Download Free Hypermodern Python Tooling Book in PDF and EPUB Free Download. You can read online Hypermodern Python Tooling and write the review.

Keeping up with the Python ecosystem can be daunting. Its developer tooling doesn't provide the out-of-the-box experience native to languages like Rust and Go. When it comes to long-term project maintenance or collaborating with others, every Python project faces the same problem: how to build reliable workflows beyond local development while staying in sync with the evolving ecosystem. With this hands-on guide, Python developers will learn how to forge the moving parts of a Python project into an easy-to-use toolchain, using state-of-the-art tools including Poetry, Nox, pytest, mypy, pre-commit, Black, Ruff, uv, Rye, Hatch, and more. Author Claudio Jolowicz shows you how to create robust Python project structures complete with unit tests, static analysis, code formatting, and type checking. You'll learn how to: Create open source projects with state-of-the-art infrastructure Build a custom infrastructure for all Python projects in a company or team Improve and modernize the infrastructure of an existing Python project Evaluate modern Python tooling for adoption in existing projects Use tools for packaging and dependency management Automate common development tasks such as testing, dependency updates, and publishing releases
Enhance your Python skills with the third edition of Modern Python Cookbook with 130+ new and updated recipes covering Python 3.12, including new coverage on graphics, visualizations, dependencies, virtual environments, and more. Purchase of the print or Kindle book includes a free eBook in PDF format Key Features New chapters on type matching, data visualization, dependency management, and more Comprehensive coverage of Python 3.12 with updated recipes and techniques Provides practical examples and detailed explanations to solve real-world problems efficiently Book DescriptionPython is the go-to language for developers, engineers, data scientists, and hobbyists worldwide. Known for its versatility, Python can efficiently power applications, offering remarkable speed, safety, and scalability. This book distills Python into a collection of straightforward recipes, providing insights into specific language features within various contexts, making it an indispensable resource for mastering Python and using it to handle real-world use cases. The third edition of Modern Python Cookbook provides an in-depth look into Python 3.12, offering more than 140 new and updated recipes that cater to both beginners and experienced developers. This edition introduces new chapters on documentation and style, data visualization with Matplotlib and Pyplot, and advanced dependency management techniques using tools like Poetry and Anaconda. With practical examples and detailed explanations, this cookbook helps developers solve real-world problems, optimize their code, and get up to date with the latest Python features.What you will learn Master core Python data structures, algorithms, and design patterns Implement object-oriented designs and functional programming features Use type matching and annotations to make more expressive programs Create useful data visualizations with Matplotlib and Pyplot Manage project dependencies and virtual environments effectively Follow best practices for code style and testing Create clear and trustworthy documentation for your projects Who this book is for This Python book is for web developers, programmers, enterprise programmers, engineers, and big data scientists. If you are a beginner, this book offers helpful details and design patterns for learning Python. If you are experienced, it will expand your knowledge base. Fundamental knowledge of Python programming and basic programming principles will be helpful
If you’re among the Python developers put off by asyncio’s complexity, it’s time to take another look. Asyncio is complicated because it aims to solve problems in concurrent network programming for both framework and end-user developers. The features you need to consider are a small subset of the whole asyncio API, but picking out the right features is the tricky part. That’s where this practical book comes in. Veteran Python developer Caleb Hattingh helps you gain a basic understanding of asyncio’s building blocks—enough to get started writing simple event-based programs. You’ll learn why asyncio offers a safer alternative to preemptive multitasking (threading) and how this API provides a simpleway to support thousands of simultaneous socket connections. Get a critical comparison of asyncio and threading for concurrent network programming Take an asyncio walk-through, including a quickstart guidefor hitting the ground looping with event-based programming Learn the difference between asyncio features for end-user developers and those for framework developers Understand asyncio’s new async/await language syntax, including coroutines and task and future APIs Get detailed case studies (with code) of some popular asyncio-compatible third-party libraries
Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform. Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to "get stuff done" in Python? This is your guide. Python foundations, including a brief introduction to the language How to automate text, write command-line tools, and automate the filesystem Linux utilities, package management, build systems, monitoring and instrumentation, and automated testing Cloud computing, infrastructure as code, Kubernetes, and serverless Machine learning operations and data engineering from a DevOps perspective Building, deploying, and operationalizing a machine learning project
The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Python’s simplicity lets you become productive quickly, but this often means you aren’t using everything it has to offer. With this hands-on guide, you’ll learn how to write effective, idiomatic Python code by leveraging its best—and possibly most neglected—features. Author Luciano Ramalho takes you through Python’s core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time. Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3. This book covers: Python data model: understand how special methods are the key to the consistent behavior of objects Data structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode age Functions as objects: view Python functions as first-class objects, and understand how this affects popular design patterns Object-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritance Control flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packages Metaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work
The Hitchhiker's Guide to Python takes the journeyman Pythonista to true expertise. More than any other language, Python was created with the philosophy of simplicity and parsimony. Now 25 years old, Python has become the primary or secondary language (after SQL) for many business users. With popularity comes diversityâ??and possibly dilution. This guide, collaboratively written by over a hundred members of the Python community, describes best practices currently used by package and application developers. Unlike other books for this audience, The Hitchhikerâ??s Guide is light on reusable code and heavier on design philosophy, directing the reader to excellent sources that already exist.
