You're reading from Hands-On Data Analysis with Pandas A Python data science handbook for data collection, wrangling, analysis, and visualization

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800563452

Length 788 pages

Edition 2nd Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Author (1):

Stefanie Molin

View More author details

Table of Contents (21) Chapters

Preface

1. Section 1: Getting Started with Pandas

2. Chapter 1: Introduction to Data Analysis FREE CHAPTER

3. Chapter 2: Working with Pandas DataFrames

4. Section 2: Using Pandas for Data Analysis

5. Chapter 3: Data Wrangling with Pandas

6. Chapter 4: Aggregating Pandas DataFrames

7. Chapter 5: Visualizing Data with Pandas and Matplotlib

8. Chapter 6: Plotting with Seaborn and Customization Techniques

9. Section 3: Applications – Real-World Analyses Using Pandas

10. Chapter 7: Financial Analysis – Bitcoin and the Stock Market

11. Chapter 8: Rule-Based Anomaly Detection

12. Section 4: Introduction to Machine Learning with Scikit-Learn

13. Chapter 9: Getting Started with Machine Learning in Python

14. Chapter 10: Making Better Predictions – Optimizing Models

15. Chapter 11: Machine Learning Anomaly Detection

16. Section 5: Additional Resources

17. Chapter 12: The Road Ahead

18. Solutions

19. Other Books You May Enjoy

Appendix

Chapter 2: Working with Pandas DataFrames

The time has come for us to begin our journey into the pandas universe. This chapter will get us comfortable working with some of the basic, yet powerful, operations we will be performing when conducting our data analyses with pandas.

We will begin with an introduction to the main data structures we will encounter when working with pandas. Data structures provide us with a format for organizing, managing, and storing data. Knowledge of pandas data structures will prove infinitely helpful when it comes to troubleshooting or looking up how to perform an operation on the data. Keep in mind that these data structures are different from the standard Python data structures for a reason: they were created for specific analysis tasks. We must remember that a given method may only work on a certain data structure, so we need to be able to identify the best structure for the problem we are looking to solve.

Next, we will bring our first dataset...