Python | Pandas Series.dt.time

Series.dt can be used to access the values of the series as datetimelike and return several properties. Pandas Series.dt.time attribute return a numpy array of python datetime.time objects.

Syntax: Series.dt.time

Parameter : None



Returns : numpy array

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

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# importing pandas as pd
import pandas as pd
  
# Creating the 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'])
  
# Creating the index
idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5']
  
# set the index
sr.index = idx
  
# Convert the underlying data to datetime 
sr = pd.to_datetime(sr)
  
# Print the series
print(sr)

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Output :

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

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

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Output :

As we can see in the output, the Series.dt.time attribute has successfully accessed and returned the time property of the underlying data in the given series object.
 
Example #2 : Use Series.dt.time attribute to return the time property of the underlying data of the given Series object.

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# 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)

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Output :

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

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

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Output :

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



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