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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

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781800208933
Length 744 pages
Edition 1st Edition
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Authors (4):
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Blaine Bateman Blaine Bateman
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Blaine Bateman
William So William So
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William So
Saikat Basak Saikat Basak
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Saikat Basak
Thomas Joseph Thomas Joseph
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Thomas Joseph
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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

Summary

In this chapter, you learned about the pandas methods for data indexing and selection by using the primary pandas data structure – the DataFrame. You compared the DataFrame.loc() and DataFrame.iloc() methods to access items in DataFrames by labels and integer locations, respectively. You also looked at some pandas shortcut methods, including bracket notation, dot notation, and extended indexing. Along the way, you saw how the pandas index is used behind the scenes to align data, and how that can be changed by changing or resetting the index. In addition, we showed you that in many cases, you can assign new values to a subset of data by using it on the left-hand side of an assignment statement (using the equals operator). This creates a very compact and easy-to-read coding style. We saw that an important pandas capability that involved using labels for the row or column index produced more robust code – instead of "hardcoding" the column numbers, they...

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