Python Pandas - Date Functionality

Extending the Time series, Date functionalities play major role in financial data analysis. While working with Date data, we will frequently come across the following −

• Generating sequence of dates
• Convert the date series to different frequencies

Create a Range of Dates:

Using the date.range()  function by specifying the periods and the frequency, we can create the date series. By default, the frequency of range is Days.

import pandas as pd

print pd. date_range( '1/1/2011' , periods= 5 )

Its output is as follows −

DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04', '2011-01-05'],

   dtype='datetime64[ns]', freq='D')

Change the Date Frequency:

import pandas as pd

print pd. date_range( '1/1/2011' , periods= 5 , freq= 'M' )