Python | Pandas Series.asfreq()

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas Series.asfreq() function is used to convert TimeSeries to specified frequency. The function also provide filling method to pad/backfill missing values.

Syntax: Series.asfreq(freq, method=None, how=None, normalize=False, fill_value=None)



Parameter :
freq : DateOffset object, or string
method : {‘backfill’/’bfill’, ‘pad’/’ffill’}, default None
how : For PeriodIndex only, see PeriodIndex.asfreq
normalize : Whether to reset output index to midnight
fill_value : Value to use for missing values

Returns : converted : same type as caller

Example #1: Use Series.asfreq() function to change the frequency of the given series object.

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# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None])
  
# Create the Index
index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='M')
  
# set the index
sr.index = index_
  
# Print the series
print(sr)

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Output :

2010-12-31 08:45:00     8
2011-01-31 08:45:00    18
2011-02-28 08:45:00    65
2011-03-31 08:45:00    18
2011-04-30 08:45:00    32
2011-05-31 08:45:00    10
2011-06-30 08:45:00     5
2011-07-31 08:45:00    32
2011-08-31 08:45:00   NaN
Freq: M, dtype: float64

Now we will use Series.asfreq() function to change the frequency of the given series object to quarterly.

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# change to quarterly frequency
result = sr.asfreq(freq = 'Q')
  
# Print the result
print(result)

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Output :

2010-12-31 08:45:00     8
2011-03-31 08:45:00    18
2011-06-30 08:45:00     5
Freq: Q-DEC, dtype: float64

As we can see in the output, the Series.asfreq() function has successfully changed the frequency of the given series object.
 
Example #2 : Use Series.asfreq() function to change the yearly frequency of the given series object to the batches of 3 years.

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# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None])
  
# Create the Index
# apply yearly frequency
index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='Y')
  
# set the index
sr.index = index_
  
# Print the series
print(sr)

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Output :

2010-12-31 08:45:00    11.0
2011-12-31 08:45:00    21.0
2012-12-31 08:45:00     8.0
2013-12-31 08:45:00    18.0
2014-12-31 08:45:00    65.0
2015-12-31 08:45:00    18.0
2016-12-31 08:45:00    32.0
2017-12-31 08:45:00    10.0
2018-12-31 08:45:00     5.0
2019-12-31 08:45:00    32.0
2020-12-31 08:45:00     NaN
Freq: A-DEC, dtype: float64

Now we will use Series.asfreq() function to change the yearly frequency of the given series object to the batches of 3 years.

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# apply year batch frequency
result = sr.asfreq(freq = '3Y')
  
# Print the result
print(result)

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Output :

2010-12-31 08:45:00    11.0
2013-12-31 08:45:00    18.0
2016-12-31 08:45:00    32.0
2019-12-31 08:45:00    32.0
Freq: 3A-DEC, dtype: float64

As we can see in the output, the Series.asfreq() function has successfully changed the frequency of the given series object.



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