Filling missing date values with the least possible date in Pandas dataframe

Question

I have a dataframe with a date column as,

df = pd.DataFrame({\'date\':[\'2014-10-01\', np.nan, \'2015-09-30\', np.nan, np.nan, \'2019-06-03\']})

Answer 1

If you want a date that plays nicely with pandas, you'll need to consider pd.Timestamp, since this is the datetime type that pandas works with.

If you don't mind your dates having a time component, use pd.Timestamp.min:

pd.Timestamp.min
# Timestamp('1677-09-21 00:12:43.145225')

pd.to_datetime(df['date'].fillna(pd.Timestamp.min))

0   2014-10-01 00:00:00.000000
1   1677-09-21 00:12:43.145225
2   2015-09-30 00:00:00.000000
3   1677-09-21 00:12:43.145225
4   1677-09-21 00:12:43.145225
5   2019-06-03 00:00:00.000000
Name: date, dtype: datetime64[ns]

If you only want the dates (without times), then the smallest date sans time component would be

pd.Timestamp.min.ceil('D')
# Timestamp('1677-09-22 00:00:00')

pd.to_datetime(df['date'].fillna(pd.Timestamp.min.ceil('D')))

0   2014-10-01
1   1677-09-22
2   2015-09-30
3   1677-09-22
4   1677-09-22
5   2019-06-03
Name: date, dtype: datetime64[ns]