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  • Introduction to Time Series with Python (Sadrach Pierre)

    Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts. Perform time series analysis and forecasting confidently with Python.

  • Data Analysis with Python (Numpy, Matplotlib and Pandas)

    Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide, using Python. Equipped with the skills to prepare data for analysis and create meaningful data visualizations for forecasting values from data.

  • Data Visualization in Python (Daniel Nelson)

    This is a book for beginner to intermediate Python developers and will guide you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, as well as experimental libraries like Altair.

  • Introduction to Python for Finance (Trenton McKinney)

    Unlock the full potential of Python in the world of finance. This comprehensive guide is your gateway to mastering the powerful capabilities of Python to revolutionize financial analysis and investment strategies.

  • Modeling Neural Circuits Made Simple with Python

    An accessible undergraduate textbook in Computational Neuroscience that provides an introduction to the mathematical and computational modeling of neurons and networks of neurons in Python. Build a foundation for modeling Neural Circuits.

  • Learning Statistics with Python (Ethan Weed)

    This book explains basic concepts of statistics within the framework of using Python. The blending of statistics and computer coding has quickly become a standard in research to in both academia and industry.

  • An Introduction to R and Python for Data Analysis

    This book helps teach students to code in both R and Python simultaneously. The book is written in an engaging, collaborative style that makes it enjoyable to read. It maintains its formality without creating a barrier between the reader and the content.

  • Introduction to Computer Programming with Python (Harris Wang)

    This introduction to computer programming with Python begins with some of the basics of computing and programming before diving into the fundamental elements and building blocks of computer programs in Python language.

  • Introduction to Python Programming (Udayan Das, et al.)

    This book provides a comprehensive foundation in programming concepts and skills, teaches basic programming concepts, problem-solving skills, and the Python language using hands-on activities.

  • Python Programming for Economics and Finance

    Looking to enhance your skills in Economics and Finance? Dive into Python programming! With libraries like Pandas, NumPy, and Matplotlib, you can analyze data, build models, and visualize trends like never before.

  • Using Python for Introductory Econometrics (Florian Heiss, et al.)

    This book introduces the popular, powerful and free programming language and software package Python, focuses on implementation of standard tools and methods used in econometrics.

  • Python for Econometrics, Statistics, and Data Analysis

    This book is designed for someone new to statistical computing wishing to develop a set of skills necessary to perform original research for econometrics, statistics or general numerical analysis using Python.

  • Python for Data Analysis: Pandas, NumPy, and Jupyter

    The focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.

  • Automate the Boring Stuff with Python (Albert Sweigart)

    Learn how to use Python to write programs that do in minutes what would take you hours to do by hand - no prior programming experience required. You'll create Python programs that effortlessly perform useful and impressive feats of automation.

  • Python for Everybody: Exploring Data in Python 3

    This book is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.

  • Deep Learning with Python, 2nd Edition (Francois Chollet)

    This book introduces the field of deep learning using Python and the powerful Keras library. It offers insights for both novice and experienced machine learning practitioners, and builds your understanding through intuitive explanations and practical examples.

  • Machine Learning with Python Tutorial (Bernd Klein)

    This practical guide provides helps to solve machine learning challenges you may encounter in your work. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications.

  • Statistics and Machine Learning in Python (Edouard Duchesnay)

    Illustrates the fundamental concepts that link statistics and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.

  • Linear Algebra with Python (Sean Fitzpatrick)

    This textbook is for those who want to learn linear algebra from the basics. Python is used throughout the book to explain linear algebra. Learning with Python interactively, readers will naturally become accustomed to Python coding.

  • Beyond the Basic Stuff with Python: Writing Clean Code

    More than a mere collection of advanced syntax and masterful tips for writing clean code, advance your Python programming skills by using the command line and other professional tools like code formatters, type checkers, linters, and version control.

  • Python Packages (Tomas Beuzen, et al.)

    An open source book that describes modern and efficient workflows for creating Python packages. Covering the entire Python packaging life cycle, this essential guide takes readers from package creation all the way to effective maintenance and updating.

  • Inside The Python Virtual Machine (Obi Ike-Nwosu)

    This book describes how Python code is compiled and run, how the language itself can be modified and will demystify the mysterious bytecodes that run on the Python virtual machine.

  • Architecture Patterns with Python (Harry Percival, et al.)

    Enabling Test-Driven Development, Domain-Driven Design, and Event-Driven Microservices, it introduces proven architectural design patterns to help Python developers manage application complexity, and get the most value out of their test suites.

  • Clean Architectures in Python: Better Software Design

    The clean architecture is the opposite of spaghetti code, where everything is interlaced and there are no single elements that can be easily detached from the rest and replaced without the whole system collapsing.

