If you are new to programming with Python and are looking for a solid introduction, this is the book for you. Developed by computer science instructors, books in the "for the absolute beginner" series teach the principles of programming through simple game creation. You will acquire the skills that you need for practical Python programming applications and will learn how these skills can be put to use in real-world scenarios. Throughout the chapters, you will find code samples that illustrate concepts presented. At the end of each chapter, you will find a complete game that demonstrates the key ideas in the chapter, a summary of the chapter, and a set of challenges that tests your newfound knowledge. By the time you finish this book, you?ll be well versed in Python and be able to apply the basic programming principles you?ve learned to the next programming language you tackle.

Based on the authors' market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++. Begins by discussing Python's conceptually simple syntax, which allows for a greater focus on concepts. Employs a consistent object-oriented viewpoint throughout the text. Presents each data structure using ADTs and their respective implementations and introduces important design patterns as a means to organize those implementations into classes, methods, and objects. Provides a thorough discussion on the analysis and design of fundamental data structures. Includes many helpful Python code examples, with source code provided on the website. Uses illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Provides hundreds of exercises that promote creativity, help readers learn how to think like programmers, and reinforce important concepts. Contains many Python-code and pseudo-code fragments, and hundreds of exercises, which are divided into roughly 40% reinforcement exercises, 40% creativity exercises, and 20% programming projects.

This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (or MOOC) offered by the pioneering MIT--Harvard collaboration edX. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: * Collaborative filtering techniques that enable online retailers to recommend products or media * Methods of clustering to detect groups of similar items in a large dataset * Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm * Optimization algorithms that search millions of possible solutions to a problem and choose the best one * Bayesian filtering, used in spam filters for classifying documents based on word types and other features * Using decision trees not only to make predictions, but to model the way decisions are made * Predicting numerical values rather than classifications to build price models * Support vector machines to match people in online dating sites * Non-negative matrix factorization to find the independent features in a dataset * Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

Learn how to write idiomatic, effective Python code by leveraging its best features. Python's simplicity quickly lets you become productive with it, but this often means you aren’t using everything the language has to offer. By taking you through Python’s key language features and libraries, this practical book shows you how to make your code shorter, faster, and more readable all at the same time—what experts consider Pythonic.
Many programmers who learn Python basics fall into the trap of reinventing the wheel because of past experience in other languages, and try to bend the language to patterns that don't really apply to it. Author Luciano Ramalho, a Python Software Foundation member and Python programmer for 15 years, helps you drop your accent from another language so you can code Python fluently.
•Learn practical applications of generators for database processing
•Rethink some design patterns in a Python context
•Examine attribute descriptors and when to use them: the key to ORMs
•Explore Pythonic objects: protocols versus interfaces, abstract base classes and multiple inheritance

This book offers a highly accessible introduction to Natural Language Processing, the field that underpins a variety of language technologies, ranging from predictive text and email filtering to automatic summarization and translation. With Natural Language Processing with Python, you'll learn how to write Python programs to work with large collections of unstructured text. You'll access richly-annotated datasets using a comprehensive range of linguistic data structures. And you'll understand the main algorithms for analyzing the content and structure of written communication.
Packed with examples and exercises, Natural Language Processing with Python will help you:
* Extract information from unstructured text, to guess the topic or identify "named entities"
* Analyze linguistic structure in text, including parsing and semantic analysis
* Access popular linguistic databases, including WordNet and treebanks
* Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence
Perfect for individual study, or as a classroom and workshop textbook, this book will help you gain practical skills in Natural Language Processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library.
If you're interested in developing Web applications, analyzing multilingual news sources, documenting endangered languages, or if you are simply curious to have a programmer's perspective on how human language works, you will find Natural Language Processing with Python both fascinating and immensely useful.

Cython can yield massive performance improvements over pure Python—speedups of 3000X are easily attainable for certain patterns. With this book, Kurt Smith shows you how to use Cython to easily wrap C and C++ libraries in Python, handling all the details of memory management for you. By removing the barrier between Python and C, Cython harnesses the best of both languages while remaining familiar and comfortable to Python users.
Cython has proven its usefulness in many foundational projects: Pandas, SymPy, Sage, and dozens of others, both open and closed source. You'll learn how Cython is an essential part of any performance-oriented Python programmer’s arsenal.

Mark Pilgrim's Dive Into Python 3 is a hands-on guide to Python 3 (the latest version of the Python language) and its differences from Python 2. As in the original book, Dive Into Python, 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.
This book includes:
* Example programs completely rewritten to illustrate powerful new concepts now available in Python 3: sets, iterators, generators, closures, comprehensions, and much more
* A detailed case study of porting a major library from Python 2 to Python 3
* A comprehensive appendix of all the syntactic and semantic changes in Python 3
This is the perfect resource for you if you need to port applications to Python 3, or if you like to jump into languages fast and get going right away.
What you'll learn
* Understand Python 3 code by seeing it broken down and explained
* Make full use of the latest Python features such as iterators, generators, closures, classes and comprehensions
* Refactor existing code to improve maintainability
* Learn how to serialize Python objects with the pickle protocol and JSON format
* Learn how to package your own Python libraries and upload them to the Python Package Index to share your projects with Python developers worldwide
* Use Python 3 to consume HTTP web services
* Port existing Python applications to Python 3 by following a case study for a major library
Who is this book for?
* Anyone who wants to learn the latest version of Python in a fast, hands-on fashion
* Existing Python programmers who want to learn quickly how to make the most of the features of the latest version of Python and port their code to it
* Programmers coming from other languages wanting a fast introduction to Python that gets them thinking about advanced concepts quickly

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.
With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.
You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

'A Byte of Python' is a book on programming using the Python language. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you.

