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
Other Books You May Enjoy
Appendix C: The Complete UniPoly Class
Appendix D: The Complete Guitar Class Hierarchy
Appendix F: Production Notes

3.8 Parallel traverse

Given the data about guitars from the beginning of this chapter:

brands = ["Fender", "Fender", "Gibson",
    "Gibson", "Ibanez"]
models = ["Stratocaster", "Telecaster",
    "Les Paul", "Flying V", "RG"]
years = [1954, 1950, 1952, 1958, 1987]

how can we print out a sentence like

The Fender Stratocaster was first introduced in 1954.

for each model?

We can use an index via range and do it with four nested for loops, but this is extremely slow and inefficient.

Exercise 3.24

Write code to accomplish this task using an index via range and four nested for loops. Why is it slow and inefficient?

Instead, we use zip to move through the four lists at the same time:

for brand, model, year in zip(brands, models, years):
    print(f"The {brand} {model} was first...