DataFrame.date_range() to generate range of dates with start end frequence options in Pandas

Generate Datetimeindex by using the frequency option. ( List of all Frequency Aliases )

import pandas as pd 
df=pd.date_range(start='4/20/2020', end='4/27/2020')
print(df)

Default frequency is D ( Day ) so we will get all days starting from 20th April 2020 to 27th April 2020.

DatetimeIndex(['2020-04-20', '2020-04-21', '2020-04-22', '2020-04-23',
               '2020-04-24', '2020-04-25', '2020-04-26', '2020-04-27'],
              dtype='datetime64[ns]', freq='D')

Using today's date

from datetime import timedelta
import pandas as pd 
dt_now=pd.to_datetime('now') # todays date 
dt_previous=(dt_now-timedelta(days=10)).strftime("%Y-%m-%d") 
df=pd.date_range(start=dt_previous, end=dt_now) # last 10 days
print(df)

Creating a list by using tolist()

import datetime
import pandas as pd 
l1=pd.date_range(end=datetime.date.today(),periods=10).tolist()
print(l1)

Options

We used start and end options in above code. Similarly we can use other options.

periods

We can specify integer values like periods=3, this is optional.

import pandas as pd 
pd.date_range(start='4/20/2020', end='4/27/2020',periods=3)

Output

DatetimeIndex(['2020-04-20 00:00:00', '2020-04-23 12:00:00',
               '2020-04-27 00:00:00'],
              dtype='datetime64[ns]', freq=None)

freq

Default value is D. We can specify what to consider as frequence of generation of elements. Here D is for Days, M for Month, H for hours etc.
3D6H : 3 Days and 6 Hours
3H30min : 3 Hours 30 Minutes

pd.date_range(start='4/20/2020', end='4/27/2020',freq='D')

Output

DatetimeIndex(['2020-04-20', '2020-04-21', '2020-04-22', '2020-04-23',
               '2020-04-24', '2020-04-25', '2020-04-26', '2020-04-27'],
              dtype='datetime64[ns]', freq='D')

Using Hour as frequency we can specify periods. But here we will remove the end option.

import pandas as pd 
pd.date_range(start='4/20/2020', freq='H',periods=5)

Output

DatetimeIndex(['2020-04-20 00:00:00', '2020-04-20 01:00:00',
               '2020-04-20 02:00:00', '2020-04-20 03:00:00',
               '2020-04-20 04:00:00'],
              dtype='datetime64[ns]', freq='H')

pd.date_range(start='4/20/2020', freq='5h30min',periods=5)

Output

DatetimeIndex(['2020-04-20 00:00:00', '2020-04-20 05:30:00',
               '2020-04-20 11:00:00', '2020-04-20 16:30:00',
               '2020-04-20 22:00:00'],
              dtype='datetime64[ns]', freq='330T')

pd.date_range(start='4/20/2010', end='4/27/2020',freq='A-FEB') # Feb End

DatetimeIndex(['2011-02-28', '2012-02-29', '2013-02-28', '2014-02-28',
               '2015-02-28', '2016-02-29', '2017-02-28', '2018-02-28',
               '2019-02-28', '2020-02-29'],
              dtype='datetime64[ns]', freq='A-FEB')

Month start

pd.date_range(start='1/1/2010', periods=12,freq='MS') # month start

Output

DatetimeIndex(['2010-01-01', '2010-02-01', '2010-03-01', '2010-04-01',
               '2010-05-01', '2010-06-01', '2010-07-01', '2010-08-01',
               '2010-09-01', '2010-10-01', '2010-11-01', '2010-12-01'],
              dtype='datetime64[ns]', freq='MS')

Month end

pd.date_range(start='1/1/2010', periods=12,freq='M') # month end

Output

DatetimeIndex(['2010-01-31', '2010-02-28', '2010-03-31', '2010-04-30',
               '2010-05-31', '2010-06-30', '2010-07-31', '2010-08-31',
               '2010-09-30', '2010-10-31', '2010-11-30', '2010-12-31'],
              dtype='datetime64[ns]', freq='M')

Hour and Minutes

All hours of a date

df=pd.date_range(start='4/20/2022', end='4/21/2020',freq='H') # all hours

df=pd.date_range(start='4/20/2022', freq='H',periods=5) #first 5 Hour

df=pd.date_range(start='4/20/2022', freq='T',periods=5) # minutes

Using present date and time we can get next 5 hours from now.

df=pd.date_range(start=pd.to_datetime('now'), freq='H',periods=5)

Output

DatetimeIndex(['2022-06-10 13:00:26.938739', '2022-06-10 14:00:26.938739',
               '2022-06-10 15:00:26.938739', '2022-06-10 16:00:26.938739',
               '2022-06-10 17:00:26.938739'],
              dtype='datetime64[ns]', freq='H')

Using python date and time

from datetime import datetime
my_now = datetime.now()
df=pd.date_range(start=my_now, freq='T',periods=5)

tz

Time zone we can add. tz='Asia/Kolkata'

import pandas as pd 
pd.date_range(start='1/1/2010', periods=3,freq='4M' ,tz='Asia/Kolkata')

Output

DatetimeIndex(['2010-01-31 00:00:00+05:30', '2010-05-31 00:00:00+05:30',
               '2010-09-30 00:00:00+05:30'],
              dtype='datetime64[ns, Asia/Kolkata]', freq='4M')

normalize

Normalize the start and end dates to midnight before generating the date range.

name

We can give name of to the resulting DataFrame.

closed

Make the interval close to left ( start ) or right ( end ), the default value is None
closed='right'

pd.date_range(start='1/1/2010', end='1/5/2010',closed='right')

Output

DatetimeIndex(['2010-01-02', '2010-01-03', '2010-01-04', '2010-01-05'])

Let us change it to closed='left'

pd.date_range(start='1/1/2010', end='1/5/2010',closed='left')

output

DatetimeIndex(['2010-01-01', '2010-01-02', '2010-01-03', '2010-01-04'])

Practice this exercise to understand how to use date and time in Pandas DataFrame.
Exercise3 on Date and time
Exercise3-2 on basics of Date and time

Pandas date & time to_datetime() period_range() strftime()

Pandas Python & MySQL Python- Tutorials

Subscribe to our YouTube Channel here