How to replace all non-NaN entries of a dataframe with a Series?

Question

I know that you can use pandas.DataFrame.fillna to replace all null values with a Series, but is there an easy way to replace all non-null values with a Series?

Alternatively, I have seen df.loc[~df.isnull()] for replacing all null values with a single value, but again - is there a way to pass in a Series?

Edit: For example, say I have a DataFrame df with a column called code which contains some null values and some non-null values. Then say I have a Series called new_code which contains only non-null values. I want to replace all of the non-null values in df['code'] with the values of new_code (where the number of non-null values is equal to the length of new_code ).

Answer 1

You can do it as below.

Since you have not provided a df, I am using my own df (input & output shown). 'f' is the series that has been created.

a = df.loc[~df['Age'].isnull()]
b = df.loc[~df['Age'].isnull()].index
f= pd.Series([i for i in range(1,12)], index=b)
df.loc[~df['Age'].isnull(),['Age']]=f

Input

Country     Age
0   USA     NaN
1   EU      15.0
2   China   35.0
3   USA     45.0
4   EU      NaN
5   China   NaN
6   USA     28.0
7   EU      26.0
8   China   78.0
9   USA     65.0
10  EU      53.0
11  China   66.0
12  USA     32.0
13  EU      NaN
14  China   14.0

f

1      1
2      2
3      3
6      4
7      5
8      6
9      7
10     8
11     9
12    10
14    11

Output

Country     Age
0   USA     NaN
1   EU      1.0
2   China   2.0
3   USA     3.0
4   EU      NaN
5   China   NaN
6   USA     4.0
7   EU      5.0
8   China   6.0
9   USA     7.0
10  EU      8.0
11  China   9.0
12  USA     10.0
13  EU      NaN
14  China   11.0