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Pandas Series dt.date | Extract Date From DateTime Objects

Last Updated : 06 Feb, 2024
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The dt.date attribute extracts the date part of the DateTime objects in a Pandas Series.

It returns the NumPy array of Python datetime.date objects, mainly the date part of timestamps without information about the time and timezone.

Example

Python3




import pandas as pd
sr = pd.Series(['2012-10-21 09:30', '2019-7-18 12:30', '2008-02-2 10:30',
               '2010-4-22 09:25', '2019-11-8 02:22'])
idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5']
sr.index = idx
sr = pd.to_datetime(sr)
result = sr.dt.date
print(result)


Output:

Date part extracted using dt.date

Syntax

Syntax: Series.dt.dateĀ 

Parameter : NoneĀ 

Returns :NumPy array with datetime.date objects

How to Extract Date from a DateTime object in Pandas Series

To extract the date from a DateTime object in Pandas Series we use the dt.date attribute of the Pandas library in Python.

Let us understand it better with an example:

Example:

Python3




# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series(pd.date_range('2012-12-12 12:12'
                       periods = 5, freq = 'H'))
  
# 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 :

created Series

Now we will use the dt.date attribute to return the date property of the underlying data of the given Series object.

Python3




# return the date
result = sr.dt.date
  
# print the result
print(result)


Output :

date part of series

As we can see in the output, the dt.date attribute has successfully accessed and returned the date property of the underlying data in the given series object.



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