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The Pandas Workshop

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

Product type Paperback

Published in Jun 2022

Publisher Packt

ISBN-13 9781800208933

Length 744 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Data Analysis

Authors (4):

Blaine Bateman

William So

Saikat Basak

Thomas Joseph

View More author details

Table of Contents (21) Chapters

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

The world of data

In today's digitally driven world, data is being generated at an ever-increasing pace. The World Economic Forum reports that, by 2025, 463 exabytes of data will be created each day globally. An exabyte is a 1 followed by 18 zeros. That is just a little less than 5,359 TB per second, or 5.3 million GB per second. Unsurprisingly, not all of this data is in the form of simple text files. While CSV files are very common and highly useful, they are just one of the many possible data formats we may want to work with while using pandas. In this chapter, we will explore more options to bring data into pandas DataFrames and Series, and store data back in memory. Such data operations are called input/output or I/O operations. For example, data from your finance team might be from software such as SAS or Stata. Furthermore, if you work with "big data," you may need to access Parquet or HDF data. Depending on your business requirements and the complexity of the...

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Authors (4)

Blaine Bateman

Blaine Bateman

Blaine Bateman has more than 35 years of experience working with various industries from government R&D to startups to $1B public companies. His experience focuses on analytics including machine learning and forecasting. His hands-on abilities include Python and R coding, Keras/Tensorflow, and AWS & Azure machine learning services. As a machine learning consultant, he has developed and deployed actual ML models in industry.

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Thomas Joseph

Thomas Joseph

Thomas V. Joseph is a data science practitioner, researcher, trainer, mentor, and writer with more than 19 years of experience. He has extensive experience in solving business problems using machine learning toolsets across multiple industry segments.

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William So

William So

William So is a Data Scientist with both a strong academic background and extensive professional experience. He is currently the Head of Data Science at Douugh and also a Lecturer for Master of Data Science and Innovation at the University of Technology Sydney. During his career, he successfully covered the end-end spectrum of data analytics from ML to Business Intelligence helping stakeholders derive valuable insights and achieve amazing results that benefits the business. William is a co-author of the "The Applied Artificial Intelligence Workshop" published by Packt.

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Saikat Basak

Saikat Basak

Saikat Basak is a data scientist and a passionate programmer. Having worked with multiple industry leaders, he has a good understanding of problem areas that can potentially be solved using data. Apart from being a data guy, he is also a science geek and loves to explore new ideas in the frontiers of science and technology.

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