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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

Chapter 10: Data Modeling – Modeling Basics

In this chapter, you will learn how to discover patterns in data using resampling and smoothing. The .resample(), .rolling(), and .ewm() pandas methods will be introduced and you will learn how to use them to filter out the noise and perform other useful explorations of data series. You will learn how sampling can sometimes include data from future times, which is a problem for predictive modeling, and how to address that. At the end of the chapter, you will see how a combination of scaling (introduced in Chapter 9, Data Modeling – Preprocessing), and smoothing can show interesting similarities between different data series, which might otherwise be overlooked.

By the end of this chapter, you will be skilled at applying scaling, sampling, and smoothing in a variety of ways to your data analyses.

This chapter covers the following topics:

  • Learning the modeling basics
  • Predicting future values of time series
  • ...
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