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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

Introduction to data modeling

Data is often provided to you in a form that isn't completely suitable for analysis and modeling. As an example, suppose you are trying to summarize and analyze the sales of students selling cookies in an effort to raise money for a school trip. You would like to get an idea of the expected sales per student per week, in order to recognize students putting in effort and achieving higher sales. Unfortunately, the data for any given student comes in at somewhat random times, making comparisons more difficult. You decide to take each student's sales and fill in the missing days by interpolating between the days for which you have data. The process is quite tedious, and part-way through, you realize you will also have to go back and divide each day by the weekly total, otherwise you are inflating the total sales. Pandas provides the .resample() method you saw in Chapter 9, Data Modeling – Preprocessing, and by combing that with a .rolling...

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