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.