Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Dancing with Python
  • Toc
  • feedback
Dancing with Python

Dancing with Python

By : Robert S. Sutor
5 (7)
close
Dancing with Python

Dancing with Python

5 (7)
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)
close
close
Preface
Preface
Why did I write this book?
For whom did I write this book?
What does this book cover?
What conventions do I use in this book?
Download the example code files
Download the color images
Get in touch
Free Chapter
1
Chapter 1: Doing the Things That Coders Do
Chapter 1: Doing the Things That Coders Do
1.1 Data
1.2 Expressions
1.3 Functions
1.4 Libraries
1.5 Collections
1.6 Conditional processing
1.7 Loops
1.8 Exceptions
1.9 Records
1.10 Objects and classes
1.11 Qubits
1.12 Circuits
1.13 Summary
2
Part I: Getting to Know Python
Part I: Getting to Know Python
3
Chapter 2: Working with Expressions
Chapter 2: Working with Expressions
2.1 Numbers
2.2 Strings
2.3 Lists
2.4 Variables and assignment
2.5 True and False
2.6 Arithmetic
2.7 String operations
2.8 List operations
2.9 Printing
2.10 Conditionals
2.11 Loops
2.12 Functions
2.13 Summary
4
Chapter 3: Collecting Things Together
Chapter 3: Collecting Things Together
3.1 The big three
3.2 Lists
3.3 The joy of O(1)
3.4 Tuples
3.5 Comprehensions
3.6 What does “Pythonic” mean?
3.7 Nested comprehensions
3.8 Parallel traverse
3.9 Dictionaries
3.10 Sets
3.11 Summary
5
Chapter 4: Stringing You Along
Chapter 4: Stringing You Along
4.1 Single, double, and triple quotes
4.2 Testing for substrings
4.3 Accessing characters
4.4 Creating strings
4.5 Strings and iterations
4.6 Strings and slicing
4.7 String tests
4.8 Splitting and stripping
4.9 Summary
6
Chapter 5: Computing and Calculating
Chapter 5: Computing and Calculating
5.1 Using Python modules
5.2 Integers
5.3 Floating-point numbers
5.4 Rational numbers
5.5 Complex numbers
5.6 Symbolic computation
5.7 Random numbers
5.8 Quantum randomness
5.9 Summary
7
Chapter 6: Defining and Using Functions
Chapter 6: Defining and Using Functions
6.1 The basic form
6.2 Parameters and arguments
6.3 Naming conventions
6.4 Return values
6.5 Keyword arguments
6.6 Default argument values
6.7 Formatting conventions
6.8 Nested functions
6.9 Variable scope
6.10 Functions are objects
6.11 Anonymous functions
6.12 Recursion
6.13 Summary
8
Chapter 7: Organizing Objects into Classes
Chapter 7: Organizing Objects into Classes
7.1 Objects
7.2 Classes, methods, and variables
7.3 Object representation
7.4 Magic methods
7.5 Attributes and properties
7.6 Naming conventions and encapsulation
7.7 Commenting Python code
7.8 Documenting Python code
7.9 Enumerations
7.10 More polynomial magic
7.11 Class variables
7.12 Class and static methods
7.13 Inheritance
7.14 Iterators
7.15 Generators
7.16 Objects in collections
7.17 Creating modules
7.18 Summary
9
Chapter 8: Working with Files
Chapter 8: Working with Files
8.1 Paths and the file system
8.2 Moving around the file system
8.3 Creating and removing directories
8.4 Lists of files and folders
8.5 Names and locations
8.6 Types of files
8.7 Reading and writing files
8.8 Saving and restoring data
8.9 Summary
10
PART II: Algorithms and Circuits
PART II: Algorithms and Circuits
11
Chapter 9: Understanding Gates and Circuits
Chapter 9: Understanding Gates and Circuits
9.1 The software stack
9.2 Boolean operations and bit logic gates
9.3 Logic circuits
9.4 Simplifying bit expressions
9.5 Universality for bit gates
9.6 Quantum gates and operations
9.7 Quantum circuits
9.8 Universality for quantum gates
9.9 Summary
12
Chapter 10: Optimizing and Testing Your Code
Chapter 10: Optimizing and Testing Your Code
