The Python Workshop: Learn to code in Python and kickstart your career in software development or data science

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Table of Contents
Preface
Chapter 1: Vital Python – Math, Strings, Conditionals, and Loops
	Introduction
	Vital Python
	Numbers: Operations, Types, and Variables
		To Open a Jupyter Notebook
	Python as a Calculator
		Standard Math Operations
		Basic Math Operations
		Order of Operations
		Exercise 1: Getting to Know the Order of Operations
		Spacing in Python
		Number Types: Integers and Floats
		Exercise 2: Integer and Float Types
		Complex Number Types
		Errors in Python
		Variables
		Variable Assignment
		Exercise 3: Assigning Variables
		Changing Types
		Reassigning Variables in Terms of Themselves
		Activity 1: Assigning Values to Variables
		Variable Names
		Exercise 4: Variable Names
		Multiple Variables
		Exercise 5: Multiple Variables in Python
		Comments
		Exercise 6: Comments in Python
		Docstrings
		Activity 2: Finding a Solution Using the Pythagorean Theorem in Python
	Strings: Concatenation, Methods, and input()
		String Syntax
		Exercise 7: String Error Syntax
		Escape Sequences with Quotes
		Multi-Line Strings
		The print() Function
		Exercise 8: Displaying Strings
		String Operations and Concatenation
		Exercise 9: String Concatenation
	String Interpolation
		Comma Separators
		Format
		The len() Function
		String Methods
		Exercise 10: String Methods
		Casting
		Exercise 11: Types and Casting
		The input() Function
		Exercise 12: The input() Function
		Activity 3: Using the input() Function to Rate Your Day
	String Indexing and Slicing
		Indexing
	Slicing
		Strings and Their Methods
	Booleans and Conditionals
		Booleans
		Exercise 13: Boolean Variables
		Logical Operators
		Comparison Operators
		Exercise 14: Comparison Operators
		Comparing Strings
		Exercise 15: Comparing Strings
		Conditionals
		The if Syntax
		Indentation
		Exercise 16: Using the if Syntax
		if else
		Exercise 17: Using the if-else Syntax
		The elif Statement
	Loops
		The while Loops
		An Infinite Loop
		break
		Activity 4: Finding the Least Common Multiple (LCM)
		Programs
		Exercise 18: Calculating Perfect Squares
		Exercise 19: Real Estate Offer
		The for Loop
		Exercise 20: Using for Loops
		The continue Keyword
		Activity 5: Building Conversational Bots Using Python
	Summary
Chapter 2: Python Structures
	Introduction
	The Power of Lists
		Exercise 21: Working with Python Lists
		Matrices as Nested Lists
		Exercise 22: Using a Nested List to Store Data from a Matrix
		Activity 6: Using a Nested List to Store Employee Data
	Matrix Operations
		Exercise 23: Implementing Matrix Operations (Addition and Subtraction)
		Matrix Multiplication Operations
		Exercise 24: Implementing Matrix Operations (Multiplication)
	List Methods
		Exercise 25: Basic List Operations
		Accessing an Item from a List
		Exercise 26: Accessing an Item from Shopping List Data
		Adding an Item to a List
		Exercise 27: Adding Items to Our Shopping List
	Dictionary Keys and Values
		Exercise 28: Using a Dictionary to Store a Movie Record
		Activity 7: Storing Company Employee Table Data Using a List and a Dictionary
		Zipping and Unzipping Dictionaries Using zip()
		Exercise 29: Using the zip() Method to Manipulate Dictionaries
	Dictionary Methods
		Exercise 30: Accessing a Dictionary Using Dictionary Methods
	Tuples
		Exercise 31: Exploring Tuple Properties in Our Shopping List
	A Survey of Sets
		Exercise 32: Using Sets in Python
		Set Operations
		Exercise 33: Implementing Set Operations
	Choosing Types
	Summary
Chapter 3: Executing Python – Programs, Algorithms, and Functions
	Introduction
	Python Scripts and Modules
		Exercise 34: Writing and Executing Our First Script
		Exercise 35: Writing and Importing Our First Module
		Shebangs in Ubuntu
		Docstrings
		Exercise 36: Adding a Docstring to my_module.py
		Imports
		Exercise 37: Finding the System Date
		The if __name__ == "__main__" Statement
		Activity 8: What's the Time?
