You're reading from Time Series Analysis with Python Cookbook Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Product type Paperback

Published in Jan 2026

Last Updated in Jul 2025

Publisher

ISBN-13 9781805124283

Length 98 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Analysis

Author (1):

Tarek A. Atwan

View More author details

Table of Contents (13) Chapters

1. Time Series Analysis with Python Cookbook, Second Edition: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation FREE CHAPTER

2. Getting Started with Time Series Analysis

3. Reading Time Series Data from Files

4. Reading Time Series Data from Databases

5. Persisting Time Series Data to Files

6. Persisting Time Series Data to Databases

7. Working with Date and Time in Python

8. Handling Missing Data

9. Outlier Detection Using Statistical Methods

10. Exploratory Data Analysis and Diagnosis

11. Building Univariate Time Series Models Using Statistical Methods

12. Additional Statistical Modeling Techniques for Time Series

13. Outlier Detection Using Unsupervised Machine Learning

Working with time deltas

When working with time-series data, you may need to perform some calculations on your datetime columns, such as adding or subtracting. Examples can include adding 30 days to purchase datetime to determine when the return policy expires for a product or when a warranty ends. For example, the Timedelta class makes it possible to derive new datetime objects by adding or subtracting at different ranges or increments, such as seconds, daily, and weekly. This includes time zone-aware calculations.

In this recipe, you will explore two practical approaches in pandas to capture date/time differences – the pandas.Timedelta class and the pandas.to_timedelta function.

How to do it…

In this recipe, you will work with hypothetical sales data for a retail store. You will generate the sales DataFrame, which will contain items purchased from the store and the purchase date. You will then explore different scenarios using the Timedelta class and the to_timedelta...