For example:
In [77]: pd.Series([datetime.datetime(2012, 1, 1)]).dtype
Out[77]: dtype('<M8[ns]')
works as expected.
However:
In [78]: pd.Series([datetime.date(2012, 1, 1)]).dtype
Out[78]: dtype('O')
create a Series of type object rather than datetime64.
You can get the desired effect:
In [89]: pd.Series(np.array([np.datetime64(datetime.date(2012, 1, 1))])).dtype
Out[89]: dtype('<M8[D]')
Comment From: jreback
there is nothing wrong with datetime.date, but it REALLY should be passed as a datetime.datetime. Its very hard to do this 'automatically' because there is nothing technically wrong with allowing it in (its just dtyped == object).
OTOH maybe should just do this conversion .....
and your dtype in 89 will just not work; its not a valid dtype (well in 0.11 it causes subtle issues), in 0.12 I think this will raise (but'll I'll add this as an enhancement issue)
Comment From: cancan101
Why do you think that it should be passed in as datetime.datetime rather than datetime.date? There is something to be said about maintaining the precision of the observation as being an entire day rather than a specific point in time.
Numpy theoretically supports this notion with time units: http://docs.scipy.org/doc/numpy-dev/reference/arrays.datetime.html#datetime-units
Comment From: jreback
http://pandas.pydata.org/pandas-docs/dev/gotchas.html#timestamp-limitations
Comment From: jreback
http://pandas.pydata.org/pandas-docs/dev/timeseries.html#time-span-representation
Comment From: cancan101
Here is perhaps a cleaner example. I do think that auto conversion COULD be performed:
pd.DataFrame([[date(2012,1,1)]]).dtypes
Out[14]:
0 object
dtype: object
pd.DataFrame([[datetime.combine(date(2012,1,1), time(0,0,0))]]).dtypes
Out[15]:
0 datetime64[ns]
dtype: object
Comment From: jtratner
You can put something together for 0.14 if you want
Comment From: cancan101
Sounds good. I imagine this should not be too hard. On Nov 4, 2013 9:38 PM, "Jeff Tratner" notifications@github.com wrote:
You can put something together for 0.14 if you want
— Reply to this email directly or view it on GitHubhttps://github.com/pydata/pandas/issues/4338#issuecomment-27743286 .
Comment From: jtratner
I know @jreback has had a pretty good sense of what we want to support for dates, so I'm going to defer to him on this though.
Comment From: max-sixty
Is this still active? @jtratner mentioned @jreback had a design in mind - is that the case?
I'm hitting this in our work - loading a set of datetime.date objects, and getting an object rather than datetime index:
In [75]:
series=pd.Series(range(5), index=[datetime.date.today() + datetime.timedelta(i) for i in range(5)])
series
Out[75]:
2015-10-23 0
2015-10-24 1
2015-10-25 2
2015-10-26 3
2015-10-27 4
dtype: int64
In [76]:
series.index
Out[76]:
Index([2015-10-23, 2015-10-24, 2015-10-25, 2015-10-26, 2015-10-27], dtype='object')
Replacing date with datetime:
In [81]:
series=pd.Series(range(5), index=[datetime.datetime.today() + datetime.timedelta(i) for i in range(5)])
series
Out[81]:
2015-10-23 20:39:36.077137 0
2015-10-24 20:39:36.077156 1
2015-10-25 20:39:36.077169 2
2015-10-26 20:39:36.077174 3
2015-10-27 20:39:36.077178 4
dtype: int64
In [82]:
series.index
series.index
Out[82]:
DatetimeIndex(['2015-10-23 20:39:36.077137', '2015-10-24 20:39:36.077156',
'2015-10-25 20:39:36.077169', '2015-10-26 20:39:36.077174',
'2015-10-27 20:39:36.077178'],
dtype='datetime64[ns]', freq=None)
Comment From: jreback
@maximilianr
datetime.date could be auto-converted to datetime64[ns] though that would be an API change.
handling datetime.date as an actual dtype, `datetime64[D]``. is possible, though would require a fair bit of work.
It is not on the priority list, though you are welcome to take a look.
Comment From: TomAugspurger
I don't think we should auto-convert datetime.date to datetimes. They're different things. In the future, we (or a 3rd party) may make an ExtensionArray for dates, and we would have to backtrack this change.
If the user wants datetimes now, then they have pd.to_datetime.