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 DatetimeIndex.to_series() function create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index.
Syntax: DatetimeIndex.to_series(keep_tz=False, index=None, name=None)
Parameters :
keep_tz : return the data keeping the timezone
index : index of resulting Series. If None, defaults to original index
name : name of resulting Series. If None, defaults to name of original indexReturn : Series
Example #1: Use DatetimeIndex.to_series() function to create a series object from the given DatetimeIndex object. Also set the value of index for the series.
# importing pandas as pd import pandas as pd   # Create the DatetimeIndex # Here 'S' represents secondly frequency didx = pd.DatetimeIndex(start ='2018-11-15 09:45:10', freq ='S', periods = 5)   # Print the DatetimeIndex print(didx) |
Output :
Now we want to construct a series out of the DatetimeIndex object.
# construct the series didx.to_series(index =['A', 'B', 'C', 'D', 'E']) |
Output :
As we can see in the output, the function has returned a series object constructed from the didx DatetimeIndex object.
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Example #2: Use DatetimeIndex.to_series() function to create a series object from the given DatetimeIndex object. Also set the value of index for the series.
# importing pandas as pd import pandas as pd   # Create the DatetimeIndex # Here 'M' represents monthly frequency didx = pd.DatetimeIndex(start ='2015-03-02', freq ='M', periods = 5)   # Print the DatetimeIndex print(didx) |
Output :
Now we want to construct a series out of the DatetimeIndex object.
# construct the series didx.to_series(index =['First', 'Second', 'Third', 'Fourth', 'Fifth']) |
Output :
As we can see in the output, the function has returned a series object constructed from the didx DatetimeIndex object.
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