How to Convert the TimeSeries using the series.asfreq() method?

PandasServer Side ProgrammingProgramming

The pandas.Series.asfreq() method is used to convert the Time Series to the specified frequency. By using the parameters of this method we can fill missing(null) values also.

It will return a series object with reindexed frequency, which is specified through the asfreq() method. The parameters of the asfreq() method are freq, method=None, how=None, normalize=False, and fill_value=None. Other than freq remaining all parameters have default values.

Let's create a timeseries object by using the pandas date_range module and apply the asfreq() method.

Example 1

import pandas as pd

# create the index
index = pd.date_range('2021-07-01', periods=10, freq='H')

#creating pandas Series with date index
series = pd.Series([1,3,None,5,7,9,None,2,3,4], index=index)

print(series)

# converted time series
print("Converted time series",series.asfreq(freq='2H'))

Explanation

Initially, we imported the pandas module into our workspace by using the python import keyword, then we created an index variable with time sequence data by using the date range function, after that, we have created a pandas series object with a time sequence.

Output

2021-07-01 00:00:00 1.0
2021-07-01 01:00:00 3.0
2021-07-01 02:00:00 NaN
2021-07-01 03:00:00 5.0
2021-07-01 04:00:00 7.0
2021-07-01 05:00:00 9.0
2021-07-01 06:00:00 NaN
2021-07-01 07:00:00 2.0
2021-07-01 08:00:00 3.0
2021-07-01 09:00:00 4.0
Freq: H, dtype: float64

Converted time series
2021-07-01 00:00:00 1.0
2021-07-01 02:00:00 NaN
2021-07-01 04:00:00 7.0
2021-07-01 06:00:00 NaN
2021-07-01 08:00:00 3.0
Freq: 2H, dtype: float64

The initial time series is created by 10 periods with the frequency of hours. Which is displayed in the 1st part of the above output block.

And the converted time series object is also displayed in the above output block, where the frequency is converted to 2hours. And the resultant series is having only 5 elements in it.

Example 2

import pandas as pd

# create the index
index = pd.date_range('2021-06-30', periods=10, freq='M')

#creating pandas Series with date index
series = pd.Series(index.month_name(), index=index)

print(series)

# converted time series
print("Converted time series",series.asfreq(freq='Q'))

Explanation

In this above example, we have created a pandas Series using the Date_range method with 10 periods of months frequency. After that, we have changed the frequency of the series object to quarterly.

Output

2021-06-30      June
2021-07-31      July
2021-08-31    August
2021-09-30 September
2021-10-31   October
2021-11-30  November
2021-12-31  December
2022-01-31   January
2022-02-28  February
2022-03-31     March
Freq: M, dtype: object

Converted time series
2021-06-30      June
2021-09-30 September
2021-12-31  December
2022-03-31     March
Freq: Q-DEC, dtype: object

In the above output block, we can see both the initial timeseries object and converted timeseries object. The converted time series is updated to quarterly frequency, by providing the “Q” value to the “freq” parameter of the asfreq method.

raja
Updated on 09-Mar-2022 07:16:06

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