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Python | Pandas Series.dt.time

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

Syntax: Series.dt.time

Parameter: None

Returns : numpy array

Example # 1: Use the Series.dt.time attribute to return the time 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 base data to date and time

sr = pd.to_datetime (sr)

 
# Print series

print (sr)

Output:

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

# return time

result = sr.dt.time

 
# print the result

print (result)

Output:

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

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

# import pandas as pd

import pandas as pd

 
# With series building

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)

Exit:

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

# return time

result = sr.dt.time

 
# print the result

print (result)

Output:

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

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