  • Clean Code in Python: Refactor Your Legacy Code Base

    The book describes the basic elements of writing clean code and how it plays an important role in Python programming. You will learn about writing efficient and readable code using the Python standard library and best practices for software design.

  • Problem Solving with Algorithms/Data Structures using Python

    This is a textbook about computer science. It is also about Python. However, there is much more. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

  • Fundamentals of Python Programming (Richard L. Halterman)

    It focuses on introducing programming techniques and developing good habits. To that end, our approach avoids some of the more esoteric features of Python and concentrates on the programming basics that transfer directly to other imperative programming.

  • O'Reilly® Think Python, 2nd Edition (Allen B. Downey)

    This hands-on guide takes you through the Python programming language a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. 2nd edition updated for Python 3.

  • The Little Book of Algorithms in Python (William Lau)

    This workbook is designed to help those learning and teaching Computer Science at secondary school level. The aim of the book is to help students build fluency in their Python programming.

  • Data Structures and Algorithms with OPP Design Patterns in Python

    It promotes object-oriented design using Python and illustrates the use of the latest object-oriented design patterns. Virtually all the data structures are discussed in the context of a single class hierarchy.

  • Learn Python the Right Way: How to Think like a Computer Scientist

    The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science.

  • The Big Book of Small Python Projects: 81 Easy Practice Programs

    This book demonstrates how to combine different libraries and frameworks to build amazing things. It picks up where the complete beginner books leave off, expanding on existing concepts and introducing new tools that you'll use every day.

  • Practical Python Projects (Yasoob Khalid)

    This collection of 81 Python projects will have you making digital art, games, animations, counting programs, and more right away. Once you see how the code works, you’ll practice re-creating the programs and experiment by adding your own custom touches.

  • Python Programming Exercises, Gently Explained (Al Sweigart)

    This is the perfect book for beginner and intermediate programmers who want to test their Python skills but aren’t ready to begin professional-level software development. The 42 programming exercises in this book let you practice what you've learned.

  • Functional Programming in Python (David Mertz)

    It describes ways to avoid Python’s imperative-style flow control, the nuances of callable functions, how to work lazily with iterators, and the use of higher-order functions. He also lists several third-party Python libraries useful for functional programming.

  • Introduction to Statistical Learning: with Applications in Python

    This book covers the same materials as Introduction to Statistical Learning: with Applications in R (ISLR) but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

  • Python 3 Patterns, Recipes and Idioms (Bruce Eckel, et al)

    This book is aimed at more experienced Python programmers who are looking to deepen their understanding of the language and modern programming idioms. It focuses on some of the more advanced techniques used by libraries, frameworks, and applications.

  • The Recursive Book of Recursion using Python (Al Sweigart)

    Recursion has an intimidating reputation. This book uses Python and JavaScript examples to teach the basics of recursion, exposing the ways that it's often poorly taught and clarifying the fundamental principles of all recursive algorithms.

  • O'Reilly® Python Data Science Handbook: Essential Tools

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

  • Modeling and Simulation in Python (Allen B. Downey)

    This book is an introduction to physical modeling using a computational approach with Python. You will learn how to use Python to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; etc.

  • Introduction to Scientific Programming with Python

    This book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, assuming little or no prior experience in programming.

  • Python for Network Engineers (Natasha Samoylenko)

    Everything in the book is focused on network equipment and interaction with it, using the Python programming language. This immediately makes it possible to use the knowledge gained in the daily work of network engineers.

  • Programming for Computations - Python 3 Edition

    This book outlines the shortest possible path from no previous experience with programming to a set of skills that allows students to write simple programs for solving common mathematical problems with numerical methods in the context.

  • First Semester in Numerical Analysis with Python (Yaning Liu)

    This book introduces students to Numerical Methods using Python for the implementation of the algorithms. Discusses several common applications of Numerical Analysis and implementation using real world examples and hands on programming exercises.

  • Computational Physics with Python (Eric Ayars)

    This book provides an unusually broad survey of the topics of modern computational physics. Its philosophy is rooted in learning by doing, with new scientific materials as well as with the Python programming language.

  • Python Design Patterns (Brandon Rhodes)

    Understand the structural, creational, and behavioral Python design patterns - this book will help you learn the core concepts of design patterns and the way they can be used to resolve software design problems using Python.

  • O'Reilly® 20 Python Libraries You Aren't Using (But Should)

    This book helps you explore some of the lesser known Python libraries and tools, including third-party modules and several extremely useful tools in the standard library that deserve more attention.

  • O'Reilly® Think Bayes: Bayesian Statistics in Python

    If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.

  • Regression Analysis using Python (Eric Marsden)

    Become competent at implementing Regression Analysis in Python Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer.

