How to fill a field based on other column in Python?

I need to fill a column’s fields, based on the other columns, in this way:

The first df has two columns – names and ages of the kids, but some rows are NaNs, however nothing should be done with the NaNs, just ignore them. But the ages should be filled in, based on the second df.

First df:

data = {'Name Kids':  ['Valentina', 'Mark', 'Sofia', np.nan, 'Manny', 'Alex', 'Claire', np.nan, np.nan],
        'Age Kids': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]
        }

df = pd.DataFrame (data, columns = ['Name Kids','Age Kids'])

print (df)

is this:

   Name Kids  Age Kids
0  Valentina       NaN
1       Mark       NaN
2      Sofia       NaN
3        NaN       NaN
4      Manny       NaN
5       Alex       NaN
6     Claire       NaN
7        NaN       NaN
8        NaN       NaN

So based on the second df, the ages of the children should be inserted in the first df. However, in this df are more kids then in the first one, but the kids that are in plus should not be copied.

Second df:

data1 = {'Children Names':  ['Eloise', 'Valentina', 'Brian', 'Daphne', 'Mark', 'Sofia', 'Betty', 'Manny', 'Ronnie', 'Alex', 'Claire'],
        'Children Ages': [17, 13, 11, 7, 12, 3, 16, 10, 1, 5, 14]
        }

df1 = pd.DataFrame (data1, columns = ['Children Names', 'Children Ages'])

print (df1)

is this:

          Children Names  Children Ages
0          Eloise             17
1       Valentina             13
2           Brian             11
3          Daphne              7
4            Mark             12
5           Sofia              3
6           Betty             16
7           Manny             10
8          Ronnie              1
9            Alex              5
10         Claire             14

Therefore, the final df if the first one but modified, in such way that the kids that were also found in the second df now have their matching age.

Final df is the first one but modified:

   Name Kids  Age Kids
0  Valentina      13.0
1       Mark      12.0
2      Sofia       3.0
3        NaN       NaN
4      Manny      10.0
5       Alex       5.0
6     Claire      14.0
7        NaN       NaN
8        NaN       NaN

I tried something like this, but I could not figure out how to verify for the matching names and how to copy the ages from second df to first df.

df.loc[df['Name Kids'] != np.nan,  'Age Kids'] =

How to get to the final result? I am very new to this and I would really need help if you can help me please!

Answer

You can use .map to map the ages across where the names match.

df['Age Kids'] = df['Name Kids'].map(dict(df1[['Children Names', 'Children Ages']].to_numpy())

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Answer