Top 12 Python Books for Beginners & Experienced Programmers

A Concise Introduction to Programming in Python by Mark J. Johnson. Whether for a classroom or self-study purposes, this book is written to provide readers with a hands-on introduction to Python code. Designed for those with no prior programming experience, this text breaks down the core principles and key concepts to understanding Python language. It also offers accessible examples, Python exercises and Python projects to show how Python can be used for finance, biology, image processing and textual analysis. For those who want to dive deeper into this programming language, the book also includes optional additional related topics to explore.

A Functional Start to Computing with Python by Ted Herman. This book is a Python essential reference for new students. It enables readers to learn computing without the use of “loops, variables, and object abstractions at the start.” Get started almost immediately by defining new functions and learning the most important concepts of computing, from software engineering to advance functional programming ideas. The book then builds on this knowledge to introduce more complex ideas to help students build their knowledge and understand assignment syntax and iteration. It also comes with access to a supplementary website with chapter-by-chapter guides and tools.

Discovering Computer Science by Jessen Havill. Written for computer science newcomers, this introductory book is a great resource for computational problem-solving and programming in Python. Each section of this book presents a key authentic problem from the natural and social sciences and digital humanities to show students how to find effective Python solutions. As the book progresses, the problems become more complex and require more computer science principles, such as abstraction, efficiency, testing and algorithmic techniques.

Making Music with Computer by Bill Manaris and Andrew R. Brown. Learning Python isn’t limited to engineering and computer science professionals — it can also be a key tool for many music creators. As computers have evolved, music students can now use Python code to show their audio creativity through computer science. Each section of this book uses music-creation activities to showcase fundamental Python programming concepts. This includes data types, algorithms, operators, iterations, lists, functions and classes.

Programming Language Explorations by Ray Toal, Rachel Rivera, Alexander Schneider and Eileen Choe. This book not only builds reader knowledge of Python but also of 11 other modern programming languages that are popularly used today. It contextualizes how these programming languages exist with one another conceptually, as well as how they can be used for different coding applications. Each programming language section offers readers example programs and an overview of basic elements, type systems, functional forms, scoping rules and concurrency patterns. They also include open source projects, more materials and exercises to hone practical computer science skills.

Research Software Engineering with Python by Damien Irving, Kate Hertweck, Luke Johnston, Joel Ostblom, Charlotte Wickham and Greg Wilson. Python is an essential tool for research in science. Specifically understanding intermediate Python concepts, such as how to use code for data analysis, is an in-demand skill set for many researchers. Each author dissects a practical experience where scientists needed software skills to create trustworthy research. Although designed for graduate-level research, this book is written for students looking to build from a basic knowledge of programming language.

A Tour of Data Science: Learn R and Python in Parallel by Nailong Zhang. There are two popular programming languages in computer science: Python and R. If you’re a student of data science, this is the perfect resource to learn about both. This book explores each type of code and contextualizes them for programming, statistics, optimization and machine learning applications. Written specifically from a data science perspective, this book provides a concentrated exploration of the two languages to build on the reader’s prior knowledge.

Data Science and Analytics with Python by Jesus Rogel-Salazar. Ready to transition from student to practitioner in data science and data analytics? This is the ideal textbook for you. It analyzes important tools that were developed using Python, such as SciKit-learn, Pandas and Numpy. Every chapter can be studied and applied independently, so the book can be used as reference material in practice. The first half of the book covers important Python features, such as defining a Python primer, pattern recognition and artificial intelligence. From there the book delves into regression analysis, clustering techniques, classification algorithms and more.

Modeling and Simulation in Python by Jason M. Kinser. Students with a working knowledge of Python code can use this book to take their coding skills to the next level. It explores how creating tools and data structure with Python can sometimes lead to mistakes, which require problem-solving algorithmic thinking skills. This book introduces readers to different computational environments that can use Python languages, such as movie databases, Chess games and even Shakespeare.

Exercises in Programming Style by Cristina Videira Lopes. People don't become Python programmers overnight. Coding in fluent Python takes practice. This book, honored as an ACM Notable Book, shows programmers the various ways you can write programs and design data systems using Python. Specifically, it demonstrates 40 different styles for writing the term frequency tasks based on ten categories (historical, basic, function composition, objects and object interactions, reflection and metaprogramming, adversity, data-centric, concurrency, interactivity and neural networks). It’s the perfect practice book for advanced students in computer science and software engineering programs.

Advanced Data Science and Analytics with Python by Jesus Rogel-Salazar. Even practicing data scientists need to regularly develop and apply their skills to stay on top of current computer programming expectations. This advanced Python reference book covers important data science tools developed in Python, such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and more. It breaks down the practical use of each tool so any programmer can use the book when needed for a practical perspective rather than as a sequential learning guide. Other in-depth topics included in this book are time series analysis, natural language processing, topic modeling, social network analysis, neural networks and deep learning.

Python Packages by Tomas Beuzen and Tiffany Timbers. This book is designed for readers who want to learn comprehensively about Python packaging. Although written for experienced Python users, this resource begins at an introductory level and then leads into advanced Python programming topics. Through the chapters, the authors showcase the full Python packaging life cycle, from creation to maintenance. Read in-depth examples of automated testing, creating documentation, versioning and updating a package and implementing continuous integration and deployment.