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

13.3 Histograms

A histogram is a form of bar chart for displaying statistical data. In Figure 5.12 in section 5.7.2, I showed a plot of one million random numbers in a normal distribution with mean μ = 1.0 and standard deviation σ = 0.2. This code creates a histogram with the same characteristics:

import matplotlib.pyplot as plt
import numpy as np



# generate the random numbers in a normal distribution
mu = 1.0
sigma = 0.2
random_numbers = np.random.normal(mu, sigma, 1000000)

# set the number of bins in which to divide up the random numbers
bin_size = 100

# draw the histogram
the_plot = plt.hist(random_numbers, bins=bin_size)
<Figure size 432x288 with 1 Axes>
A histogram of 1,000,000 random numbers in a normal distribution

The bins argument determines how many bars are in the histogram.

Exercise 13.13

Does decreasing bin_size make the histogram smoother or blockier?