Series.dt can be used to access the values of the series as datetimelike and return several properties. Pandas Series.dt.freq attribute return the time series frequency applied on the given series object if any, else it return None.
Syntax: Series.dt.freq
Parameter : None
Returns : frequency
Example #1: Use Series.dt.freq attribute to find the frequency of the underlying datetime based data in the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(['2012-12-31', '2019-1-1 12:30', '2008-02-2 10:30', '2010-1-1 09:25', '2019-12-31 00:00']) # 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.freq attribute to find the frequency of the datetime based data in the given series object.
# find the frequency result = sr.dt.freq # print the result print(result) |
Output :
As we can see in the output, the Series.dt.freq attribute has returned None indicating the frequency for the given datetime data is not known.
Example #2 : Use Series.dt.freq attribute to find the frequency of the underlying datetime based data in the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series(pd.date_range('2012-12-31 00:00', periods = 5, freq = 'D', tz = 'US / Central')) # 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.freq attribute to find the frequency of the datetime based data in the given series object.
# find the frequency result = sr.dt.freq # print the result print(result) |
Output :
As we can see in the output, the Series.dt.freq attribute has successfully returned the frequency of the underlying datetime based data in the given Series object.
