Pandas Series dt.to_period() Method | Convert DateTime to Period Format
Last Updated :
06 Feb, 2024
The Pandas dt.to_period() method converts the underlying data of the given Series object to PeriodArray/Index at a particular frequency.
It is used to convert a DateTime series to a period series with a specific frequency, such as daily, monthly, quarterly, or yearly periods.
Example
Python3
import pandas as pd
sr = pd.Series([ '2012-12-31' , '2019-1-1 12:30' , '2008-02-2 10:30' ,
'2010-1-1 09:25' , '2019-12-31 00:00' ])
idx = [ 'Day 1' , 'Day 2' , 'Day 3' , 'Day 4' , 'Day 5' ]
sr.index = idx
sr = pd.to_datetime(sr)
result = sr.dt.to_period(freq = 'W' )
print (result)
|
Output:
Syntax
Syntax: Series.dt.to_period(freq)
Parameter
- freq : string or Offset, optional
Returns: The original Series cast to PeriodArray/Index at the specified frequencyÂ
How to Convert a Pandas DateTime Series to a Period Series
To convert a Pandas DateTime Series to a Period series we use the dt.to_period() method of the Pandas library in Python.Â
Let us understand it better with an example:
Example
Use the dt.to_period() function to cast the underlying data of the given series object to Index at two-year frequency.Â
Python3
import pandas as pd
sr = pd.Series(pd.date_range( '2012-12-31 00:00' , periods = 5 , freq = 'D' ,
tz = 'US / Central' ))
idx = [ 'Day 1' , 'Day 2' , 'Day 3' , 'Day 4' , 'Day 5' ]
sr.index = idx
print (sr)
|
Output:
Now we can use dt.to_period() method to convert it to period format
Python3
result = sr.dt.to_period(freq = '2Y' )
print (result)
|
Output:
As we can see in the output, the Series.dt.to_period() function has successfully cast the data to the target frequency.Â
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