大 pandas 通过非Nan值之前和之后的数值来填充nans [英] pandas filling nans by mean of before and after non-nan values

问题描述

我想用相邻元素的平均值填充df的nan.

I would like to fill df's nan with an average of adjacent elements.

考虑一个数据框:

df = pd.DataFrame({'val': [1,np.nan, 4, 5, np.nan, 10, 1,2,5, np.nan, np.nan, 9]})
    val
0   1.0
1   NaN
2   4.0
3   5.0
4   NaN
5   10.0
6   1.0
7   2.0
8   5.0
9   NaN
10  NaN
11  9.0

我想要的输出是:

    val
0   1.0
1   2.5
2   4.0
3   5.0
4   7.5
5   10.0
6   1.0
7   2.0
8   5.0
9   7.0 <<< deadend
10  7.0 <<< deadend
11  9.0

我研究了其他解决方案，例如

I've looked into other solutions such as Fill cell containing NaN with average of value before and after, but this won't work in case of two or more consecutive np.nans.

任何帮助将不胜感激！

推荐答案

使用 bfill 并除以2:

Use ffill + bfill and divide by 2:

df = (df.ffill()+df.bfill())/2

print(df)
     val
0    1.0
1    2.5
2    4.0
3    5.0
4    7.5
5   10.0
6    1.0
7    2.0
8    5.0
9    7.0
10   7.0
11   9.0

如果第一个元素和最后一个元素包含NaN，则使用(

EDIT : If 1st and last element contains NaN then use (Dark suggestion):

df = pd.DataFrame({'val':[np.nan,1,np.nan, 4, 5, np.nan, 
                          10, 1,2,5, np.nan, np.nan, 9,np.nan,]})
df = (df.ffill()+df.bfill())/2
df = df.bfill().ffill()

print(df)
     val
0    1.0
1    1.0
2    2.5
3    4.0
4    5.0
5    7.5
6   10.0
7    1.0
8    2.0
9    5.0
10   7.0
11   7.0
12   9.0
13   9.0

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