Pandas Series dt.to_pydatetime | Return Python DateTime Objects

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 :

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 :

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 :

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.