I know pandas is a bit meaty for a date time library if you don't already use it but their Timestamp class is awesome. String parsing is a breeze, offsets and t

I know pandas is a bit meaty for a date time library if you don't already use it but their Timestamp class is awesome. String parsing is a breeze, offsets and timezones are easy and then there's a ton of support for time series. In [34]: pd.Timestamp('2016-08') == pd.Timestamp('2016.08') == pd.Timestamp('2016/08') == pd.Timestamp('08/2016') Out[34]: True In [38]: pd.Timestamp('2016') == pd.Timestamp(datetime.datetime(2016,1,1)) Out[38]: True In [49]: pd.Timestamp('2016') + pd.offsets.MonthOffset(months=7) == pd.Timestamp('2016-08') Out[49]: True In [52]: pd.Timestamp.now() Out[52]: Timestamp('2016-08-17 08:01:07.576323') In [53]: pd.Timestamp.now() + pd.offsets.MonthBegin(normalize=True) Out[53]: Timestamp('2016-09-01 00:00:00') see http://pandas.pydata.org/pandas-docs/stable/timeseries.html http://pandas.pydata.org/pandas-docs/stable/timeseries.html for more examples