10.1 Testing your code
10.2 Timing how long your code takes to run
10.3 Optimizing your code
10.4 Looking for orphan code
10.5 Defining and using decorators
10.6 Summary
13
Chapter 11: Searching for the Quantum Improvement
Chapter 11: Searching for the Quantum Improvement
11.1 Classical searching
11.2 Quantum searching via Grover
11.3 Oracles
11.4 Inversion about the mean
11.5 Amplitude amplification
11.6 Searching over two qubits
11.7 Summary
14
PART III: Advanced Features and Libraries
PART III: Advanced Features and Libraries
15
Chapter 12: Searching and Changing Text
Chapter 12: Searching and Changing Text
12.1 Core string search and replace methods
12.2 Regular expressions
12.3 Introduction to Natural Language Processing
12.4 Summary
16
Chapter 13: Creating Plots and Charts
Chapter 13: Creating Plots and Charts
13.1 Function plots
13.2 Bar charts
13.3 Histograms
13.4 Pie charts
13.5 Scatter plots
13.6 Moving to three dimensions
13.7 Summary
17
Chapter 14: Analyzing Data
Chapter 14: Analyzing Data
14.1 Statistics
14.2 Cats and commas
14.3 pandas DataFrames
14.4 Data cleaning
14.5 Statistics with pandas
14.6 Converting categorical data
14.7 Cats by gender in each locality
14.8 Are all tortoiseshell cats female?
14.9 Cats in trees and circles
14.10 Summary
18
Chapter 15: Learning, Briefly
chevron up
Chapter 15: Learning, Briefly
15.1 What is machine learning?
15.2 Cats again
15.3 Feature scaling
15.4 Feature selection and reduction
15.5 Clustering
15.6 Classification
15.7 Linear regression
15.8 Concepts of neural networks
15.9 Quantum machine learning
15.10 Summary
19
References
References
20
Other Books You May Enjoy
Other Books You May Enjoy
21
Index
Index Formatting Examples
Appendices
Appendices
Appendix A: Tools
Appendix A: Tools
A.1 The operating system command line
A.2 Installing Python
A.3 Installing Python modules and packages
A.4 Installing a virtual environment
A.5 Installing the Python packages used in this book
A.6 The Python interpreter
A.7 IDLE
A.8 Visual Studio Code
A.9 Jupyter notebooks
A.10 Installing and setting up Qiskit
A.11 The IBM Quantum Composer and Lab
A.12 Linting
Appendix B: Staying Current
Appendix B: Staying Current
B.1 python.org
B.2 qiskit.org
B.3 Python expert sites
B.4 Asking questions and getting answers
Appendix C: The Complete UniPoly Class
Appendix C: The Complete UniPoly Class
Appendix D: The Complete Guitar Class Hierarchy
Appendix D: The Complete Guitar Class Hierarchy
Appendix E: Notices
Appendix E: Notices
E.1 Photos, images, and diagrams
E.2 Data
E.3 Trademarks
E.4 Python 3 license
Appendix F: Production Notes
Appendix F: Production Notes

15.5 Clustering

Suppose I have a CSV dataset containing 75 (x, y) geometric coordinates. I load these into the xy_df pandas DataFrame and look at its descriptive statistical summary:

xy_df = pd.read_csv("src/examples/clustering-xy.csv")
xy_df.describe()

            x       y
count 75.0000 75.0000
mean   7.5733  4.5401
std    4.0102  2.1265
min    1.9796  1.1947
25%    3.4182  2.8896
50%    7.0173  3.6819
75%   12.2170  6.9615
max   13.5643  8.2785

Here is the usual sample of the first five points:

xy_df.head()

        x      y
0 13.4832 3.2657
1  7.6388 7.0170
2  2.9279 2.9603
3  7.4514 6.4439
4  3.3011 2.4642

How are these points spread out geometrically? Are they uniformly distributed within their minimum and maximum ranges?

A scatter plot would help us see the distribution because we are in two dimensions, but let’s try to collect or cluster the points into k groups first. Here, k is...

End of Section 6
Next Section
bookmark search playlist download
font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete