9 .dt accessor

Series has an accessor to succinctly return datetime like properties for the values of the Series, if it is a datetime/period like Series. This will return a Series, indexed like the existing Series.

# datetime
In [1]: s = pd.Series(pd.date_range('20130101 09:10:12', periods=4))

In [2]: s
Out[2]: 
0   2013-01-01 09:10:12
1   2013-01-02 09:10:12
2   2013-01-03 09:10:12
3   2013-01-04 09:10:12
dtype: datetime64[ns]

In [3]: s.dt.hour
Out[3]: 
0    9
1    9
2    9
3    9
dtype: int64

In [4]: s.dt.second
Out[4]: 
0    12
1    12
2    12
3    12
dtype: int64

In [5]: s.dt.day
Out[5]: 
0    1
1    2
2    3
3    4
dtype: int64

This enables nice expressions like this:

In [6]: s[s.dt.day==2]
Out[6]: 
1   2013-01-02 09:10:12
dtype: datetime64[ns]

You can easily produces tz aware transformations:

In [7]: stz = s.dt.tz_localize('US/Eastern')

In [8]: stz
Out[8]: 
0   2013-01-01 09:10:12-05:00
1   2013-01-02 09:10:12-05:00
2   2013-01-03 09:10:12-05:00
3   2013-01-04 09:10:12-05:00
dtype: datetime64[ns, US/Eastern]

In [9]: stz.dt.tz
Out[9]: <DstTzInfo 'US/Eastern' LMT-1 day, 19:04:00 STD>

You can also chain these types of operations:

In [10]: s.dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
Out[10]: 
0   2013-01-01 04:10:12-05:00
1   2013-01-02 04:10:12-05:00
2   2013-01-03 04:10:12-05:00
3   2013-01-04 04:10:12-05:00
dtype: datetime64[ns, US/Eastern]

You can also format datetime values as strings with Series.dt.strftime() which supports the same format as the standard strftime().

# DatetimeIndex
In [11]: s = pd.Series(pd.date_range('20130101', periods=4))

In [12]: s
Out[12]: 
0   2013-01-01
1   2013-01-02
2   2013-01-03
3   2013-01-04
dtype: datetime64[ns]

In [13]: s.dt.strftime('%Y/%m/%d')
Out[13]: 
0    2013/01/01
1    2013/01/02
2    2013/01/03
3    2013/01/04
dtype: object

# PeriodIndex
In [14]: s = pd.Series(pd.period_range('20130101', periods=4))

In [15]: s
Out[15]: 
0   2013-01-01
1   2013-01-02
2   2013-01-03
3   2013-01-04
dtype: object

In [16]: s.dt.strftime('%Y/%m/%d')
Out[16]: 
0    2013/01/01
1    2013/01/02
2    2013/01/03
3    2013/01/04
dtype: object

The .dt accessor works for period and timedelta dtypes.

# period
In [17]: s = pd.Series(pd.period_range('20130101', periods=4, freq='D'))

In [18]: s
Out[18]: 
0   2013-01-01
1   2013-01-02
2   2013-01-03
3   2013-01-04
dtype: object

In [19]: s.dt.year
Out[19]: 
0    2013
1    2013
2    2013
3    2013
dtype: int64

In [20]: s.dt.day
Out[20]: 
0    1
1    2
2    3
3    4
dtype: int64

# timedelta
In [21]: s = pd.Series(pd.timedelta_range('1 day 00:00:05', periods=4, freq='s'))

In [22]: s
Out[22]: 
0   1 days 00:00:05
1   1 days 00:00:06
2   1 days 00:00:07
3   1 days 00:00:08
dtype: timedelta64[ns]

In [23]: s.dt.days
Out[23]: 
0    1
1    1
2    1
3    1
dtype: int64

In [24]: s.dt.seconds
Out[24]: 
0    5
1    6
2    7
3    8
dtype: int64

In [25]: s.dt.components
Out[25]: 
   days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
0     1      0        0        5             0             0            0
1     1      0        0        6             0             0            0
2     1      0        0        7             0             0            0
3     1      0        0        8             0             0            0

Note

Series.dt will raise a TypeError if you access with a non-datetimelike values