如何用Pandas DatatFrame中的行总和替换NaN

问题描述：

我正在尝试将某些列中的NaN替换为Pandas DataFrame中行的总和.参见下面的示例数据:

I am trying to replace the NaN in certain columns with the sum of the row in a Pandas DataFrame. See below the example data:

Items|  Estimate1|  Estimate2|  Estimate3|     
Item1|  NaN      |     NaN   |            8    
Item2|  NaN      |  NaN          |  5.5|

我希望获得估算值1& 2对于项目1和2分别为8和5.5.

I am hoping to have Estimate 1 & 2 to be 8 and 5.5 for Item 1 and 2 respectively.

到目前为止，我已经尝试使用df.fillna(df.sum(), inplace=True)，但是DataFrame中没有任何更改.谁能帮助我更正我的代码或推荐正确的方法?

So far I have tried using df.fillna(df.sum(), inplace=True) but there is no change in the DataFrame. Can anyone assist me correct my code or recommend the right way to do it?

答

提供axis=1似乎无效(因为用Series填充仅适用于逐列的情况，不适用于逐行的情况).
一种解决方法是将每一行的总和广播"到与原始索引/列具有相同索引/列的数据帧.稍微修改一下示例数据框:

Providing axis=1 does not seem to work (as filling with a Series only works for the column-by-column case, not for row-by-row).
A workaround is to 'broadcast' the sum of each row to a dataframe that has the same index/columns as the original one. With a slightly modified example dataframe:

In [57]: df = pd.DataFrame([[np.nan, 3.3, 8], [np.nan, np.nan, 5.5]], index=['Item1', 'Item2'], columns=['Estimate1', 'Estimate2', 'Estimate3'])

In [58]: df
Out[58]:
       Estimate1  Estimate2  Estimate3
Item1        NaN        3.3        8.0
Item2        NaN        NaN        5.5

In [59]: fill_value = pd.DataFrame({col: df.sum(axis=1) for col in df.columns})

In [60]: fill_value
Out[60]:
       Estimate1  Estimate2  Estimate3
Item1       11.3       11.3       11.3
Item2        5.5        5.5        5.5

In [61]: df.fillna(fill_value)
Out[61]:
       Estimate1  Estimate2  Estimate3
Item1       11.3        3.3        8.0
Item2        5.5        5.5        5.5

对此存在一个开放的增强问题: https://github.com/pydata/pandas /issues/4514

There is an open enhancement issue for this: https://github.com/pydata/pandas/issues/4514

如何用Pandas DatatFrame中的行总和替换NaN

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