A project-based approach to learning Python programming for beginners. Intriguing projects teach you how to tackle challenging problems with code. You've mastered the basics. Now you're ready to explore some of Python's more powerful tools. Real-World Python will show you how. Through a series of hands-on projects, you'll investigate and solve real-world problems using sophisticated computer vision, machine learning, data analysis, and language processing tools. You'll be introduced to important modules like OpenCV, NumPy, Pandas, NLTK, Bokeh, Beautiful Soup, Requests, HoloViews, Tkinter, turtle, matplotlib, and more. You'll create complete, working programs and think through intriguing projects that show you how to: Save shipwrecked sailors with an algorithm designed to prove the existence of God Detect asteroids and comets moving against a starfield Program a sentry gun to shoot your enemies and spare your friends Select landing sites for a Mars probe using real NASA maps Send unbreakable messages based on a book code Survive a zombie outbreak using data science Discover exoplanets and alien megastructures orbiting distant stars Test the hypothesis that we're all living in a computer simulation And more! If you're tired of learning the bare essentials of Python Programming with isolated snippets of code, you'll relish the relevant and geeky fun of Real-World Python!
While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it's the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein--creator of xlwings, a popular open source package for automating Excel with Python--shows experienced Excel users how to integrate these two worlds efficiently. Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started. Use Python without extensive programming knowledge Get started with modern tools, including Jupyter notebooks and Visual Studio code Use pandas to acquire, clean, and analyze data and replace typical Excel calculations Automate tedious tasks like consolidation of Excel workbooks and production of Excel reports Use xlwings to build interactive Excel tools that use Python as a calculation engine Connect Excel to databases and CSV files and fetch data from the internet using Python code Use Python as a single tool to replace VBA, Power Query, and Power Pivot
Do less work when testing your Python code, but be just as expressive, just as elegant, and just as readable. The pytest testing framework helps you write tests quickly and keep them readable and maintainable - with no boilerplate code. Using a robust yet simple fixture model, it's just as easy to write small tests with pytest as it is to scale up to complex functional testing for applications, packages, and libraries. This book shows you how. For Python-based projects, pytest is the undeniable choice to test your code if you're looking for a full-featured, API-independent, flexible, and extensible testing framework. With a full-bodied fixture model that is unmatched in any other tool, the pytest framework gives you powerful features such as assert rewriting and plug-in capability - with no boilerplate code. With simple step-by-step instructions and sample code, this book gets you up to speed quickly on this easy-to-learn and robust tool. Write short, maintainable tests that elegantly express what you're testing. Add powerful testing features and still speed up test times by distributing tests across multiple processors and running tests in parallel. Use the built-in assert statements to reduce false test failures by separating setup and test failures. Test error conditions and corner cases with expected exception testing, and use one test to run many test cases with parameterized testing. Extend pytest with plugins, connect it to continuous integration systems, and use it in tandem with tox, mock, coverage, unittest, and doctest. Write simple, maintainable tests that elegantly express what you're testing and why. What You Need: The examples in this book are written using Python 3.6 and pytest 3.0. However, pytest 3.0 supports Python 2.6, 2.7, and Python 3.3-3.6.