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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
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')
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')
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')
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)
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')
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')
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')
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