Pandas Series dt.date | Extract Date From DateTime Objects
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:
Syntax
Syntax: Series.dt.date
Parameter : None
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 :
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 :
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.
Contact Us