熊猫通过非 nan 值之前和之后填充 nans

Question

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

Consider a dataframe:

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

My desired output is:

    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

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.nan s.

Any help is greatly appreciated!

Answer 1

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

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

Answer 2

Althogh in case of multiple nan 's in a row it doesn't produce the exact output you specified, other users reaching this page may actually prefer the effect of the method interpolate() :

df = df.interpolate()

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    6.3
10   7.7
11   9.0