  • Mathematical Python (Patrick Walls)

    This book is an introduction to mathematical computing including basic Python programming, scientific computing with NumPy, SciPy and Matplotlib, applications in calculus, linear algebra and differential equations.

  • How To Code in Python 3 (Lisa Tagliaferri)

    This book is designed to bring developers and others who are anxious to learn Python up to speed quickly. You will learn warts, gotchas, best practices and hints that have been gleaned through the years in days.

  • Python Tutorial (Guido van Rossum)

    This tutorial introduces the reader informally to the basic concepts and features of the Python language and system. This fast-paced, thorough introduction will have you writing programs, solving problems, and making things that work in no time.

  • The Python Handbook (Flavio Copes)

    Whether you have never written a line of code or you have some programming experience, this book is the right choice because it will inevitably put you in front of new, high-paying job opportunities, ...

  • How to Think Like a Computer Scientist: Learning with Python 3

    This book is an introduction to computer science using the Python programming language. It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging.

  • The Coder's Apprentice: Learning Programming with Python 3

    This book is aimed at teaching Python 3 to students and teenagers who are completely new to programming, assumes no previous knowledge of programming on the part of the students, and contains numerous exercises to train their programming skills.

  • Code Like a Pythonista: Idiomatic Python (David Goodger)

    This book is a thorough introduction to every feature of the Python language for programmers who are impatient to write production code. You'll dive deep into idiomatic Python patterns so you can write professional Python programs in no time.

  • Learn More Python 3 The Hard Way (Zed A. Shaw)

    This book of 52 hands-on projects is perfect for everyone who's written Python code but isn't yet comfortable taking new ideas all the way to finished software. Each project helps you build a key practical skill.

  • Create Graphical User Interfaces with Python (Laura Sach, et al)

    This book is for everyone, from beginners to experienced Python programmers who want to explore graphical user interfaces (GUIs). There are ten fun projects for you to create, including a painting program, an emoji match game, and a stop-motion animation creator.

  • Foundations of Robotics: Approach with Python and ROS

    This book introduces key concepts in robotics in an easy to understand language using an engaging project-based approach. It covers contemporary topics in robotics, providing an accessible entry point to fundamentals in all the major domains.

  • O'Reilly® Think DSP: Digital Signal Processing in Python

    This book is an introduction to signal processing and system analysis using a computational approach with Python as the programming language. It develops the important ideas incrementally, with a focus on applications.

  • PySDR: A Guide to SDR and DSP using Python (Marc Lichtman)

    This textbook acts as a hands-on introduction to the areas of Digital Signal Processing (DSP), Software-Defined Radio (SDR), and wireless communications. Think of this textbook like a gateway into the world of DSP and SDR.

  • Kalman and Bayesian Filters in Python (Roger R Labbe Jr.)

    This book is an introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your browser. What better way to learn?

  • Python for Geospatial Analysis (Ujaval Gandhi)

    Suitable for GIS practitioners with no programming background or python knowledge. The course will introduce basic Python programming concepts, libraries for spatial analysis, geospatial APIs and techniques for building spatial data processing pipelines.

  • Introduction to Python for Geographic Data Analysis

    Introduce the basics of Python programming and geographic data analysis for all “geo-minded” people (geographers, geologists and others using spatial data). Apply Python GIS geospatial processes to a variety of problems, and work with remote sensing data.

  • Python Scripting for Spatial Data Processing (Pete Bunting, et al)

    This book is a Python tutorial for beginners aiming at teaching spatial data processing. It is used as part of the courses taught in Remote Sensing and GIS, using psycopg2, and ogr2ogr, etc., at Aberystwyth University, UK.

  • Geographic Data Science with Python (Sergio Rey, et al.)

    Provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data, by using geographical and computational reasoning to unlock new insights hidden within data.

  • Learning Python (Fabrizio Romano)

    The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code.

  • Mining Social Media using Python: Finding Stories in Data

    This book shows you how to use Python and key data analysis tools to find the stories buried in social media. Perform advanced data analysis using Python, Jupyter Notebooks, and the pandas library.

  • Python Machine Learning Projects (Brian Boucheron, et al)

    This book tries to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning. If you know some Python and you want to use machine learning and deep learning, pick up this book.

  • Deep Learning with PyTorch (Eli Stevens, et al)

    This book teaches you to create deep learning and neural network systems with PyTorch. It gets you to work right away building a tumor image classifier from scratch. You'll learn best practices for the entire deep learning pipeline, tackling advanced projects.

  • Machine Learning with TensorFlow (Nishant Shukla)

    This book gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms.

  • Elements of Data Science using Python (Allen B. Downey)

    This book is an introduction to data science for people with no programming experience. The goal is to present a small, powerful subset of Python that allows you to do real work in data science as quickly as possible.

  • Biopython Tutorial and Cookbook (Jeff Chang, et al)

    Biopython is a set of freely available tools for biological computation written in Python. This book provides information to get you started with Biopython, in addition to specific documentation on a number of modules.

  • Building Skills in Object-Oriented Design in Python

    This book will help you build Object-Oriented design skills through the creation of a moderately complex family of games. It is a step-by-step guide to OO design and implementation for developers who want to use Python to create efficient programs.

  • Program Arcade Games: With Python and Pygame (Paul Craven)

    Learn and use Python and PyGame to design and build cool arcade games. After reading and using this book, you'll be able to learn to program and build simple arcade game applications using one of today's most popular programming languages, Python.

  • Coding Games With Pygame Zero and Python (Richard Smith)

    Teach pro-gramming using action games used to make learning more interesting. Some of the examples are entirely focused on introducing new language concepts or showing how the Pygame Zero API works, but most are a mixture of both.

  • Code the Classics – Volume 1: Using Python and Pygame

    This book not only tells the stories of some of the seminal video games of the 1970s and 1980s, but shows you how to create your own games inspired by them using Python and Pygame Zero, following examples programmed by Raspberry Pi founder Eben Upton.

  • Learning Computing with Robots using Python (Deepak Kumar)

    This book will introduce you to the world of computers, robots, and computing. You will learn that computing is no more about computers than astronomy is about telescopes. Robots have been in existence much longer than computers.

  • Classic Computer Science Problems in Python (David Kopec)

    This book deepens your knowledge of problem-solving techniques from the realm of computer science by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more.

  • CS for All: An Introduction to Computer Science using Python

    To provide an introduction to computer science as an intellectually rich and vibrant field rather than focusing exclusively on computer programming. It emphasizes concepts and problem-solving over syntax and programming language features.

  • Practices of the Python Pro (Dane Hillard)

    You'll learn to design professional-level, clean, easily maintainable software at scale using the incredibly popular programming language, Python. You'll find easy-to-grok examples and instantly useful techniques that will help you code like a pro.

  • IPython Interactive Computing and Visualization Cookbook

    This book contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code.

  • A Whirlwind Tour of Python (Jake VanderPlas)

    This book a fast-paced introduction to essential features of the Python language, aimed at researchers and developers who are already familiar with programming in another language, particularly for using Python for data science and/or scientific programming.

  • Learn Python, Break Python: A Beginner's Guide to Programming

    This book is a hands-on introduction to the Python programming language, written for people who have no experience with programming whatsoever. You don't need to worry. Learning how to program a computer is far from impossible.

  • Annotated Algorithms in Python: with Applications

    This book covers Analysis and Design of Algorithms, Scientific Computing, Monte Carlo Simulations, and Parallel Algorithms. It teaches the core knowledge required by any scientist interested in numerical algorithms and computational finance.

  • O'Reilly® Test-Driven Development with Python (Harry Percival)

    By taking you through the development of a real web application from beginning to end, this hands-on guide demonstrates the practical advantages of test-driven development (TDD) with Python. You'll learn how to write and run tests BEFORE building your apps.

  • Non-Programmer's Tutorial for Python 3 (Josh Cogliati, et al)

    This book is a tutorial designed to be a introduction to the Python programming language. This guide is for someone with no programming experience. There are a lots of code inside this guide. You should type in code that to see what happens.

  • The Hitchhiker's Guide to Python: Best Practices for Development

    This guide describes best practices currently used by package and application developers. Unlike other books for this audience, It is light on reusable code and heavier on design philosophy, directing the reader to excellent sources that already exist.

  • Cracking Codes with Python: Building and Breaking Ciphers

    Learn how to program in Python while making and breaking ciphers - algorithms used to create and send secret messages! You'll begin with simple programs for the reverse and Caesar ciphers and then work your way up to public key cryptography, etc.

  • Open Workbook of Cryptology: Python Projects

    This book uses Python and some standard cryptographic libraries in Python to explore these cryptological ideas. It should be accessible to students with a solid basic comfort level with Python – but could also be used as a way to solidify Python.

  • Python and Coding Theory (David Joyner)

    This is the lecture notes for a course on Python and coding theory designed for students who have little or no programmig experience. You will learn some of the Python computer programming language and selected topics in coding theory.

  • Hands-on Python Tutorial (Dr. Andrew N. Harrington)

    This book provides a concise, step-by-step guide to Python programming for beginners. A lot of examples, illustrations, end of chapter summary and practice exercises (with solutions) are provided to help the reader learn faster, remember longer, etc.

  • Essential Python (Krzysztof Kowalczyk)

    This book provides clear and concise explanation of topics for programmers both starting to learn the Python programming language as well as those diving in more complex topics. Examples are linked to online playground that allows you to play with them.

