Python | Pandas Timestamp.to_pydatetime

Pandas Series dt.to_pydatetime | Return Python DateTime Objects

Last Updated : 08 Feb, 2024

Pandas dt.to_pydatetime() method returns the data as an array of native Python DateTime objects. Timezone information is retained if present.

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_pydatetime()  
print(result)

Output :

output of dt.to_pydatetime

Syntax

Syntax: Series.dt.to_pydatetime()

Parameter: None

Returns: numpy ndarray

How to Convert Pandas DateTime Objects into Python DateTime Objects

To convert a Pandas Series DateTime object into a Python DateTime object we use the Series.dt.to_pydatetime method.

Let us understand it better with an example:

Example:

Use the Series.dt.to_pydatetime() function to return the given series object as an array of native Python DateTime objects.

Python3

# 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 :

datetime series created

Now we will use the dt.to_pydatetime() function to return the data as an array of native Python DateTime objects.

Python3

# return the series data as a  
# native python datetime data 
result = sr.dt.to_pydatetime()  
  
# print the result 
print(result) 

Output :

python datetime objects returned

As we can see in the output, the Series.dt.to_pydatetime() function has successfully returned the underlying data of the given series object as an array of native Python DateTime data.

Python | Pandas Timestamp.to_pydatetime

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