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

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:

# 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.

# 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.