pandas DataFrame:用列的平均值替换 nan 值 [英] pandas DataFrame: replace nan values with average of columns

问题描述

我有一个主要填充实数的 Pandas DataFrame，但其中也有一些 nan 值.

I've got a pandas DataFrame filled mostly with real numbers, but there is a few nan values in it as well.

如何将 nan 替换为它们所在列的平均值?

How can I replace the nans with averages of columns where they are?

这个问题和这个问题很相似:numpy array:用列的平均值替换 nan 值，但不幸的是，那里给出的解决方案不适用于 Pandas DataFrame.

This question is very similar to this one: numpy array: replace nan values with average of columns but, unfortunately, the solution given there doesn't work for a pandas DataFrame.

推荐答案

您可以简单地使用 DataFrame.fillna 直接填充nan:

In [27]: df 
Out[27]: 
          A         B         C
0 -0.166919  0.979728 -0.632955
1 -0.297953 -0.912674 -1.365463
2 -0.120211 -0.540679 -0.680481
3       NaN -2.027325  1.533582
4       NaN       NaN  0.461821
5 -0.788073       NaN       NaN
6 -0.916080 -0.612343       NaN
7 -0.887858  1.033826       NaN
8  1.948430  1.025011 -2.982224
9  0.019698 -0.795876 -0.046431

In [28]: df.mean()
Out[28]: 
A   -0.151121
B   -0.231291
C   -0.530307
dtype: float64

In [29]: df.fillna(df.mean())
Out[29]: 
          A         B         C
0 -0.166919  0.979728 -0.632955
1 -0.297953 -0.912674 -1.365463
2 -0.120211 -0.540679 -0.680481
3 -0.151121 -2.027325  1.533582
4 -0.151121 -0.231291  0.461821
5 -0.788073 -0.231291 -0.530307
6 -0.916080 -0.612343 -0.530307
7 -0.887858  1.033826 -0.530307
8  1.948430  1.025011 -2.982224
9  0.019698 -0.795876 -0.046431

fillna 的文档字符串表示 value 应该是标量或字典，但是，它似乎也适用于 Series.如果你想传递一个字典，你可以使用 df.mean().to_dict().

The docstring of fillna says that value should be a scalar or a dict, however, it seems to work with a Series as well. If you want to pass a dict, you could use df.mean().to_dict().

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