Working with Dates in Pandas

Canonical import statements

import pandas as pd

Timestamp objects contain both date and time information. They are analogous to datetime objects.

pd.Timestamp('9/1/2016 10:05AM')

Timestamp('2016-09-01 10:05:00')

Period objects are time durations. They are analogous to timedelta objects.

pd.Period('1/2016')

Period('2016-01', 'M')

pd.Period('3/5/2016')

Period('2016-03-05', 'D')

Various string formats of dates can be converted to Timestamp objects.

ds = pd.DataFrame(['2 June 2013', 'Aug 29, 2014', '2015-06-26', '7/12/16'])
ds[1] = pd.to_datetime(ds[0])
ds

Pandas has built-in ways of generating datetimes at specified intervals.

dates = pd.date_range('10-01-2016', periods=9, freq='2W-SUN')
dates = pd.DataFrame(dates)
dates

The accessor methods Pandas provides work as you would expect.

dates['Year'] = dates[0].dt.year
dates['Month'] = dates[0].dt.month
dates['Day'] = dates[0].dt.day
dates['Weekday'] = dates[0].dt.weekday
dates