Pandas Series dt.to_period() Method | Convert DateTime to Period Format

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

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 frequency 

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. 

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. 

# importing pandas as pd
import pandas as pd

# Creating the Series
sr = pd.Series(pd.date_range('2012-12-31 00:00', periods = 5, freq = 'D',
                            tz = 'US / Central'))

# Creating the index
idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5']

# set the index
sr.index = idx

# Print the series
print(sr)

Output:

Now we can use dt.to_period() method to convert it to period format

# cast to target frequency
result = sr.dt.to_period(freq = '2Y') 

# print the result
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.