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.

2 edition

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You’ll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.
what's inside
Deep learning from first principles
Image-classification, imagine segmentation, and object detection
Deep learning for natural language processing
Timeseries forecasting
Neural style transfer, text generation, and image generation

To tap into the power of Python's open data science stack—including NumPy, Pandas, Matplotlib, Scikit-learn, and other tools—you first need to understand the syntax, semantics, and patterns of the Python language. This report provides a brief yet comprehensive introduction to Python for engineers, researchers, and data scientists who are already familiar with another programming language.
Author Jake VanderPlas, an interdisciplinary research director at the University of Washington, explains Python’s essential syntax and semantics, built-in data types and structures, function definitions, control flow statements, and more, using Python 3 syntax.
You’ll explore:
- Python syntax basics and running Python code
Basic semantics of Python variables, objects, and operators
- Built-in simple types and data structures
- Control flow statements for executing code blocks conditionally
- Methods for creating and using reusable functions
Iterators, list comprehensions, and generators
- String manipulation and regular expressions
- Python’s standard library and third-party modules
- Python’s core data science tools
- Recommended resources to help you learn more

Grokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You'll start with sorting and searching and, as you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python.

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

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.