Python | Pandas DatetimeIndex.to_series()
Last Updated :
29 Dec, 2018
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 index
Return : 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.
import pandas as pd
didx = pd.DatetimeIndex(start = '2018-11-15 09:45:10' , freq = 'S' , periods = 5 )
print (didx)
|
Output :
Now we want to construct a series out of the DatetimeIndex object.
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.
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.
import pandas as pd
didx = pd.DatetimeIndex(start = '2015-03-02' , freq = 'M' , periods = 5 )
print (didx)
|
Output :
Now we want to construct a series out of the DatetimeIndex object.
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.
Like Article
Suggest improvement
Share your thoughts in the comments
Please Login to comment...