  • O'Reilly® Python Web Frameworks (Carlos De La Guardia)

    This book describes Python web frameworks ranging from full-stack options that offer a lot of functionality to micro frameworks that focus on simplicity with fewer features. Learn how to choose a framework that best fits your development needs.

  • Hadoop with Python (Zachary Radtka, et al)

    This book takes you through the basic concepts behind Hadoop, MapReduce, Pig, and Spark. Then, through multiple examples and use cases, you'll learn how to work with these technologies by applying various Python tools.

  • Python Notes for Professionals (Stack Overflow Contributors)

    This book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow. Text content is released under Creative Commons BY-SA. See credits at the end of this book whom contributed to the various chapters.

  • How to Make Mistakes in Python? (Mike Pirnat)

    Even the best programmers make mistakes. Some have been simple and silly; others were embarrassing and downright costly. The author dissects some of his most memorable blunders, peeling them back layer-by-layer to reveal just what went wrong.

  • Fast Lane to Python (Norm Matloff)

    This book aims to enable the reader to quickly acquire a Python foundation. The material particularly feel quite comfortable to anyone with background in an object-oriented programming (OOP) language such as C++ or Java.

  • A Beginner's Python Tutorial (Wikibooks)

    If you are new to programming with Python and are looking for a solid introduction, this is the book for you - a comprehensive and easy-to-read introduction to Python programming includes a wealth of programming tutorials for writing your first lines of code.

  • Programming for Computations - Python (Svein Linge, et al)

    This book presents computer programming as a key method for solving mathematical problems using Python. Each treated concept is illustrated and explained in detail by means of working examples. It is intended for novice programmers and engineers.

  • Solving Ordinary Differential Equations in Python (Joakim Sundnes)

    This open access book explains the foundations of modern solvers for ordinary differential equations (ODEs). Explicit and implicit methods are motivated and explained, and all the solvers are implemented as a class hierarchy in Python.

  • Solving PDEs in Python: The FEniCS Tutorial I (H. Langtangen)

    This book offers a concise and gentle introduction to finite element programming in Python based on the popular FEniCS software library. Using a series of examples, it guides readers through the essential steps to quickly solving a PDE in FEniCS.

  • Developing Graphics Frameworks with Python and OpenGL

    It shows you how to create software for rendering complete three-dimensional scenes, explains the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive worlds.

  • Python and OpenGL for Scientific Visualization (Nicolas P. Rougier)

    The goal of this book is to reconcile Python programmers with OpenGL, providing both an introduction to modern OpenGL and a set of basic and advanced techniques in order to achieve both fast, scalable & beautiful scientific visualizations.

  • Scientific Visualisation: Python and Matplotlib (Nicolas P. Rougier)

    Matplotlib provides a large library of customizable plots, along with a comprehensive set of backends. Through practical, hands-on and straightforward examples, the book guides you through Data Visualization and Exploration using Python and Matplotlib.

  • From Python to NumPy (Nicolas P. Rougier)

    NumPy is one of the most important scientific computing libraries available for Python. This book teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts.

  • Guide to NumPy (Travis E. Oliphant)

    This book is for programmers, scientists, or engineers, who have basic Python knowledge and would like to be able to do numerical computations with Python. It will give you a solid foundation in NumPy arrays and universal functions.

  • NumPy Tutorials (Usman Malik, Anne Bonner, et al)

    They provide everything you need to know to get started with NumPy. They also explain the basics of NumPy such as its architecture and environment, discusses the various array functions, types of indexing, etc. With examples for better understanding.

  • Scipy Lecture Notes (Emmanuelle Gouillart, et al)

    This book is the teaching material on the scientific Python ecosystem, a quick introduction to central tools and techniques. It is for programmers from beginner to expert. Work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries.

  • SciPy Programming Succinctly (James McCaffrey)

    This book offers readers a quick, thorough grounding in knowledge of the Python open source extension SciPy. The SciPy library, accompanied by its interdependent NumPy, offers Python programmers advanced functions that work with arrays and matrices.

  • Bayesian Methods for Hackers: Using Python and PyMC

    This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation - no advanced mathematics required.

  • O'Reilly® Picking a Python Version: A Manifesto (David Mertz)

    This report guides you through the implicit decision tree of choosing what Python version, implementation, and distribution is best suited for you. There are two major versions: the Python 2.x series, and the newer Python 3.x series.

  • Dive Into Python 3 (Mark Pilgrim)

    This book is a hands-on guide to Python 3 (the latest version of the Python language) and its differences from Python 2. Each chapter starts with a real, complete code sample, proceeds to pick it apart and explain the pieces, and then puts it all back together in a summary at the end.

  • Porting to Python 3: An In-depth Guide (Lennart Regebro)

    Porting to Python 3 doesn't have to be daunting. This book guides you through the process of porting your Python 2 code to Python 3, from choosing a porting strategy to solving your distribution issues.

