pandas handle column with different date time formats gracefully

Question

I have a column with a birthdate. Some are N.A, some 01.01.2016 but some contain 01.01.2016 01:01:01 Filtering the N.A. values works fine. But handling the different date formats seems clumsy. Is it possible to have pandas handle these gracefully and e.g. for a birthdate only interpret the date and not fail?

Probably already answered here: http://stackoverflow.com/a/32854764/6614295 — jotasi, Aug 30 '16 at 08:12

score 4 · Accepted Answer · answered Aug 30 '16 at 08:11

4

pd.to_datetime() will handle multiple formats

>>> ser = pd.Series(['NaT', '01.01.2016', '01.01.2016 01:01:01'])
>>> pd.to_datetime(ser)
0                   NaT
1   2016-01-01 00:00:00
2   2016-01-01 01:01:01
dtype: datetime64[ns]

answered Aug 30 '16 at 08:11

Asish M.

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yes, you might need to convert your N.A.s to `'NaT'` though – Asish M. Aug 30 '16 at 08:13

pandas handle column with different date time formats gracefully

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