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 Jun 2022

Publisher Packt

ISBN-13 9781801075541

Length 630 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Data Analysis

Author (1):

Tarek A. Atwan

View More author details

Table of Contents (18) Chapters

Preface

1. Chapter 1: Getting Started with Time Series Analysis

2. Chapter 2: Reading Time Series Data from Files FREE CHAPTER

3. Chapter 3: Reading Time Series Data from Databases

4. Chapter 4: Persisting Time Series Data to Files

5. Chapter 5: Persisting Time Series Data to Databases

6. Chapter 6: Working with Date and Time in Python

7. Chapter 7: Handling Missing Data

8. Chapter 8: Outlier Detection Using Statistical Methods

9. Chapter 9: Exploratory Data Analysis and Diagnosis

10. Chapter 10: Building Univariate Time Series Models Using Statistical Methods

11. Chapter 11: Additional Statistical Modeling Techniques for Time Series

12. Chapter 12: Forecasting Using Supervised Machine Learning

13. Chapter 13: Deep Learning for Time Series Forecasting

14. Chapter 14: Outlier Detection Using Unsupervised Machine Learning

15. Chapter 15: Advanced Techniques for Complex Time Series

16. Index

Why subscribe?

17. Other Books You May Enjoy

Working with DatetimeIndex

The pandas library has many options and features to simplify tedious tasks when working with time-series data, dates, and time.

When working with time-series data in Python, it is common to load into a pandas DataFrame with an index of type DatetimeIndex. As an index, the DatetimeIndex class extends pandas DataFrame capabilities to work more efficiently and intelligently with time-series data. This was demonstrated numerous times in Chapter 2, Reading Time Series Data from Files, and Chapter 3, Reading Time Series Data from Databases.

By the end of this recipe, you will appreciate pandas' rich set of date functionality to handle almost any representation of date/time in your data. Additionally, you will learn how to use different functions in pandas to convert date-like objects to a DatetimeIndex.