nside these pages, you'll find a comprehensive step-by-step guide to the Python programming language, with clear explanations and simple code examples to help you fully understand each concept. We'll start with the basics, covering topics like data types, variables, and control structures, and then move on to more advanced concepts like object-oriented programming, data manipulation, and working with external libraries, as well as many other advanced concepts.
But that’s not all. We then go on to get applying your newfound advanced Python skills to solve some of the worlds greatest programming challenges, including:
Image processing
Sound processing
Video processing
Desktop application development
Web Development
Web Scraping
Data Analysis
Big data processing
Cloud computing
Machine learning
Natural language processing
Game development
Excel integration
Automation
Robotics
IoT
Virtual reality
These aren’t areas we will simply skip over. You will get the clear instruction and code examples you need to truly get started programming in these specialist applications of the Python language showing you’re the true power and diversity of the language itself.
Throughout the book, you'll also find practical examples and project ideas to help you re-enforce your newfound knowledge and build real-world enterprise-grade code. By the time you finish this book, you'll have advanced skills in Python programming and be well on your way to becoming a "Super Pythonista"!
Author(s): Charles Kyriakou
Publisher: Independently published
Year: 2022
Language: English
Pages: 324
Table of Contents
Introduction
Chapter 1: Introduction to programming and Python
What is programming?
What is Python?
Why learn Python?
Chapter 2: Setting up a development environment
Why do you need a development environment?
How to set up a development environment
How to set up a development environment in PyCharm
How to set up a development environment in VS Code
Chapter 3: Basic concepts and syntax of Python
Variables
Data types
Operators
Control statements
Chapter 4: Working with lists, tuples, and dictionaries
Lists and list comprehensions
Indexing and slicing lists
Modifying lists
Sorting lists
Tuples and tuple manipulation
Dictionaries and dictionary manipulation
Chapter 5: Working with data types in Python
Working with variables
Working with numbers
Working with strings
Working with lists
Chapter 6: Control structures in Python
Using if statements
Using for loops
Using while loops
Using try and except statements
Using with statements
Chapter 7: Working with files and data input/output
Reading and writing text files
Working with file paths and modes
Reading and writing files line by line
Working with CSV and JSON data
What is CSV data?
Working with CSV data in Python (using the csv module)
What is JSON data?
Working with JSON data in Python
Database connectivity (SQLite, MySQL, etc.)
What is a database?
Connecting to and querying a database in Python
Chapter 8: Writing and using functions in Python
Defining a function
Calling a function
Returning a value
Scope
Argument default values
Variable number of arguments
How to call a function in Python
Using keyword arguments in Python
Using lambda functions in Python
Chapter 9: Working with modules in Python
What are modules?
Importing specific names from a module
Renaming imported names
Importing all names from a module
Creating and using your own modules
Chapter 10: Object orientated programming (OOP)
What is object-oriented programming?
Defining and using classes
Creating and using objects
Inheritance and polymorphism
Chapter 11: Python libraries and frameworks
Introduction to libraries and frameworks
What are libraries and frameworks?
How to find and install libraries and frameworks
NumPy and Pandas for scientific computing and data analysis
What is NumPy?
What is Pandas?
Using NumPy and Pandas for data manipulation and analysis
Django for web development
What is Django?
Setting up a Django project
Creating a web application with Django
Other popular libraries and frameworks
What is TensorFlow?
What is Pygame?