	Python Algorithms
		Exercise 38: The Maximum Number
		Time Complexity
		Time Complexity for the Maximum Number Algorithm
		Sorting Algorithms
		Exercise 39: Using Bubble Sort in Python
		Searching Algorithms
		Exercise 40: Linear Search in Python
		Exercise 41: Binary Search in Python
	Basic Functions
		Exercise 42: Defining and Calling the Function in Shell
		Exercise 43: Defining and Calling the Function in Python Script
		Exercise 44: Importing and Calling the Function from the Shell
		Positional Arguments
		Keyword Arguments
		Exercise 45: Defining the Function with Keyword Arguments
		Exercise 46: Defining the Function with Positional and Keyword Arguments
		Exercise 47: Using **kwargs
		Activity 9: Formatting Customer Names
	Iterative Functions
		Exercise 48: A Simple Function with a for Loop
		Exiting Early
		Exercise 49: Exiting the Function During the for Loop
		Activity 10: The Fibonacci Function with an Iteration
	Recursive Functions
		A Terminating Case
		Exercise 50: Recursive Countdown
		Exercise 51: Factorials with Iteration and Recursion
		Activity 11: The Fibonacci Function with Recursion
	Dynamic Programming
		Exercise 52: Summing Integers
		Timing Your Code
		Exercise 53: Timing Your Code
		Activity 12: The Fibonacci Function with Dynamic Programming
	Helper Functions
		Don't Repeat Yourself
		Exercise 54: Helper Currency Conversion
	Variable Scope
		Variables
		Defining inside versus outside a Function
		The Global Keyword
		The Nonlocal Keyword
	Lambda Functions
		Exercise 55: The First Item in a List
		Mapping with Lambda Functions
		Exercise 56: Mapping with a Logistic Transform
		Filtering with Lambda Functions
		Exercise 57: Using the Filter Lambda
		Sorting with Lambda Functions
	Summary
Chapter 4: Extending Python, Files, Errors, and Graphs
	Introduction
	Reading Files
		Exercise 58: Reading a Text File Using Python
		Exercise 59: Reading Partial Content from a Text File
	Writing Files
		Exercise 60: Creating and Writing Content to Files to Record the Date and Time in a Text File
	Preparing for Debugging (Defensive Code)
		Writing Assertions
		Exercise 61: Working with Incorrect Parameters to Find the Average Using Assert with Functions
	Plotting Techniques
		Exercise 62: Drawing a Scatter Plot to Study the Data between Ice Cream Sales versus Temperature
		Exercise 63: Drawing a Line Chart to Find the Growth in Stock Prices
		Exercise 64: Plotting Bar Plots to Grade Students
		Exercise 65: Creating a Pie Chart to Visualize the Number of Votes in a School
		Exercise 66: Generating a Heatmap to Visualize the Grades of Students
		Exercise 67: Generating a Density Plot to Visualize the Score of Students
		Exercise 68: Creating a Contour Plot
		Extending Graphs
		Exercise 69: Generating 3D plots to Plot a Sine Wave
	The Don'ts of Plotting Graphs
		Manipulating the Axis
		Cherry Picking Data
		Wrong Graph, Wrong Context
		Activity 13: Visualizing the Titanic Dataset Using a Pie Chart and Bar Plots
	Summary
Chapter 5: Constructing Python – Classes and Methods
	Introduction
	Classes and Objects
		Exercise 70: Exploring Strings
	Defining Classes
		Exercise 71: Creating a Pet Class
	The __init__ method
		Exercise 72: Creating a Circle Class
		Keyword Arguments
		Exercise 73: The Country Class with Keyword Arguments
	Methods
		Instance Methods
		Exercise 74: Adding an Instance Method to Our Pet Class
		Adding Arguments to Instance Methods
		Exercise 75: Computing the Size of Our Country
		The __str__ method
		Exercise 76: Adding an __str__ Method to the Country Class
		Static Methods
		Exercise 77: Refactoring Instance Methods Using a Static Method
		Class Methods
		Exercise 78: Extending Our Pet Class with Class Methods
	Properties
		The Property Decorator
		Exercise 79: The Full Name Property
		The Setter Method
		Exercise 80: Writing a Setter Method
		Validation via the Setter Method
	Inheritance
		The DRY Principle Revisited
		Single Inheritance
		Exercise 81: Inheriting from the Person Class
		Sub-Classing Classes from Python Packages
		Exercise 82: Sub-Classing the datetime.date Class
		Overriding Methods
		Calling the Parent Method with super()
