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.

# 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)

Output :

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

# return the time
result = sr.dt.time

# print the result
print(result)

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.

# 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 Series.dt.time attribute to return the time property of the underlying data of the given Series object.

# return the time
result = sr.dt.time

# print the result
print(result)

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.