2

I have a following dataframe:

      A    label
0    1.0     a
1    2.0     a
2    3.0     a
3    NaN     a
4    NaN     a
5    NaN     a
6    9.0     a
7    8.0     a
8    7.0     a
9    NaN     a
10   NaN     a
11  21.0     a
12  32.0     a
13  12.0     a

I want to fill nan values in the column A as follow:

Fill null values at index 3,4 and 5: by taking average of values at index 2 and 6 i.e. (3+9)/2.

Fill null values at index 9 and 10: by taking average of values at index 8 and 11 i.e. (7+21)/2.

And similarly for all other null values in the dataframe, if occurs. I have spent a lot of time thinking about the precise solution, but couldn't find one. How can I do that?

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1 Answer 1

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3

Use forward filling of missing values, add back filling of misisng values and last divide by 2 for mean:

df['A'] = df.A.ffill().add(df.A.bfill()).div(2)
print (df)
       A label
0    1.0     a
1    2.0     a
2    3.0     a
3    6.0     a
4    6.0     a
5    6.0     a
6    9.0     a
7    8.0     a
8    7.0     a
9   14.0     a
10  14.0     a
11  21.0     a
12  32.0     a
13  12.0     a

Details:

print (df.assign(ffill = df.A.ffill(),
                 bfill = df.A.bfill(),
                 both  = df.A.ffill().add(df.A.bfill()),
                 fin = df.A.ffill().add(df.A.bfill()).div(2)))
       A label  ffill  bfill  both   fin
0    1.0     a    1.0    1.0   2.0   1.0
1    2.0     a    2.0    2.0   4.0   2.0
2    3.0     a    3.0    3.0   6.0   3.0
3    NaN     a    3.0    9.0  12.0   6.0
4    NaN     a    3.0    9.0  12.0   6.0
5    NaN     a    3.0    9.0  12.0   6.0
6    9.0     a    9.0    9.0  18.0   9.0
7    8.0     a    8.0    8.0  16.0   8.0
8    7.0     a    7.0    7.0  14.0   7.0
9    NaN     a    7.0   21.0  28.0  14.0
10   NaN     a    7.0   21.0  28.0  14.0
11  21.0     a   21.0   21.0  42.0  21.0
12  32.0     a   32.0   32.0  64.0  32.0
13  12.0     a   12.0   12.0  24.0  12.0
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