Book Image

Dancing with Python

By : Robert S. Sutor
Book Image

Dancing with Python

By: Robert S. Sutor

Overview of this book

Dancing with Python helps you learn Python and quantum computing in a practical way. It will help you explore how to work with numbers, strings, collections, iterators, and files. The book goes beyond functions and classes and teaches you to use Python and Qiskit to create gates and circuits for classical and quantum computing. Learn how quantum extends traditional techniques using the Grover Search Algorithm and the code that implements it. Dive into some advanced and widely used applications of Python and revisit strings with more sophisticated tools, such as regular expressions and basic natural language processing (NLP). The final chapters introduce you to data analysis, visualizations, and supervised and unsupervised machine learning. By the end of the book, you will be proficient in programming the latest and most powerful quantum computers, the Pythonic way.
Table of Contents (29 chapters)
Part I: Getting to Know Python
PART II: Algorithms and Circuits
PART III: Advanced Features and Libraries
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Appendix C: The Complete UniPoly Class
Appendix D: The Complete Guitar Class Hierarchy
Appendix F: Production Notes

14.9 Cats in trees and circles

Though I have focused on matplotlib as the primary visualization engine for data, several other Python packages may suit your advanced needs. I especially suggest you look at seaborn and plotly. [SEA] [PLY]

I end this chapter by using our cat data to create treemaps and Venn diagrams.

14.9.1 Treemaps

Now that we have the colour data, we can visualize it in many different ways. The squarify module creates treemaps, which are rectangles broken down into sub-rectangles based on relative data counts.

First, we install squarify from the operating system command line: [SQU]

pip install squarify

and import it:

import matplotlib.pyplot as plt
import squarify

In the last section, we created the list colours containing all the unique cat colour codes in our DataFrame. We now create three other lists:

grey_colours = [colour for colour in colours if &quot...