  • Python Scripting for Spatial Data Processing (Pete Bunting, et al)

    This book is a Python tutorial for beginners aiming at teaching spatial data processing. It is used as part of the courses taught in Remote Sensing and GIS, using psycopg2, and ogr2ogr, etc., at Aberystwyth University, UK.

  • Python 3 Basics Tutorial (Kristian Rother)

    Written specifically for beginners, it takes you step-by-step through writing your very first program, explaining each portion of code as we go along, guides you through setting up Python, choosing an IDE, as well as the various elements of coding in Python.

  • Modeling Creativity - Case Studies in Python (Tom De Smedt)

    This book is to model creativity using computational approaches in Python. The aim is to construct computer models that exhibit creativity in an artistic context, that is, that are capable of generating or evaluating an artwork (visual or linguistic), etc.

  • Python 201: (Slightly) Advanced Python Topics (Dave Kuhlman)

    This course contains discussions of several advanced topics that are of interest to Python programmers: regular expressions, unit tests, extending and embedding Python, parsing, GUI applications, guidance on packages and modules.

  • The 1 Page Python Book: Beginners Guide to Programming in Python

    This book is a guide to learn Python programming. Precise code examples and broken down explanations, neatly organised into chapters. Provides links to external resources for supplementary reading wherever required.

  • Learn Python Fast Deep Simple (Behnam Khani)

    A Visual and Interactive Journey into Python Programming. Dive into captivating visuals and interactive exercises, perfect for beginners and intermediate programmers. Unleash your Python skills with this immersive eBook.

  • Intermediate Python (Obi Ike-Nwosu)

    Provides a reader with a holistic and in-depth knowledge of the Python language. It explains how methods and functions are related, how sequences can be created elegantly, the tools for functional programming, how user defined objects can be used.

  • Intermediate Python (Muhammad Yasoob Ullah Khalid)

    Python is an amazing language with a strong and friendly community of programmers. This book aims to give you bits of information about some interesting topics which you can further explore after getting the basics of Python down your throat,

  • Python for Informatics: Exploring Information (Charles Severance)

    This book provides an Informatics-oriented introduction to programming. The primary difference between a computer science approach and the Informatics approach taken in this book is a greater focus on using Python to solve problems.

  • Algorithmic Problem Solving with Python (John B. Schneider)

    This book uses Python to introduce folks to programming and algorithmic thinking. It is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.

  • Python for Econometrics, Statistics and Data Analysis

    This book provides an introduction to Python for a beginning programmer. They may also be useful for an experienced Python programmer interested in using NumPy, SciPy, and matplotlib for numerical and statistical analaysis.

  • The Full Stack Python (Matt Makai)

    This book explains each Python web application stack layer and provides the best web resources for those topics. Throughout the book it takes an example open source Python web application through a complete deployment on a virtual private server.

  • Essentials of Compilation: An Incremental Approach in Python

    A hands-on approach to understanding and building compilers using Python. Explains the essential concepts, algorithms, and data structures that underlie modern compilers and lays the groundwork for future study of advanced topics.

  • O'Reilly® Natural Language Processing with Python

    This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.

  • Hands-On Natural Language Processing with Python

    It teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today's NLP challenges. you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges.

  • Think Stats, 2nd Edition: Exploratory Data Analysis in Python

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. Develop your understanding of probability and statistics by writing and testing code.

  • Gui Programming With Python: Using the Qt Toolkit (B. Rempt)

    The main topic of this book is application development using PyQt. Whether you're building GUI prototypes or full-fledged cross-platform GUI applications with native look-and-feel, PyQt is your fastest, easiest, most powerful solution.

  • Deep Learning Tutorials using Python (LISA Lab)

    The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.

  • Python for Kids: A Playful Introduction to Programming

    This book brings Python to life and brings you (and your parents) into the world of programming. It will guide you through the basics as you experiment with unique example programs that feature ravenous monsters, secret agents, thieving ravens, and more.

  • Hacking Secret Ciphers with Python (Albert Sweigart)

    This book teaches you how to write your own cipher programs and also the hacking programs that can break the encrypted messages from these ciphers.

  • Think Python - How to Think Like a Computer Scientist

    Think Python is an introduction to Python programming for students with no programming experience. It starts with the most basic concepts of programming, and is carefully designed to define all terms when they are first used.

  • Open Data Structures: An Introduction using Python (Pat Morin)

    This book is an introduction to the field of data structures and algorithms, it covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs.

  • Programming Computer Vision with Python: Tools and Algorithms

    This book is a hands-on introduction to computer vision using Python. It gives an easily accessible entry point to hands-on computer vision with enough understanding of the underlying theory and algorithms to be a foundation for students, researchers.

  • Invent Your Own Computer Games with Python, 3rd Edition

    It teaches you how to program computer games in the Python programming language. Each chapter gives you the complete source code for a new game. It was written to be understandable by anyone of any age who has never programmed before.

  • Learn to Program Using Python: A Tutorial for Hobbyists, Starters

    This book is based on a popular on-line tutorial that has been expanded and enhanced for this book. It takes you step-by-step through all the essential programming topics. It takes you step-by-step through all the essential programming topics.

  • Introduction to Python for Computational Science and Engineering

    This book summarises a number of core ideas relevant to Computational Engineering and Scientific Computing using Python. The emphasis is on introducing some basic Python (programming) concepts that are relevant for numerical algorithms.

  • Python Scripting for Computational Science (Hans Langtangen)

    With a primary focus on examples and applications of relevance to computational scientists, this brilliantly useful book shows computational scientists how to develop tailored, flexible, and human-efficient working environments built from small scripts written in the easy-to-learn, high-level Python language.

  • Essential Python 3 (Kevin Vans-Colina)

    This book is written to provide all the essential information to get you programming in Python 3. It starts by running through the basics of the language before showing you how to use some of the powerful libraries included in Python 3.

  • Python Succinctly (Jason Cannon)

    Learn to use the Python language to create programs of all kinds. It will guide you from complete unfamiliarity with Python to creating practical applications. With Python Succinctly, lack of experience isn't an obstacle to programming language mastery.

  • The Python Language Reference Manual (Guido van Rossum, et al)

    This manual is intended for advanced users who need a complete description of the Python 3.x language syntax and object system.

  • A Practical Introduction to Python Programming (Brian Heinold)

    This book is for anyone who wants to understand Python programming. Both a tutorial and a reference, you'll code along with the book, writing programs to solve real-world problems as you learn the fundamentals of programming using Python 3.

  • An Introduction to Python (Guido van Rossum)

    This manual provides an introduction to Python, an easy to learn object-oriented programming language. Python combines power with clear syntax. It has modules, classes, exceptions, very high level data types, and dynamic typing.

  • Python for You and Me (Kushal Das)

    This is a simple book to learn Python programming language, it is for the programmers who are new to Python. It shall introduce you to an easy way to learn Python and be able to complete your own projects!

  • Make Games with Python on Raspberry Pi (Sean M. Tracey)

    You are going to learn how to make a game on our Raspberry Pi from the ground up. It is designed to help you learn many of the essential skills you'll need to make games with Python and Pygame on your Raspberry Pi.

  • O'Reilly® Mining the Social Web, 2nd Edition (Matthew A. Russell)

    This book shows you how to answer these questions like how can you tap into social data and discover who's connecting with whom, which insights are lurking just beneath the surface, and what people are talking about?

  • O'Reilly® Think Stats: Probability and Statistics using Python

    This book shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

  • O'Reilly® The Python Standard Library (Fredrik Lundh)

    Ideal for any working Python developer, this book provides an excellent tour of some of the most important modules in today's Python standard.

  • Learning to Program Using Python (Cody Jackson)

    The core Python language (both versions 2.x and 3.x) is discussed, as well as an introduction to graphical user interface creation. The ideas covered in this book provide the reader with many major programming topics, applicable to a wide variety of programming languages.

  • Programming and Mathematical Thinking: Discrete Math & Python

    Starting at an elementary level, this book teaches about fundamental structures of discrete mathematics and many simple but powerful programming techniques using those structures.

  • Keras Succinctly (James McCaffrey)

    The goal of this book is to introduce you Keras, the one of the most popular and powerful libraries for building neural networks in Python. You'll learn how to build a convolutional neural network in Python!

  • Dive Into Python: Python from Novice to Pro (Mark Pilgrim)

    This book is your 'desert island' Python book. If you've never programmed before, Python is an excellent language to learn modern programming techniques.

  • A Byte of Python (Swaroop C H)

    This book serves as a tutorial or guide to the Python language for a beginner audience. It is written for the latest Python 3, even though Python 2 is the commonly found version of Python today (read more about it in Python 2 versus 3 section).

  • A Python Book (Dave Kuhlman)

    This book is a self-learning document for a course in Python programming. It contains (1) a part for beginners, (2) a discussion of several advanced topics that are of interest to Python programmers, and (3) a Pythonworkbook with lots of exercises.

  • Text Processing in Python (David Mertz)

    This book is an example-driven, hands-on tutorial that carefully teaches programmers how to accomplish numerous text processing tasks using the Python language. It provides efficient and effective solutions to specific text processing problems.