Using TensorFlow and Pygame in Python
Chapter 12: Debugging and error handling in Python
Common error types and how to handle them
Using the built-in debugger (pdb)
Debugging with third-party tools such as PyCharm and pdb++
Handling exceptions with try-except blocks
Raising and handling custom exceptions
Debugging and error handling best practices
Chapter 13: Development Tools and Techniques
Virtual Environments for Managing Packages and Dependencies
Working with the Python Package Index (PyPI)
Creating and Distributing Python Packages
Using Continuous Integration and Deployment Tools
Performance Optimization and Profiling Techniques
Chapter 14: The Python Inspect Library
Inspecting Modules and Classes
Tips for Using the Inspect Library
Chapter 15: Python and Optimization
Linear Programming with Pulp and Pyomo
Nonlinear Optimization with Scipy
Global Optimization with DEAP and PyGMO
Constraint Programming with Gurobi and Pyomo
Advanced Optimization Techniques with Python
Chapter 16: Advanced Python concepts and techniques
Decorators and metaprogramming
Generators and iterators
Working Asynchronous programming with asyncio
Working with sets, queues, and stacks
Processing and manipulating data with Pandas
Working with databases and SQL
Web development with Flask
Building and deploying web applications
Regular expressions
Chapter 17: Python and Image Processing
Loading and Manipulating Images with Pillow and OpenCV
Filtering and Enhancing Images with Scikit-image
Extracting Features from Images with Scikit-image and OpenCV
Advanced Image Processing Techniques with Python
Chapter 18: Python and Audio Processing
Loading and Manipulating Audio Files with Librosa and PyAudio
Filtering and Enhancing Audio with Scikit-sound
Extracting Features from Audio with Librosa and Scikit-sound
Advanced Audio Processing Techniques with Python
Chapter 19: Python and Video Processing
Introduction to Video Processing with Python
Loading and Manipulating Video Files with OpenCV and MoviePy
Filtering and Enhancing Video with OpenCV and Scikit-video
Extracting Features from Video with OpenCV and Scikit-video
Advanced Video Processing Techniques with Python
Chapter 20: Python and Desktop Applications
Creating GUI Applications with PyGTK and PyQt
Integrating with External Libraries and APIs
Storing and Accessing Data in a Database
Packaging and Distributing a Desktop Application
Advanced Desktop Application Development
Chapter 21: Python and Web Development
Introduction to Web Development with Python
Building a Web Server with Flask
Working with Templates and Forms
Integrating a Database with a Web Application
Deploying a Web Application to a Hosting Provider
Advanced Web Development Techniques with Django
Building and Deploying a RESTful API with Flask-RESTful
Chapter 22: Python and web scraping
Introduction to web scraping with Python
Using Beautiful Soup to parse HTML and XML
Scraping dynamic websites with Selenium
Handling cookies, headers, and authentication
Scraping data from APIs and data streams
Storing and processing scraped data
Advanced web scraping techniques and best practices
Chapter 23: Python and Data Analysis
Working with Data Structures and Data Types in Python
Loading and Cleaning Data Using Pandas
Exploring and Visualizing Data with Matplotlib and Seaborn
Performing Statistical Analysis with SciPy
Working with Time Series Data
Predictive Modeling and Machine Learning with scikit-learn
Advanced Data Analysis Techniques with NumPy and Pandas
Chapter 24: Python and big data processing
Processing large datasets with Pandas and Dask
Distributed computing with PySpark
Integrating with Hadoop and other big data technologies
Advanced big data processing techniques with PySpark and Dask
Chapter 25: Python and Cloud Computing
Deploying Python Applications to the Cloud
Working with Cloud-Based Storage and Databases
Scaling and Optimizing Applications in the Cloud
Advanced Cloud Computing Techniques with Python
Chapter 26: Python and Machine Learning
Supervised Learning Algorithms
Linear Regression
Support Vector Machines (SVMs)
Decision Trees
Unsupervised Learning Algorithms
Clustering
Dimensionality Reduction
Deep Learning with TensorFlow and Keras
Evaluating and Optimizing Machine Learning Models
Working with Real-World Data Sets and Projects
Chapter 27: Python and natural language processing
Preprocessing and cleaning text data
Extracting features and creating a feature matrix
Classification and clustering of text data
Topic modeling and document summarization
Advanced natural language processing
Chapter 28: Python and game development
Creating simple games with Pygame
Handling user input and collision detection
Animating and rendering graphics
Creating levels and game mechanics
Integrating sound and music
Advanced game development techniques with Pygame
Chapter 29: Python and Excel integration
Reading and writing Excel files with Pandas and openpyxl
Accessing and manipulating Excel data with xlwings
Creating custom Excel functions (PyXLL)
Advanced Excel integration techniques with Python
Chapter 30: Python and automation
Automating tasks with the subprocess module
Controlling the mouse and keyboard with PyAutoGUI
Automating web browsing with Selenium
Integrating with external tools and platforms
Advanced automation techniques with Python
Chapter 31: Python and Robotics
Robot Hardware
Robot Software
Robot Applications
Advanced Robotics Techniques with Python
Machine Learning and Artificial Intelligence
Motion Planning and Control
Perception Tasks
Chapter 32: Python and IoT
Connecting to and interacting with IoT devices
Collecting and processing sensor data with Pandas and Dask
Visualizing and reporting on IoT data with Matplotlib and Plotly
Building and deploying IoT applications with Flask and AWS IoT
Advanced IoT techniques with Python
Chapter 33: Python and Virtual Reality
Creating Virtual Environments with PyOpenGL and PyVR
Rendering 3D Graphics with PyOpenGL and PyVR
Creating Interactive Experiences with PyVR and PyOpenVR
Integrating with VR Hardware and Platforms
Advanced Virtual Reality Techniques with Python
About the Author
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