		Exercise 83: Overriding Methods Using super()
		Multiple Inheritance
		Exercise 84: Creating a Consultation Appointment System
		Method Resolution Order
		Activity 14: Creating Classes and Inheriting from a Parent Class
	Summary
Chapter 6: The Standard Library
	Introduction
	The Importance of the Standard Library
		High-Level Modules
		Lower-Level Modules
		Knowing How to Navigate in the Standard Library
		Exercise 85: Using the dataclass Module
		Exercise 86: Extending the echo.py Example
	Dates and Times
		Exercise 87: Comparing datetime across Time Zones
		Exercise 88: Calculating the Time Delta between Two datetime Objects
		Exercise 89: Calculating the Unix Epoch Time
		Activity 15: Calculating the Time Elapsed to Run a Loop
	Interacting with the OS
		OS Information
		Exercise 90: Inspecting the Current Process Information
		Using pathlib
		Exercise 91: Using the glob Pattern to List Files within a Directory
		Listing All Hidden Files in Your Home Directory
	Using the subprocess Module
		Exercise 92: Customizing Child Processes with env vars
		Activity 16: Testing Python Code
	Logging
		Using Logging
		Logger Object
		Exercise 93: Using a logger Object
		Logging in warning, error, and fatal Categories
		Configuring the Logging Stack
		Exercise 94: Configuring the Logging Stack
	Collections
		Counters
		Exercise 95: Counting Words in a Text Document
		defaultdict
		Exercise 96: Refactoring Code with defaultdict
		ChainMap
	Functools
		Caching with functools.lru_cache
		Exercise 97: Using lru_cache to Speed Up Our Code
		Partial
		Exercise 98: Creating a print Function That Writes to stderr
		Activity 17: Using partial on class Methods
	Summary
Chapter 7: Becoming Pythonic
	Introduction
	Using List Comprehensions
		Exercise 99: Introducing List Comprehensions
		Exercise 100: Using Multiple Input Lists
		Activity 18: Building a Chess Tournament
	Set and Dictionary Comprehensions
		Exercise 101: Using Set Comprehensions
		Exercise 102: Using Dictionary Comprehensions
		Activity 19: Building a Scorecard Using Dictionary Comprehensions and Multiple Lists
	Default Dictionary
		Exercise 103: Adopting a Default Dict
	Iterators
		Exercise 104: The Simplest Iterator
		Exercise 105: A Custom Iterator
		Exercise 106: Controlling the Iteration
	Itertools
		Exercise 107: Using Infinite Sequences and takewhile
		Exercise 108: Turning a Finite Sequence into an Infinite One, and Back Again
	Generators
		Exercise 109: Generating a Sieve
		Activity 20: Using Random Numbers to Find the Value of Pi
	Regular Expressions
		Exercise 110: Matching Text with Regular Expressions
		Exercise 111: Using Regular Expressions to Replace Text
		Activity 21: Regular Expressions
	Summary
Chapter 8: Software Development
	Introduction
	Debugging
		Exercise 112: Debugging a Salary Calculator
		Activity 22: Debugging Sample Python Code for an Application
	Automated Testing
		Test Categorization
		Test Coverage
		Writing Tests in Python with Unit Testing
		Exercise 113: Checking Sample Code with Unit Testing
		Writing a Test with pytest
	Creating a PIP Package
		Exercise 114: Creating a Distribution That Includes Multiple Files within a Package
		Adding More Information to Your Package
	Creating Documentation the Easy Way
		Docstrings
		Using Sphinx
		Exercise 115: Documenting a Divisible Code File
		More Complex Documentation
	Source Management
		Repository
		Commit
		Staging Area
		Undoing Local Changes
		History
		Ignoring Files
		Exercise 116: Making a Change in CPython Using git
	Summary
Chapter 9: Practical Python – Advanced Topics
	Introduction
	Developing Collaboratively
		Exercise 117: Writing Python on GitHub as a Team
	Dependency Management
		Virtual Environments
		Exercise 118: Creating and Setting Up a conda Virtual Environment to Install numpy and pandas
		Saving and Sharing Virtual Environments
		Exercise 119: Sharing Environments between a conda Server and Your Local System
	Deploying Code into Production
		Exercise 120: Dockerizing Your Fizzbuzz Tool
	Multiprocessing
		Multiprocessing with execnet
		Exercise 121: Working with execnet to Execute a Simple Python Squaring Program
