I have a pandas series as follows...
0 2039-03-16
1 2056-01-21
2 2051-11-18
3 2064-03-05
4 2048-06-05
Name: BIRTH, dtype: datetime64
It was created from string data as follows
s = data['BIRTH']
s = pd.to_datetime(s)
s
I want to convert all dates after year 2040 to 1940
I can do this for a single record as follows
s.iloc[0].replace(year=d.year-100)
but I really want to just run it over the whole series. I can't work it out. Help!??
PS - I know there's ways outside of pandas using Python's DT module but I'd like to learn how to do this within Pandas please
Using DateOffset is the obvious choice here:
df['date'] - pd.offsets.DateOffset(years=100)
0 1939-03-16
1 1956-01-21
2 1951-11-18
3 1964-03-05
4 1948-06-05
Name: date, dtype: datetime64[ns]
Assign it back:
df['date'] -= pd.offsets.DateOffset(years=100)
df
date
0 1939-03-16
1 1956-01-21
2 1951-11-18
3 1964-03-05
4 1948-06-05
We have the offsets module to deal with non-fixed frequencies, it comes in handy in situations like these.
To fix your code, you'd have wanted to apply datetime.replace rowwise using apply (not recommended):
df['date'].apply(lambda x: x.replace(year=x.year-100))
0 1939-03-16
1 1956-01-21
2 1951-11-18
3 1964-03-05
4 1948-06-05
Name: date, dtype: datetime64[ns]
Or using a list comprehension,
df.assign(date=[x.replace(year=x.year-100) for x in df['date']])
date
0 1939-03-16
1 1956-01-21
2 1951-11-18
3 1964-03-05
4 1948-06-05
Neither of these handle NaT entries very well.