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)
2
Part I: Getting to Know Python
10
PART II: Algorithms and Circuits
14
PART III: Advanced Features and Libraries
19
References
20
Other Books You May Enjoy
Appendices
Appendix C: The Complete UniPoly Class
Appendix D: The Complete Guitar Class Hierarchy
Appendix F: Production Notes

14.1 Statistics

This section introduces several core statistical functions for analyzing data. The data we use is the ages in 2021 of one hundred attendees at a fictional concert of 1990s music cover bands.

import numpy as np

ages = np.array(
    [40, 41, 39, 35, 42, 37, 45, 43, 42, 38, 39, 45, 37, 36,
     45, 41, 41, 31, 42, 40, 39, 38, 40, 39, 41, 46, 42, 44,
     46, 48, 45, 39, 46, 43, 35, 38, 43, 41, 36, 40, 34, 44,
     42, 44, 40, 49, 47, 51, 52, 45, 44, 47, 39, 38, 43, 39,
     45, 40, 36, 43, 38, 43, 32, 35, 36, 42, 40, 38, 37, 36,
     41, 41, 31, 39, 51, 38, 42, 36, 35, 36, 40, 40, 37, 43,
     39, 42, 44, 50, 39, 38, 37, 33, 52, 35, 44, 29, 42, 39,
     40, 42]
)

14.1.1 Means and medians

The mean is the average of the data values. We often write the mean as the Greek letter mu = μ, though you may also see “s.”

mean = np.mean(ages)
mean
40.62

You can also compute the mean directly by...