Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
pandas.date_range() is one of the general functions in Pandas which is used to return a fixed frequency DatetimeIndex.
Syntax: pandas.date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, **kwargs)
Parameters:
start : Left bound for generating dates.
end : Right bound for generating dates.
periods : Number of periods to generate.
freq : Frequency strings can have multiples, e.g. ‘5H’. See here for a list of frequency aliases.
tz : Time zone name for returning localized DatetimeIndex. By default, the resulting DatetimeIndex is timezone-naive.
normalize : Normalize start/end dates to midnight before generating date range.
name : Name of the resulting DatetimeIndex.
closed : Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None, the default).Returns: DatetimeIndex
Code #1:
# importing pandas as pdimport pandas as pd per1 = pd.date_range(start ='1-1-2018', end ='1-05-2018', freq ='5H') for val in per1: print(val) |
Output:
Code #2:
# importing pandas as pdimport pandas as pd dRan1 = pd.date_range(start ='1-1-2018', end ='8-01-2018', freq ='M') dRan2 = pd.date_range(start ='1-1-2018', end ='11-01-2018', freq ='3M') print(dRan1, '\n\n', dRan2) |
Output:
Code #3:
# importing pandas as pdimport pandas as pd # Specify start and periods, the number of periods (days).dRan1 = pd.date_range(start ='1-1-2018', periods = 13) # Specify end and periods, the number of periods (days).dRan2 = pd.date_range(end ='1-1-2018', periods = 13) # Specify start, end, and periods; the frequency # is generated automatically (linearly spaced).dRan3 = pd.date_range(start ='01-03-2017', end ='1-1-2018', periods = 13) print(dRan1, "\n\n", dRan2, '\n\n', dRan3) |
Output:
Code #4:
# importing pandas as pdimport pandas as pd # Specify start and periods, the number of periods (days).dRan1 = pd.date_range(start ='1-1-2018', periods = 13, tz ='Asia / Tokyo') dRan1 |
Output:
