This book presents an intuitive approach to the concepts of Python Programming for students. It is appropriate for courses generally known as “Introduction to Python Programming.” This book assists students in discovering the power of Python programming. The text has taken into account the reality that students taking “Introduction to Python Programming” course are likely to come from a variety of disciplines. In addition to Computer Science majors, there tend to be students from other majors like other engineering streams, physics, chemistry, biology, environmental science, geography, economics, psychology and business. This book takes you through step by step process of learning the Python programming language. Each line of the code is marked with numbers and is explained in detail. In this book all the names of variables, strings, lists, dictionaries, tuples, functions, methods and classes consist of several natural words and in the explanation part they are written in italics to indicate the readers that they are part of programming code and to distinguish them from normal words. This programming style of using readable natural names makes the reading of code lot easier and prevents programming errors. Whether your ultimate goal is to break into cutting-edge data science, build scalable web ecosystems, solve competitive programming riddles, or orchestrate complex automation scripts, this book provides the foundational architecture to get you there. It eliminates the gatekeeping, breaks down complex abstractions into universal truths, and instills the self-reliance required to fetch your own resources long after the final page is turned.
Michael Johnson is a distinguished software engineer, veteran computer science educator, and author with over two decades of experience building high-performance systems and leading enterprise-level digital transformations. Holding a Master of Science in Computer Science from Stanford University specialising in software architecture and artificial intelligence, Michael has dedicated his career to bridging the gap between complex engineering concepts and practical, real-world application. His technical expertise spans across distributed systems, cloud computing, and data science pipelines, with Python remaining the core foundation of his production development and architectural design for the past fifteen years. Throughout his career, Michael has served as a Principal Software Architect at leading Silicon Valley technology firms and has frequently been invited as a guest lecturer at top-tier universities. Known for his ability to deconstruct dense, intimidating code structures into elegant, intuitive logic, he has mentored thousands of aspiring developers, turning complete novices into proficient, industry-ready engineers. His book, Learning Python, represents the culmination of this lifelong commitment to educational excellence. Rather than simply listing syntax, Michael leverages his extensive academic background and deep industry insight to provide readers with a comprehensive, masterclass-level framework. His writing empowers learners to not only write Python code but to truly think like a programmer, making this text the definitive guide for anyone serious about mastering the language.
Preface Chapter 1. Introduction to Python History and Evolution of Python Features and Advantages of Python Python Versions and Implementation Setting Up Python Environment Python IDEs and Tools Writing Your First Python Program Python Syntax and Indentation Applications of Python Chapter 2. Python Basics Variables and Data Types Operators and Expressions Input and Output Functions Type Conversion and Casting Control Flow Statements (if, elif, else) Loops: for and while Break, Continue, and Pass Statements Error Handling Basics Chapter 3. Functions and Modules Defining and Calling Functions Function Arguments and Return Values Lambda Functions and Anonymous Functions Variable Scope and Lifetime Recursive Functions Python Modules and Packages Importing Modules and Namespaces Writing and Using Custom Modules Chapter 4. Data Structures in Python Lists and List Operations Tuples and Tuple Operations Sets and Set Operations Dictionaries and Dictionary Methods Comprehensions (List, Set, Dictionary) Strings and String Methods Nested and Complex Data Structures Choosing the Right Data Structure Chapter 5. Object-Oriented Programming in Python Classes and Objects Attributes and Methods Constructors and Destructors Inheritance and Multiple Inheritance Polymorphism and Encapsulation Abstract Classes and Interfaces Class and Static Methods Special Methods and Operator Overloading Chapter 6. File Handling and Exception Management File Operations: Open, Read, Write, Close File Modes and File Objects Working with Text and Binary Files Context Managers and with Statement Exception Handling: try, except Finally and Else Clauses Custom Exceptions Logging and Error Management Chapter 7. Python Libraries and Packages Overview of Standard Libraries Math, Random, and DateTime Modules NumPy for Numerical Computation Pandas for Data Analysis Matplotlib and Seaborn for Visualization Requests and JSON for Web Data Regular Expressions (re Module) Installing and Managing Packages with pip Chapter 8. Python for Web and Networking Introduction to Web Programming HTTP Requests with Python Working with APIs and JSON Data Web Scraping with BeautifulSoup Socket Programming Basics Client-Server Architecture Networking Libraries (socket, urllib, requests) Simple Web Application Projects Bibliography Index
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