Pandas Series Asfreq Method

Introduction

The Series.asfreq() method in Pandas is used to convert a time series to a specified frequency. It allows us to fill in missing values or null values in the time series. This tutorial will guide you on how to use the Series.asfreq() method in Python Pandas.

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Skills Graph

Import the necessary libraries

import pandas as pd

Create a datetime index

index = pd.date_range('1/4/2021', periods=4, freq='T')

Create a Series with missing values

series = pd.Series([1.0, None, None, 3.0], index=index)

Print the original Series

print("--------The Series is-------")
print(series)

Convert the Series to a specified frequency

print("-----------After converting the Timeseries-----------")
print(series.asfreq(freq='H'))

Upsample the Series and fill missing values

print("-----------Upsample Timeseries and fill values-----------")
print(series.asfreq(freq='40s', fill_value=5.0))

Upsample the Series using 'bfill' method

print("-----------Upsample Timeseries using 'bfill' method-----------")
print(series.asfreq(freq='70s', method='bfill'))

Summary

In this tutorial, we learned how to use the Series.asfreq() method in Python Pandas. We used this method to convert a time series to a specified frequency and filled in missing values or null values. We also learned how to upsample the frequency of the time series and fill in missing values using the 'bfill' method. The Series.asfreq() method is a useful tool for working with time series data in Pandas.