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')

Its output is as follows −

DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-30', '2011-05-31'],
   dtype='datetime64[ns]', freq='M')

bdate_range

bdate_range() stands for business date ranges. Unlike date_range(), it excludes Saturday and Sunday.

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')

Observe, after 3rd March, the date jumps to 6th march excluding 4th and 5th. Just check your calendar for the days.

Convenience functions like date_range and bdate_range utilize a variety of frequency aliases. The default frequency for date_range is a calendar day while the default for bdate_range is a business day.

import pandas as pd
start = pd.datetime(2011, 1, 1)
end = pd.datetime(2011, 1, 5)

print pd.date_range(start, end)

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')

Offset Aliases

A number of string aliases are given to useful common time series frequencies. We will refer to these aliases as offset aliases.