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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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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

Exploring regression modeling

You have already used regression models in Chapter 9 and Chapter 10. Here, we will go deeper into regression modeling and compare linear and non-linear models for data modeling. A famous early example of regression analysis was produced by Sir Francis Galton, who lived in England from 1822 to 1911. Among many activities, Galton collected data on the heights of fathers and mothers and their adult children. It is notable that today, the data would be considered biased, as the sample was most likely from more affluent families that had access to better nutrition and living conditions than the average for the time in England. Nonetheless, the data serves as a good introduction to regression:

  1. Here, we load a simplified version of the data (adapted from the original) into a pandas DataFrame and plot the heights of all the children and the fathers:
    galton_heights = pd.read_csv('Datasets/galton.csv')
    galton_heights.head()

This produces...

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