Timedelta parse AM/PM

I have some data that is roughly like “%H:%M:%S %P” (where %P is AM/PM). I’d like to store it as a Timedelta representing seconds since midnight.

Currently parsing that “succeeds”, ignoring the AM / PM

In [3]: import pandas as pd

In [6]: raw = ['3:25:00 AM', '3:25:00 PM', '12:30:00 AM', '12:30:00 PM']
   ...: base = pd.to_timedelta(raw)
   ...: base
   ...:
Out[6]: TimedeltaIndex(['03:25:00', '03:25:00', '12:30:00', '12:30:00'], dtype='timedelta64[ns]', freq=None)

Here’s my current workaround.

In [7]: split = pd.Series(raw).str.extract(r"(?P<base>\S+) (?P<am_pm>\w{2})", expand=True)
   ...: base = pd.to_timedelta(split['base'])
   ...: hour = base.dt.total_seconds() // (60 * 60)
   ...:
   ...: move_ahead = (split.am_pm == "PM") & (hour < 12)
   ...: move_back = (split.am_pm == "AM") & (hour == 12)
   ...:
   ...: base[move_ahead] += pd.Timedelta("12H")
   ...: base[move_back] -= pd.Timedelta("12H")
   ...: base
   ...:
Out[7]:
0   03:25:00
1   15:25:00
2   00:30:00
3   12:30:00
Name: base, dtype: timedelta64[ns]

That works, but is a bit tricky to get right (assuming I have gotten it right). I’d propose that we either

1. raise when we see am/pm in the data, and add this as cookbook recipe
2. Try to support parsing this kind of data directly

[chris-b1]

The 'today' part of this is a hack, but here’s a slightly simpler recipe

In [23]: raw = ['3:25:00 AM', '3:25:00 PM', '12:30:00 AM', '12:30:00 PM']

In [24]: pd.to_datetime(raw)
Out[24]: 
DatetimeIndex(['2017-12-15 03:25:00', '2017-12-15 15:25:00',
               '2017-12-15 00:30:00', '2017-12-15 12:30:00'],
              dtype='datetime64[ns]', freq=None)

In [25]: pd.to_datetime(raw) - pd.Timestamp('today').normalize()
Out[25]: TimedeltaIndex(['03:25:00', '15:25:00', '00:30:00', '12:30:00'], dtype='timedelta64[ns]', freq=None)

[WillAyd]

Yep!