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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

Chapter 3: Data I/O

In the previous chapter, you learned how pandas stores information in Series and DataFrames, and were introduced to the .read_csv() and .to_csv() methods, which are used to read data from a storage drive into DataFrames or Series and save data in a CSV file. In this chapter, you will learn about a range of other data sources, including different file formats and data from web pages and application programming interfaces (APIs). By the end of this chapter, you will be comfortable with getting data into pandas from a wide range of common sources, enabling you to work with different teams in your organization.

In this chapter, we will cover the following topics:

The world of data
Exploring data sources
Fundamental formats
Additional text formats
Manipulating SQL data
Activity 3.01 – using SQL data for pandas analytics

The rest of the chapter is locked

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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.

See other products by Blaine Bateman

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.

See other products by Thomas Joseph

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.

See other products by William So

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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