You've learned the basics of Python, but how do you take your skills to the next stage? Even if you know enough to be productive, there are a number of features that can take you to the next level in Python. Pro Python explores concepts and features normally left to experimentation, allowing you to be even more productive and creative. In addition to pure code concerns, Pro Python will develop your programming techniques and approaches, which will help make you a better Python programmer. Not only will this book help your code, it will also help you understand and interact with the many established Python communities, or even start your own. * Take your Python knowledge and coding skills to the next level. * Write clean, innovative code that will be respected among your peers. * Make your code do more with introspection and metaprogramming. * Design complete frameworks and libraries (two are included in the book!). What you'll learn * Write strong Python code that will be respected in the Python community. * Understand the reasons behind big design decisions in Python. * Write programs that can reconfigure themselves in Python. * Disguise your code as different types of objects in Python. * Inspect just about any object in Python. * Prepare your code for international audiences. * Ensure code quality with rigorous testing. Who this book is for This book is for intermediate to advanced Python programmers who are looking to understand how and why Python works the way it does and how they can take their code to the next level. Table of Contents * Principles and Philosophy * Advanced Basics * Functions * Classes * Common Protocols * Object Management * Strings * Documentation * Testing * Distribution * Sheets: A CSV Framework

Portable, powerful, and a breeze to use, Python is the popular open source object-oriented programming language used for both standalone programs and scripting applications. Completely updated for Python 3, the recipes in this book include: Data structures and algorithms Strings and text Dates and times Metaprogramming Testing With scores of practical examples and pertinent background information, the Python Cookbook, 3rd Edition is the one source you need if you're looking to build efficient, flexible, scalable, and well-integrated systems.

You’ve completed a basic Python programming tutorial or finished Al Sweigart’s best selling Automate the Boring Stuff with Python. What’s the next step toward becoming a capable, confident software developer?
Welcome to Beyond the Basic Stuff with Python. More than a mere collection of advanced syntax and masterful tips for writing clean code, you’ll learn how to advance your Python programming skills by using the command line and other professional tools like code formatters, type checkers, linters, and version control. Sweigart takes you through best practices for setting up your development environment, naming variables, and improving readability, then tackles documentation, organization and performance measurement, as well as object-oriented design and the Big-O algorithm analysis commonly used in coding interviews. The skills you learn will boost your ability to program—not just in Python but in any language.
You’ll learn:
Coding style, and how to use Python’s Black auto-formatting tool for cleaner code
Common sources of bugs, and how to detect them with static analyzers
How to structure the files in your code projects with the Cookiecutter template tool
Functional programming techniques like lambda and higher-order functions
How to profile the speed of your code with Python’s built-in timeit and cProfile modules
The computer science behind Big-O algorithm analysis
How to make your comments and docstrings informative, and how often to write them
How to create classes in object-oriented programming, and why they’re used to organize code
Toward the end of the book you’ll read a detailed source-code breakdown of two classic command-line games, the Tower of Hanoi (a logic puzzle) and Four-in-a-Row (a two-player tile-dropping game), and a breakdown of how their code follows the book’s best practices. You’ll test your skills by implementing the program yourself.
Of course, no single book can make you a professional software developer. But Beyond the Basic Stuff with Python will get you further down that path and make you a better programmer in the process as you learn to write readable code that’s easy to debug and perfectly Pythonic.

As Python continues to grow in popularity, projects are becoming larger and more complex. Many Python developers are now taking an interest in high-level software architecture patterns such as hexagonal/clean architecture, event-driven architecture, and strategic patterns prescribed by domain-driven design (DDD). But translating those patterns into Python isn’t always straightforward.
With this practical guide, Harry Percival and Bob Gregory from MADE.com introduce proven architectural design patterns to help Python developers manage application complexity. Each pattern is illustrated with concrete examples in idiomatic Python that explain how to avoid some of the unnecessary verbosity of Java and C# syntax. You’ll learn how to implement each of these patterns in a Pythonic way.
Architectural design patterns include:
Dependency inversion, and its links to ports and adapters (hexagonal/clean architecture)
Domain-driven design’s distinction between entities, value objects, and aggregates
Repository and Unit of Work patterns for persistent storage
Events, commands, and the message bus
Command Query Responsibility Segregation (CQRS)
Event-driven architecture and reactive microservices