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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

Solution 10.1

Suppose you are an analyst in a financial advisory firm. Your manager has given three stock symbols to you and requested your input on how they may be correlated in their price behavior. You are provided with a data file called stocks.csv, which contains the symbols, the closing prices, the trading volumes, and a sentiment indicator (some view of the stocks' quality, but you are not told the exact definition). Your initial goal here is to determine whether all three stocks show similar market characteristics, and if any or all of them do, make an initial visualization using smoothing. The long-term goal is to try to build some predictive models, so you will split the data into training and test sets. As it is a time series, it's important to split on time, not randomly. For this activity, all you will need is the pandas library, a scaling module from sklearn, and matplotlib.

Perform the following steps to complete the activity:

  1. Load the required...
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