Replace NaN values of pandas.DataFrame with values from list

Question

In a python script using the library pandas, I have a dataset of let's say 100 lines with a feature "X", containing 36 NaN values, and a list of size 36.

I want to replace all the 36 missing values of the column "X" by the 36 values I have in my list.

It's likely to be a dumb question, but I went through all the doc and couldn't find a way to do it.

Here's an example :

INPUT

Data:   X      Y
        1      8
        2      3
        NaN    2
        NaN    7
        1      2
        NaN    2

Filler

List: [8, 6, 3]

OUTPUT

Data:   X      Y
        1      8
        2      3
        8      2
        6      7
        1      2
        3      2

Answer 1

Start with your dataframe df

print(df)

     X  Y
0  1.0  8
1  2.0  3
2  NaN  2
3  NaN  7
4  1.0  2
5  NaN  2

Define the values you want to fill with (Note: there must be the same number of elements in your filler list as NaN values in your dataframe)

filler = [8, 6, 3]

Filter your column (that contains the NaN values) and overwrite the selected rows with your filler

df.X[df.X.isnull()] = filler

df.loc[df.X.isnull(), 'X'] = filler

which gives:

print(df)

     X  Y
0  1.0  8
1  2.0  3
2  8.0  2
3  6.0  7
4  1.0  2
5  3.0  2