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Hands-On Data Analysis with Pandas

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

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800563452
Length 788 pages
Edition 2nd Edition
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Author (1):
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Stefanie Molin Stefanie Molin
Author Profile Icon Stefanie Molin
Stefanie Molin
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Table of Contents (21) Chapters Close

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...

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