[abuckenheimer]

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 for more examples

[jonathanpoulter]

I also really like the Timestamp class, are the obvious reasons not to use it?

[djrobstep]

Having pandas as a dependency?

[jonathanpoulter]

That doesn't seem like a terrible dependency, is that the only reason?