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Applied Computational Thinking with Python

You're reading from   Applied Computational Thinking with Python Design algorithmic solutions for complex and challenging real-world problems

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781839219436
Length 420 pages
Edition 1st Edition
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Authors (2):
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Dayrene Martinez Dayrene Martinez
Author Profile Icon Dayrene Martinez
Dayrene Martinez
Sofía De Jesús Sofía De Jesús
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Sofía De Jesús
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Introduction to Computational Thinking
2. Chapter 1: Fundamentals of Computer Science FREE CHAPTER 3. Chapter 2: Elements of Computational Thinking 4. Chapter 3: Understanding Algorithms and Algorithmic Thinking 5. Chapter 4: Understanding Logical Reasoning 6. Chapter 5: Exploring Problem Analysis 7. Chapter 6: Designing Solutions and Solution Processes 8. Chapter 7: Identifying Challenges within Solutions 9. Section 2:Applying Python and Computational Thinking
10. Chapter 8: Introduction to Python 11. Chapter 9: Understanding Input and Output to Design a Solution Algorithm 12. Chapter 10: Control Flow 13. Chapter 11: Using Computational Thinking and Python in Simple Challenges 14. Section 3:Data Processing, Analysis, and Applications Using Computational Thinking and Python
15. Chapter 12: Using Python in Experimental and Data Analysis Problems 16. Chapter 13: Using Classification and Clusters 17. Chapter 14: Using Computational Thinking and Python in Statistical Analysis 18. Chapter 15: Applied Computational Thinking Problems 19. Chapter 16: Advanced Applied Computational Thinking Problems 20. Other Books You May Enjoy

Using additional libraries for plotting and analysis

Before we end this chapter on experimental data, the use of libraries, and plotting and analyzing data, let's look at three more libraries that are helpful in data analysis and plotting. These are not the only libraries for analysis and plotting, nor will they be the only ones we explore throughout the rest of this book:

  • Seaborn is a library used for data visualization; built on top of Matplotlib.
  • SciPy is a library used for linear algebra, optimization, statistics, and more; built on top of NumPy.
  • Scikit-Learn is a library used in machine learning; part of the SciPy stack.

In the following chapters, we'll go deeper into the use of some of these libraries as we tackle some of the application problems that require their use. For now, let's take a quick look at what each of these libraries can help us with when looking at datasets.

Using the Seaborn library

The Seaborn library provides us...

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