I have a column in a Pandas Dataframe containing birth dates in object/string format:

0    16MAR39
1    21JAN56
2    18NOV51
3    05MAR64
4    05JUN48

I want to convert the to date formatting for processing. I have used

#Convert String to Datetime type
data['BIRTH'] = pd.to_datetime(data['BIRTH'])

but the result is ...

0   2039-03-16
1   2056-01-21
2   2051-11-18
3   2064-03-05
4   2048-06-05
Name: BIRTH, dtype: datetime64[ns]

Clearly the dates have the wrong century prefix ("20" instead of "19")

I handled this using ...

data['BIRTH'] = np.where(data['BIRTH'].dt.year > 2000, data['BIRTH'] - pd.offsets.DateOffset(years=100), data['BIRTH'])

Result

0       1939-03-16
1       1956-01-21
2       1951-11-18
3       1964-03-05
4       1948-06-05

Name: BIRTH, Length: 10302, dtype: datetime64[ns]

I am wondering:

1. if there is a way to process the data that will get it right first time?
2. If there is a better way to process the data after the incorrect conversion.

I'm an amateur coder and as far as I understand things Pandas is optimised for processing efficiency. So I wanted to use the Pandas datatime module for that reason. But is it better to consider Numpy's or Pandas' datetime module here? I know this dataset is small but I am trying to improve my skills so that when I am working on larger datasets I know what to consider.

Source data