Which is the fastest way to extract day, month and year from a given date?

In 0.15.0 you will be able to use the new .dt accessor to do this nice syntactically.

In [36]: df = DataFrame(date_range('20000101',periods=150000,freq='H'),columns=['Date'])In [37]: df.head(5)Out[37]:                  Date0 2000-01-01 00:00:001 2000-01-01 01:00:002 2000-01-01 02:00:003 2000-01-01 03:00:004 2000-01-01 04:00:00[5 rows x 1 columns]In [38]: %timeit f(df)10 loops, best of 3: 22 ms per loopIn [39]: def f(df):    df = df.copy()    df['Year'] = DatetimeIndex(df['Date']).year    df['Month'] = DatetimeIndex(df['Date']).month    df['Day'] = DatetimeIndex(df['Date']).day    return df   ....: In [40]: f(df).head()Out[40]:                  Date  Year  Month  Day0 2000-01-01 00:00:00  2000      1    11 2000-01-01 01:00:00  2000      1    12 2000-01-01 02:00:00  2000      1    13 2000-01-01 03:00:00  2000      1    14 2000-01-01 04:00:00  2000      1    1[5 rows x 4 columns]

From 0.15.0 on (release in end of Sept 2014), the following is now possible with the new .dt accessor:

df['Year'] = df['Date'].dt.yeardf['Month'] = df['Date'].dt.monthdf['Day'] = df['Date'].dt.day

I use below code which works very well for me

df['Year']=[d.split('-')[0] for d in df.Date]df['Month']=[d.split('-')[1] for d in df.Date]df['Day']=[d.split('-')[2] for d in df.Date]df.head(5)

This is the cleanest answer I've found.

df = df.assign(**{t:getattr(df.data.dt,t) for t in nomtimes})

In [30]: df = pd.DataFrame({'data':pd.date_range(start, end)})In [31]: df.head()Out[31]:        data0 2011-01-011 2011-01-022 2011-01-033 2011-01-044 2011-01-05nomtimes = ["year", "hour", "month", "dayofweek"] df = df.assign(**{t:getattr(df.data.dt,t) for t in nomtimes})In [33]: df.head()Out[33]:        data  dayofweek  hour  month  year0 2011-01-01          5     0      1  20111 2011-01-02          6     0      1  20112 2011-01-03          0     0      1  20113 2011-01-04          1     0      1  20114 2011-01-05          2     0      1  2011