Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
The Pandas Workshop

You're reading from   The Pandas Workshop A comprehensive guide to using Python for data analysis with real-world case studies

Arrow left icon
Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781800208933
Length 744 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (4):
Arrow left icon
Blaine Bateman Blaine Bateman
Author Profile Icon Blaine Bateman
Blaine Bateman
William So William So
Author Profile Icon William So
William So
Saikat Basak Saikat Basak
Author Profile Icon Saikat Basak
Saikat Basak
Thomas Joseph Thomas Joseph
Author Profile Icon Thomas Joseph
Thomas Joseph
Arrow right icon
View More author details
Toc

Table of Contents (21) Chapters Close

Preface 1. Part 1 – Introduction to pandas
2. Chapter 1: Introduction to pandas FREE CHAPTER 3. Chapter 2: Working with Data Structures 4. Chapter 3: Data I/O 5. Chapter 4: Pandas Data Types 6. Part 2 – Working with Data
7. Chapter 5: Data Selection – DataFrames 8. Chapter 6: Data Selection – Series 9. Chapter 7: Data Exploration and Transformation 10. Chapter 8: Understanding Data Visualization 11. Part 3 – Data Modeling
12. Chapter 9: Data Modeling – Preprocessing 13. Chapter 10: Data Modeling – Modeling Basics 14. Chapter 11: Data Modeling – Regression Modeling 15. Part 4 – Additional Use Cases for pandas
16. Chapter 12: Using Time in pandas 17. Chapter 13: Exploring Time Series 18. Chapter 14: Applying pandas Data Processing for Case Studies 19. Chapter 15: Appendix 20. Other Books You May Enjoy

Introduction to time series

Time series data is nearly ubiquitous but can be a pain point in many analyses. For example, suppose you are asked to forecast sales for a retail store and are given daily sales figures for the last 6 months. When you review the data, you realize the store is usually open 5 days a week but sometimes has sales on Saturdays and even some Sundays. This makes most weekend days have missing values, and the time interval of the data is inconsistent. Also, when you consider estimating a monthly forecast, you realize months are of different lengths and have varying numbers of sales days. As simple and obvious as the issues are, they create a number of issues in analyzing and modeling the data over time.

The machine learning literature and popular articles are heavily biased toward classification problems, with little mention of time series. Yet much of the data we deal with is time series or at least starts out that way. Time series is a general term used to...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image