BUG: .date and timezones

[jreback]

from SO

In [1]: s = Series(pd.to_datetime([1396670200, 1405733045, 1448248441],unit='s')).dt.tz_localize('UTC').dt.tz_convert('US/Eastern')

In [2]: s
Out[2]: 
0   2014-04-04 23:56:40-04:00
1   2014-07-18 21:24:05-04:00
2   2015-11-22 22:14:01-05:00
dtype: datetime64[ns, US/Eastern]

These are correct as they are in the local timezone, but we are not propogating the tz here and just returning a naive date (in local zone)

In [3]: s.dt.date
Out[3]: 
0    2014-04-04
1    2014-07-18
2    2015-11-22
dtype: object

In [4]: s.dt.date[0]
Out[4]: datetime.date(2014, 4, 4)

These are converted to UTC, then returning a naive date
These should be local & with a timezone attached

In [5]: s.apply(lambda x: x.date())
Out[5]: 
0    2014-04-05
1    2014-07-19
2    2015-11-23
dtype: object

further, test

s.apply(lambda x: x)

[rockg]

Python datetime.date is naive so I think that is the right thing. However, clearly the second one needs to be localized.

[rockg]

This I believe is the same as #11800. I have the tests ready to go once apply is changed as mentioned there.

[jreback]

@rockg did you have a PR for this?

[rockg]

I have not pushed it...will do so by tomorrow morning.

[jreback]

gr8 thanks

[rockg]

I thought this would be fixed by #11564 but it is still around. Was that your understanding?

>>> s.apply(lambda x: x.date())
Out[1]: 
0    2014-04-05
1    2014-07-19
2    2015-11-23
dtype: object

[jreback]

that was much more focused on timeseltas
this is a separate (smaller issue) I think

[rockg]

All right...I need some direction then. This is the problem spot. Basically it gets the datetime64s out and converts those into Timestamps all of which are tz-unaware. One solution is to remove the tz like tz_localize(None) and then get values from that (there doesn't seem to be tz_localize on a datetime64 series). Other thoughts?

[jreback]

oh, that's non-conformant code, maybe something like:

        if needs_i8_conversion(values.dtype):
            f = lambda v: i8_boxer(v)
        mapped = lib.map_infer(values, f, convert=convert_dtype)