		Multiprocessing with the Multiprocessing Package
		Exercise 122: Using the Multiprocessing Package to Execute a Simple Python Program
		Multiprocessing with the Threading Package
		Exercise 123: Using the Threading Package
	Parsing Command-Line Arguments in Scripts
		Exercise 124: Introducing argparse to Accept Input from the User
		Positional Arguments
		Exercise 125: Using Positional Arguments to Accept Source and Destination Inputs from a User
	Performance and Profiling
		Changing Your Python
		PyPy
		Exercise 126: Using PyPy to Find the Time to Get a List of Prime Numbers
		Cython
		Exercise 127: Adopting Cython to Find the Time Taken to get a List of Prime Numbers
	Profiling
		Profiling with cProfile
		Activity 23: Generating a List of Random Numbers in a Python Virtual Environment
	Summary
Chapter 10: Data Analytics with pandas and NumPy
	Introduction
	NumPy and Basic Stats
		Exercise 128: Converting Lists to NumPy Arrays
		Exercise 129: Calculating the Mean of the Test Score
		Exercise 130: Finding the Median from a Collection of Income Data
		Skewed Data and Outliers
		Standard Deviation
		Exercise 131: Finding the Standard Deviation from Income Data
	Matrices
		Exercise 132: Matrices
		Computation Time for Large Matrices
		Exercise 133: Creating an Array to Implement NumPy Computations
	The pandas Library
		Exercise 134: Using DataFrames to Manipulate Stored Student testscore Data
		Exercise 135: DataFrame Computations with the Student testscore Data
		Exercise 136: Computing DataFrames within DataFrames
		New Rows and NaN
		Exercise 137: Concatenating and Finding the Mean with Null Values for Our testscore Data
		Cast Column Types
	Data
		Downloading Data
		Downloading the Boston Housing Data from GitHub
		Reading Data
		Exercise 138: Reading and Viewing the Boston Housing Dataset
		Exercise 139: Gaining Data Insights on the Boston Housing Dataset
	Null Values
		Exercise 140: Null Value Operations on the Dataset
		Replacing Null Values
	Visual Analysis
		The matplotlib Library
		Histograms
		Exercise 141: Creating a Histogram Using the Boston Housing Dataset
		Histogram Functions
		Scatter Plots
		Exercise 142: Creating a Scatter Plot for the Boston Housing Dataset
		Correlation
		Exercise 143: Correlation Values from the Dataset
		Regression
		Plotting a Regression Line
		StatsModel Regression Output
		Additional Models
		Exercise 144: Box Plots
		Violin Plots
		Activity 24: Data Analysis to Find the Outliers in Pay versus the Salary Report in the UK Statistics Dataset
	Summary
Chapter 11: Machine Learning
	Introduction
	Introduction to Linear Regression
		Simplify the Problem
		From One to N-Dimensions
		The Linear Regression Algorithm
		Exercise 145: Using Linear Regression to Predict the Accuracy of the Median Values of Our Dataset
		Linear Regression Function
	Cross-Validation
		Exercise 146: Using the cross_val_score Function to Get Accurate Results on the Dataset
	Regularization: Ridge and Lasso
	K-Nearest Neighbors, Decision Trees, and Random Forests
		K-Nearest Neighbors
		Exercise 147: Using K-Nearest Neighbors to Find the Median Value of the Dataset
		Exercise 148: K-Nearest Neighbors with GridSearchCV to Find the Optimal Number of Neighbors
		Decision Trees and Random Forests
		Exercise 149: Decision Trees and Random Forests
		Random Forest Hyperparameters
		Exercise 150: Random Forest Tuned to Improve the Prediction on Our Dataset
	Classification Models
		Exercise 151: Preparing the Pulsar Dataset and Checking for Null Values
		Logistic Regression
		Exercise 152: Using Logistic Regression to Predict Data Accuracy
		Other Classifiers
		Naive Bayes
		Exercise 153: Using GaussianNB, KneighborsClassifier, DecisionTreeClassifier, and RandomForestClassifier to Predict Accuracy in Our Dataset
		Confusion Matrix
		Exercise 154: Finding the Pulsar Percentage from the Dataset
		Exercise 155: Confusion Matrix and Classification Report for the Pulsar Dataset
	Boosting Methods
		Exercise 156: Using AdaBoost to Predict the Best Optimal Values
		Activity 25: Using Machine Learning to Predict Customer Return Rate Accuracy
	Summary
Appendix
Index