Merge not giving the desired result when replacing values in a dataframe based on a match in another dataframe

I’m trying to replace values in a dataframe based on values in a different dataframe. Below you will find a simple data sample:

df1

    old_index       Date
0   3               2019-10-23
1   19              2019-12-05
2   29              NaT
3   32              NaT
4   34              2019-12-05
5   116             NaT
6   118             2020-01-29

df2

    old_index   Date
4   3           2019-10-24
12  19          2019-12-10
15  29          2019-11-12
22  32          2021-11-19
29  34          2019-12-10
40  116         2020-03-24
45  118         2020-01-30

The resulting df should be

    old_index       Date
0   3               2019-10-23
1   19              2019-12-05
2   29              2019-11-12
3   32              2021-11-19
4   34              2019-12-05
5   116             2020-03-24
6   118             2020-01-29

I tried to use merge as shown below:

mrg = pd.merge(df1, df2, how="right", on="old_index")

But that resulted in the wrong df

    old_index   Date_x      Date_y
0   3           2019-10-23  2019-10-24
1   19          2019-12-05  2019-12-10
2   29          NaT         2019-11-12
3   32          NaT         2021-11-19
4   34          2019-12-05  2019-12-10
5   116         NaT         2020-03-24
6   118         2020-01-29  2020-01-30

Any help is appreciated.

>Solution :

You can create a mapping series then fill missing values:

df1['Date'] = df1['Date'].fillna(df1['old_index'].map(df2.set_index('old_index')['Date']))
print(df1)

# Output
   old_index       Date
0          3 2019-10-23
1         19 2019-12-05
2         29 2019-11-12
3         32 2021-11-19
4         34 2019-12-05
5        116 2020-03-24
6        118 2020-01-29

A mapping Series acts as a dictionary:

>>> df2.set_index('old_index')['Date']
old_index
3     2019-10-24
19    2019-12-10
29    2019-11-12
32    2021-11-19
34    2019-12-10
116   2020-03-24
118   2020-01-30
Name: Date, dtype: datetime64[ns]