Python | Панды Series.dt.to_pydatetime

Опубликовано: 27 Марта, 2022

Series.dt can be used to access the values of the series as datetimelike and return several properties. Pandas Series.dt.to_pydatetime() function return the data as an array of native Python datetime objects. Timezone information is retained if present.

Syntax: Series.dt.to_pydatetime()
Parameter : None
Returns : numpy.ndarray

Example #1: Use Series.dt.to_pydatetime() function to return the given series object as an array of native python datetime object.

Выход :

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

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

Выход :

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.

Example #2 : Use Series.dt.to_pydatetime() function to return the given series object as an array of native python datetime object.

# 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)

Выход :

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

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

Выход :

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.

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Python

Python | Панды Series.dt.to_pydatetime

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