Pandas dt.freq attribute returns the time series frequency applied on the given series object if any, else it returns None.
Examples
import pandas as pd
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' ])
idx = [ 'Day 1' , 'Day 2' , 'Day 3' , 'Day 4' , 'Day 5' ]
sr.index = idx
sr = pd.to_datetime(sr)
result = sr.dt.freq
print (result)
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Output
Syntax
Syntax: Series.dt.freq
Parameter: None
Returns: frequency
How to Find Frequency of Time Series Data in Pandas
To find the frequency of the time series we use dt.freq attribute of the Pandas library in Python.
Let us understand it with an example:
Example:
Use the 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)
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Output :
Now we will use the 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)
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Output :
As we can see in the output, the dt.freq attribute has successfully returned the frequency of the underlying DateTime based data in the given Series object.