Python | Pandas Series.dt.date

Michael Zippo 18.07.2021

Series.dt can be used to access series values as datetimelike and return multiple properties. Series.dt.date Pandas Series.dt.date returns an empty array of Python datetime.date objects.

Syntax: Series.dt.date

Parameter: None

Returns : numpy array

Example # 1: Use the Series.dt.date attribute to return the date property of the underlying data a Series object.

# import pandas as pd

import pandas as pd

# Create a series

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’ ])

# Create index

idx = [ ’ Day 1’ , ’Day 2’ , ’Day 3’ , ’ Day 4’ , ’ Day 5’ ]

# set index

sr.index = idx

# Convert baseline data to date and time

sr = pd.to_datetime (sr)

# Print series

print (sr)

Output:

We will now use the Series.dt.date attribute to return the date property of the underlying data of this Series object.

# return date

result = sr.dt.date

# print the result

print (result)

Output:

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

Example # 2: Use the Series.dt.date attribute to return the underlying date property of this Series object.

# import pandas as pd

import pandas as pd

# Create a series

sr = pd. Series (pd.date_range ( ’2012-12-12 12: 12’ ,

periods = 5 , freq = ’H’ ))

# Create index

idx = [ ’ Day 1’ , ’Day 2’ , ’Day 3’ , ’ Day 4’ , ’Day 5’ ]

   
  # set index  
   sr.index   =   idx 
   
  # Print series  
   print   (sr)

Output:

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

# return date

result = sr.dt.date

< code class = "comments"> # print the result

print (result)

Output:

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