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Аннотация
Cut through the noise and get real results with a step-by-step approach to learning Python 3.X programming.
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Оглавление
- Cover
- FM
- Copyright
- 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
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