date_range() Method in Python Pandas

Working With time Series - date_range() Method in Python Pandas

*It is used to generate a range of date values.

*pandas.date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, **kwargs)

The Sample Code

With start and end parameters

# Generate 8 Dates 
Input - pd.date_range(start='1/1/2019', end='1/08/2019')

DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
               '2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08'],
              dtype='datetime64[ns]', freq='D')

Input - pd.date_range(start='1/1/2019', periods=8)

DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
               '2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08'],
              dtype='datetime64[ns]', freq='D')

Input - pd.date_range(end='1/1/2019', periods=8)

DatetimeIndex(['2018-12-25', '2018-12-26', '2018-12-27', '2018-12-28',
               '2018-12-29', '2018-12-30', '2018-12-31', '2019-01-01'],
              dtype='datetime64[ns]', freq='D')

Input - pd.date_range(start='1/1/2019', end='1/20/2019', periods=10)

DatetimeIndex(['2019-01-01 00:00:00', '2019-01-03 02:40:00',
               '2019-01-05 05:20:00', '2019-01-07 08:00:00',
               '2019-01-09 10:40:00', '2019-01-11 13:20:00',
               '2019-01-13 16:00:00', '2019-01-15 18:40:00',
               '2019-01-17 21:20:00', '2019-01-20 00:00:00'],
              dtype='datetime64[ns]', freq=None)

Input - pd.date_range(start='1/1/2019', periods=12, freq='B')

DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
               '2019-01-07', '2019-01-08', '2019-01-09', '2019-01-10',
               '2019-01-11', '2019-01-14', '2019-01-15', '2019-01-16'],
              dtype='datetime64[ns]', freq='B')

# How to set timezone

Input - pd.date_range(start='1/1/2019', periods=22, tz='Asia/Kolkata')

DatetimeIndex(['2019-01-01 00:00:00+05:30', '2019-01-02 00:00:00+05:30',
               '2019-01-03 00:00:00+05:30', '2019-01-04 00:00:00+05:30',
               '2019-01-05 00:00:00+05:30', '2019-01-06 00:00:00+05:30',
               '2019-01-07 00:00:00+05:30', '2019-01-08 00:00:00+05:30',
               '2019-01-09 00:00:00+05:30', '2019-01-10 00:00:00+05:30',
               '2019-01-11 00:00:00+05:30', '2019-01-12 00:00:00+05:30',
               '2019-01-13 00:00:00+05:30', '2019-01-14 00:00:00+05:30',
               '2019-01-15 00:00:00+05:30', '2019-01-16 00:00:00+05:30',
               '2019-01-17 00:00:00+05:30', '2019-01-18 00:00:00+05:30',
               '2019-01-19 00:00:00+05:30', '2019-01-20 00:00:00+05:30',
               '2019-01-21 00:00:00+05:30', '2019-01-22 00:00:00+05:30'],
              dtype='datetime64[ns, Asia/Kolkata]', freq='D')

 dates = pd.date_range(start='1/1/2020', end='1/10/2020')

dates

DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',
               '2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08',
               '2020-01-09', '2020-01-10'],
              dtype='datetime64[ns]', freq='D')

df.set_index(dates, inplace=True)

df

	meantemp	humidity	wind_speed	meanpressure
2020-01-01	15.913043	85.869565	2.743478	59.000000
2020-01-02	18.500000	77.222222	2.894444	1018.277778
2020-01-03	17.111111	81.888889	4.016667	1018.333333
2020-01-04	18.700000	70.050000	4.545000	1015.700000
2020-01-05	18.388889	74.944444	3.300000	1014.333333
2020-01-06	19.318182	79.318182	8.681818	1011.772727
2020-01-07	14.708333	95.833333	10.041667	1011.375000
2020-01-08	15.684211	83.526316	1.950000	1015.550000
2020-01-09	14.571429	80.809524	6.542857	1015.952381