Series.dt can be used to access the values of the series as datetimelike and return several properties. Pandas Series.dt.microsecond attribute return a numpy array containing the microsecond of the datetime in the underlying data of the given series object.
Syntax: Series.dt.microsecond
Parameter : None
Returns : numpy array
Example #1: Use Series.dt.microsecond attribute to return the microsecond of the datetime in 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 = '5U'))
# 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.microsecond attribute to return the microsecond of the datetime in the underlying data of the given Series object.
# return the microsecond result = sr.dt.microsecond
# print the result print(result)
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Output :
As we can see in the output, the Series.dt.microsecond attribute has successfully accessed and returned the microsecond of the datetime in the underlying data of the given series object.
Example #2 : Use Series.dt.microsecond attribute to return the microsecond of the datetime in 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('2008-2-9 08:20:21',
periods = 5, freq = '9U'))
# 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.microsecond attribute to return the microsecond of the datetime in the underlying data of the given Series object.
# return the microsecond result = sr.dt.microsecond
# print the result print(result)
|
Output :
As we can see in the output, the Series.dt.microsecond attribute has successfully accessed and returned the microsecond of the datetime in the underlying data of the given series object.