The Series.dt.time attribute returns a NumPy array containing time values of the timestamps in a Pandas series.
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.time
print(result)
Output:
Syntax
Syntax: Series.dt.time
Parameter : None
Returns : NumPy array containing time values
How to Extract Time Value From DateTime Series
To extract the time value from the DateTime Pandas Series we use the dt.time attribute of the Pandas library.
Let us understand better with an example:
Example:
Use the 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.
