the NaNs in only one column, select just that column. in this case I'm using inplace=True to actually change the contents of df.

In [12]: df[1].fillna(0, inplace=True)
Out[12]: 
0    0.000000
1    0.570994
2    0.000000
3   -0.229738
4    0.000000
Name: 1

In [13]: df
Out[13]: 
          0         1
0       NaN  0.000000
1 -0.494375  0.570994
2       NaN  0.000000
3  1.876360 -0.229738
4       NaN  0.000000

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