  • Making Games with Python and Pygame (Albert Sweigart)

    This is a programming book that covers the Pygame game library for the Python programming language, written to be understandable by kids as young as 10 to 12 years old, although it is great for anyone of any age who has some familiarity with Python.

  • Django 3 Web Development Cookbook (Aidas Bendoraitis, et al.)

    Practical recipes for building fast, robust, and secure web apps using Django 3 and Python. Actionable solutions to common problems in Python web development. It not only helps you work with the PostgreSQL database but also the MySQL database.

  • Effective Django (Nathan Yergler)

    With this book, you should have an understanding of how Django's pieces fit together, how to use them to engineer web applications, and where to look to dig deeper. Building web applications that are testable, maintainable, and scalable.

  • The Definitive Guide to Django: Web Development Done Right

    This book will show you how to assemble Django's features and take advantage of its power to design, develop, and deploy a fully-featured web site. It will walk you through the creation of an example web application, with lots of code examples.

  • O'Reilly® Think Complexity: Science and Modeling (Allen Downey)

    This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science.

  • Computational Physics with Python (Mark Newman)

    A complete introduction to the field of computational physics, with examples and exercises in the Python programming language. It explains the fundamentals of computational physics and describes in simple terms the techniques that every physicist should know,.

  • Python Scientific Lecture Notes (Scipy Lectures)

    This book is the teaching material on the scientific Python ecosystem, a quick introduction to central tools and techniques. It is for programmers from beginner to expert. Work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries.

  • Inventory Analytics: A Practicable, Python-Driven Approach

    This book provides a comprehensive and accessible introduction to the theory and practice of Inventory Control – a significant research area central to supply chain planning. It adopts a practicable, Python-driven approach to illustrating theories and concepts.

  • Python in Hydrology (Sat Kumar Tomer)

    This book is written for learning Python using its applications in hydrology. The book covers the basic applications of hydrology, and also the advanced topic like use of copula.

  • How To Write Your Own Software Using Python (Steven F. Lott)

    This book will help you build basic programming skills, organized in a way that builds up the language in layers from simple, central concepts to more advanced features.

  • First Course in Programming with Karel the Robot and Python

    This book provides a gentle yet efficient and comprehensive introduction to modern algorithmic design and computer programming. It consists of two programming languages - Karel the Robot and Python.

  • Basic Data Analysis and More - A Guided Tour Using Python

    In this book, a selection of frequently required statistical tools will be introduced and illustrated. From a point of view of data analysis, an exemplary implementation of the presented techniques using the Python programming language is provided.

  • Become a Code Breaker with Python: a Beginner's Guide

    This book describes several encryption Python programs for various ciphers, along with how to write Python programs that can break these ciphers.

  • The Art and Craft of Programming, Python Edition (John C. Lusth)

    This book is designed to be used as the primary textbook in a college-level first course in computing. It takes a fairly traditional approach, emphasizing problem solving, design, and programming as the core skills of computer science.

  • Learning with Python: Interactive Edition

    This book provides a comprehensive, accessible introduction to Python fundamentals. An ideal first language for learners entering the rapidly expanding field of computer science, Python gives students a solid platform of key problem-solving skills that translate easily across programming languages.

  • Learning to Program with Python (Richard L. Halterman)

    This book does not attempt to cover all the facets of the Python programming language. The focus here is on introducing programming techniques and developing good habits. The code in this book is based on Python 3.

  • Design Patterns in Python (Alexander Shvets, et al)

    This book is for Python programmers with an intermediate background and an interest in design patterns implemented in idiomatic Python. Programmers of other languages who are interested in Python can also benefit from this book.

  • O'Reilly® Python Cookbook, 3rd Ed: Recipes for Mastering Python 3

    This book is packed with practical recipes written and tested with Python 3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms.

  • Go for Python Programmers (Jason McVetta)

    This book is intended to provide a solid introduction to the Go language for experienced Python programmers. You'll explore key areas of the language such as concurrency, testing, data structures, and more.

  • Building Skills in Python: A Programmer's Introduction to Python

    This book is a complete presentation of the Python language. It is oriented toward learning, which involves accumulating many closely intertwined concepts. It is intended for professional programmers who need to learn Python and provides specific help.

  • Fundamentals of Programming: With OOP, Python Edition

    A balanced and flexible approach to the incorporation of object-oriented principles in introductory courses using Python. Includes an exclusive, easy-to-use custom graphics library that helps readers grasp both basic and advanced concepts.

  • The Definitive Guide to Pylons (James Gardner)

    This book is a comprehensive introduction to Pylons, the web framework that uses the best of Ruby, Python, and Perl and the emerging WSGI standard to provide structure and flexibility.

  • Start Here: Python Programming for Beginners (Jody S. Ginther)

    This is a book for the total beginner who is interested in programming. It teaches the new programmer from ground zero through hands-on exercises.

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