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

Question

I read a csv file containing 150,000 lines into a pandas dataframe. This dataframe has a field, Date, with the dates in yyyy-mm-dd format. I want t

Answer 1

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)

Answer 2

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]: 
                 Date
0 2000-01-01 00:00:00
1 2000-01-01 01:00:00
2 2000-01-01 02:00:00
3 2000-01-01 03:00:00
4 2000-01-01 04:00:00

[5 rows x 1 columns]

In [38]: %timeit f(df)
10 loops, best of 3: 22 ms per loop

In [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  Day
0 2000-01-01 00:00:00  2000      1    1
1 2000-01-01 01:00:00  2000      1    1
2 2000-01-01 02:00:00  2000      1    1
3 2000-01-01 03:00:00  2000      1    1
4 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.year
df['Month'] = df['Date'].dt.month
df['Day'] = df['Date'].dt.day