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 Apr 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

Converting DateTime with time zone information

When working with time-series data that requires attention to different time zones, things can get out of hand and become more complicated. For example, when developing data pipelines, building a data warehouse, or integrating data between systems, dealing with time zones requires attention and consensus amongst the different stakeholders in the project. For example, in Python, there are several libraries and modules dedicated to working with time zone conversion; these include pytz, dateutil, and zoneinfo, to name a few.

Let's discuss an inspiring example regarding time zones within time-series data. It is common for large companies that span their products and services across continents to include data from different places around the globe. For example, it would be hard to make data-driven business decisions if we neglect time zones. Let's say you want to determine whether most customers come to your e-commerce site in the morning...