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Python | Pandas Series.dt.to_period

Series.dt can be used to access series values ​​as datetimelike and return multiple properties. Series.dt.to_period() Pandas Series.dt.to_period() basic data of this Series object in PeriodArray / Index with a specific frequency.

Syntax: Series.dt.to_period (* args, ** kwargs)

Parameter:

freq: string or Offset, optional

Returns: PeriodArray / Index

Example # 1: Use Series.dt.to_period () to index the underlying data of this series object at weekly frequency.

# import pandas as pd

import pandas as pd

 
# Create series

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’ ])

 
# Create index

idx = [ ’Day 1’ , ’ Day 2’ , ’Day 3’ , ’ Day 4’ , ’Day 5’ ]

  
# set index

sr.index = idx

 
# Convert base data to date and time

sr = pd.to_datetime (sr)

 
# Print series

print (sr)

Output:

We will now use Series.dt.to_period () to index the underlying data of this series object with weekly frequency.

# targeting target frequency

result = sr.dt.to_period (freq = ’ W’

 
# print the result

print (result)

Output:

As we can see in the output, Series.dt.to_period () successfully Series .dt.to_period () data to target frequency.

Example # 2: Use Series.dt.to_period () to cast the base data of this series object into a biennial index.

# import pandas as pd

import pandas as pd

 
# Create series

sr = pd.Series (pd.date_range ( ’2012-12-31 00:00’ , periods = 5 , freq = ’D’ ,

tz = ’US / Central’ ))

 
# Create index

idx = [ ’Day 1’ , ’Day 2’ , ’ Day 3’ , ’Day 4’ , ’ Day 5’ ]

 
# set index

sr.index = idx

  
# Print series

print (sr)

Output:

Now we will use Series.dt.to_period () to cast b the baseline data of this series object into a biennial index.

# targeting frequency conversion

result = sr.dt.to_period (freq = ’2Y’

  
# print the result

print (result )

Output:

As we can see in the output, Series.dt.to_period () successfully Series.dt.to_period () data to target frequency.

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