Following in the spirit of this answer, I attempted the following to convert a DataFrame column of datetimes to a column of seconds since the epoch.
df['date'] = (df['date']+datetime.timedelta(hours=2)-datetime.datetime(1970,1,1)) df['date'].map(lambda td:td.total_seconds())
The second command causes the following error which I do not understand. Any thoughts on what might be going on here? I replaced map with apply and that didn't help matters.
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-99-7123e823f995> in <module>() ----> 1 df['date'].map(lambda td:td.total_seconds()) /Users/cpd/.virtualenvs/py27-ipython+pandas/lib/python2.7/site-packages/pandas-0.12.0_937_gb55c790-py2.7-macosx-10.8-x86_64.egg/pandas/core/series.pyc in map(self, arg, na_action) 1932 return self._constructor(new_values, index=self.index).__finalize__(self) 1933 else: -> 1934 mapped = map_f(values, arg) 1935 return self._constructor(mapped, index=self.index).__finalize__(self) 1936 /Users/cpd/.virtualenvs/py27-ipython+pandas/lib/python2.7/site-packages/pandas-0.12.0_937_gb55c790-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so in pandas.lib.map_infer (pandas/lib.c:43628)() <ipython-input-99-7123e823f995> in <lambda>(td) ----> 1 df['date'].map(lambda td:td.total_seconds()) AttributeError: 'float' object has no attribute 'total_seconds'
Update:
In 0.15.0 Timedeltas became a full-fledged dtype.
So this becomes possible (as well as the methods below)
In [45]: s = Series(pd.timedelta_range('1 day',freq='1S',periods=5)) In [46]: s.dt.components Out[46]: days hours minutes seconds milliseconds microseconds nanoseconds 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 2 1 0 0 2 0 0 0 3 1 0 0 3 0 0 0 4 1 0 0 4 0 0 0 In [47]: s.astype('timedelta64[s]') Out[47]: 0 86400 1 86401 2 86402 3 86403 4 86404 dtype: float64
Original Answer:
I see that you are on master (and 0.13 is coming out very shortly), so assuming you have numpy >= 1.7. Do this. See here for the docs (this is frequency conversion)
In [5]: df = DataFrame(dict(date = date_range('20130101',periods=10))) In [6]: df Out[6]: date 0 2013-01-01 00:00:00 1 2013-01-02 00:00:00 2 2013-01-03 00:00:00 3 2013-01-04 00:00:00 4 2013-01-05 00:00:00 5 2013-01-06 00:00:00 6 2013-01-07 00:00:00 7 2013-01-08 00:00:00 8 2013-01-09 00:00:00 9 2013-01-10 00:00:00 In [7]: df['date']+timedelta(hours=2)-datetime.datetime(1970,1,1) Out[7]: 0 15706 days, 02:00:00 1 15707 days, 02:00:00 2 15708 days, 02:00:00 3 15709 days, 02:00:00 4 15710 days, 02:00:00 5 15711 days, 02:00:00 6 15712 days, 02:00:00 7 15713 days, 02:00:00 8 15714 days, 02:00:00 9 15715 days, 02:00:00 Name: date, dtype: timedelta64[ns] In [9]: (df['date']+timedelta(hours=2)-datetime.datetime(1970,1,1)) / np.timedelta64(1,'s') Out[9]: 0 1357005600 1 1357092000 2 1357178400 3 1357264800 4 1357351200 5 1357437600 6 1357524000 7 1357610400 8 1357696800 9 1357783200 Name: date, dtype: float64
The contained values are np.timedelta64[ns] objects, they don't have the same methods as timedelta objects, so no total_seconds().
In [10]: s = (df['date']+timedelta(hours=2)-datetime.datetime(1970,1,1)) In [11]: s[0] Out[11]: numpy.timedelta64(1357005600000000000,'ns')
You can astype them to int, and you get back a ns unit.
In [12]: s[0].astype(int) Out[12]: 1357005600000000000
You can do this as well (but only on an individual unit element).
In [18]: s[0].astype('timedelta64[s]') Out[18]: numpy.timedelta64(1357005600,'s')
