用之前和之后的平均值填充包含NaN的单元格 [英] Fill cell containing NaN with average of value before and after

问题描述

我想用缺失值之前和之后的单元格平均值填充熊猫数据框中的缺失值.因此，如果为[1，NaN，3]，则NaN值为2，因为(1 + 3)/2.我找不到任何方法可以通过Pandas或Scikit-learn做到这一点.有什么办法吗?

I would like to fill missing values in a pandas dataframe with the average of the cells directly before and after the missing value. So if it was [1, NaN, 3], the NaN value would be 2 because (1 + 3)/2. I could not find any way to do this with Pandas or Scikit-learn. Is there any way to do this?

推荐答案

考虑此数据框

df = pd.DataFrame({'val': [1,np.nan, 4, 5, np.nan, 10]})

    val
0   1.0
1   NaN
2   4.0
3   5.0
4   NaN
5   10.0

您可以将fillna与shift()一起使用以获取所需的输出

You can use fillna along with shift() to get the desired output

df.val = df.val.fillna((df.val.shift() + df.val.shift(-1))/2)

你得到

    val
0   1.0
1   2.5
2   4.0
3   5.0
4   7.5
5   10.0

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