Python, pandas dataframes. pd.fillna() for specific columns with specific values

Question

For example dataframe like this:

| 1  6   nan ... |
| 1  nan 5   ... |
|nan 2   4   ... |
|... ... ... ....

I want to use values from list [11, 12, 13 ...] to fill nan with the values from list. So, 10 for nans from first column, 12 for second etc. Is there a way to do this without using .hstack() combined with df[column].fillna(value)?

Answer 1

You can pass a Series to DataFrame.fillna; the columns of the DataFrame are aligned with the index of the Series:

import numpy as np
import pandas as pd
nan = np.nan

df = pd.DataFrame({'A':[1,1,nan],'B':[6,nan,2],'C':[nan,5,4]})
ser = pd.Series([11,12,13], index=list('ABC'))
print(df.fillna(ser))

yields

      A     B     C
0   1.0   6.0  13.0
1   1.0  12.0   5.0
2  11.0   2.0   4.0