Язык Python завоевал популярность благодаря тому, что позволяет новичкам почти сразу же браться за написание кода. Однако достигнуть цельного понимания уникальных возможностей Python чрезвычайно трудно, особенно если учесть, что на этом пути вас подстерегает множество скрытых ловушек.
Книга приобщит вас к стилю программирования, выдержанному в истинном "духе Python", и поможет научиться писать исключительно надежный и высокопроизводительный код. Используя сжатый стиль изложения, пионером которого был Скотт Мейерс, автор приводит 59 описаний лучших методик программирования, дает советы и показывает кратчайшие пути решения различных задач программирования на Python, дополняя их реалистичными примерами кода.
Опираясь на свой многолетний опыт создания инфраструктурных проектов для компании Google, автор раскрывает секреты малоизвестных аспектов и идиом Python, радикально влияющих на поведение и производительность кода. Вы ознакомитесь с наилучшими способами решения ключевых задач, что облегчит понимание, сопровождение и усовершенствование вашего кода.

Основные темы книги:
• Действенные рекомендации по основным аспектам разработки ПО с использованием версий Python 3.x и 2.x, дополненные подробными описаниями и примерами.
• Лучшие методики написания функций, снижающие вероятность появления ошибок в коде.
• Точное описание вариантов поведения с помощью классов и объектов.
• Рекомендации относительно того, как избежать скрытых ошибок с помощью метаклассов и динамических атрибутов.
• Эффективные подходы к решению проблем, связанных с одновременным и параллельным выполнением множества операций.
• Усовершенствованные приемы работы со встроенными модулями Python.
• Инструментальные средства и лучшие методики коллективной разработки.
• Решения по отладке, тестированию и оптимизации кода.

Бретт Слаткин - ведущий инженер-разработчик компании Google и соучредитель компании Google Consumer Surveys.
До этого разрабатывал инфраструктуру Python для Google App Engine, адаптировал Python для управления огромным серверным парком Google и использовал Python в качестве инструмента реализации системы Google для протокола PubSubHubbub. Получил степень бакалавра в области компьютерной инженерии в Колумбийском университете в Нью-Йорке.

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Язык Python завоевал популярность благодаря тому, что позволяет новичкам почти сразу же браться за написание кода. Однако достигнуть цельного понимания уникальных возможностей… Развернуть 

Managing people is difficult wherever you work. But in the tech industry, where management is also a technical discipline, the learning curve can be brutal—especially when there are few tools, texts, and frameworks to help you. In this practical guide, author Camille Fournier (tech lead turned CTO) takes you through each stage in the journey from engineer to technical manager.

From mentoring interns to working with senior staff, you’ll get actionable advice for approaching various obstacles in your path. This book is ideal whether you’re a new manager, a mentor, or a more experienced leader looking for fresh advice. Pick up this book and learn how to become a better manager and leader in your organization.

Begin by exploring what you expect from a manager
Understand what it takes to be a good mentor, and a good tech lead
Learn how to manage individual members while remaining focused on the entire team
Understand how to manage yourself and avoid common pitfalls that challenge many leaders
Manage multiple teams and learn how to manage managers
Learn how to build and bootstrap a unifying culture in teams

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Managing people is difficult wherever you work. But in the tech industry, where management is also a technical discipline, the learning curve can be brutal—especially when there… Развернуть 

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.

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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… Развернуть 

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 design patterns such as hexagonal/clean architecture, event-driven architecture, and the strategic patterns prescribed by domain-driven design (DDD). But translating those patterns into Python isn’t always straightforward.

With this hands-on guide, Harry Percival and Bob Gregory from MADE.com introduce proven architectural design patterns to help Python developers manage application complexity—and get the most value out of their test suites.

Each pattern is illustrated with concrete examples in beautiful, idiomatic Python, avoiding some of the verbosity of Java and C# syntax. 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

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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 design patterns… Развернуть 

Many forces affect software today: larger datasets, geographical disparities, complex company structures, and the growing need to be fast and nimble in the face of change. Proven approaches such as service-oriented and event-driven architectures are joined by newer techniques such as microservices, reactive architectures, DevOps, and stream processing. Many of these patterns are successful by themselves, but as this practical ebook demonstrates, they provide a more holistic and compelling approach when applied together.

Author Ben Stopford explains how service-based architectures and stream processing tools such as Apache Kafka can help you build business-critical systems. You’ll learn how to apply patterns including Event Sourcing and CQRS, and how to build multi-team systems with microservices and SOA using patterns such as "inside out databases" and "event streams as a source of truth." These approaches provide a unique foundation for how these large, autonomous service ecosystems can communicate and share data.

- Learn why streaming beats request-response based architectures in complex, contemporary use cases
- Understand why replayable logs such as Kafka provide a backbone for both service communication and shared datasets
- Explore how event collaboration and event sourcing patterns increase safety and recoverability with functional, event-driven approaches
- Build service ecosystems that blend event-driven and request-driven interfaces using a replayable log and Kafka’s Streams API
- Scale beyond individual teams into larger, department- and company-sized architectures, using event streams as a source of truth

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Many forces affect software today: larger datasets, geographical disparities, complex company structures, and the growing need to be fast and nimble in the face of change. Proven… Развернуть 

Does it seem like your Python projects are getting bigger and bigger? Are you feeling the pain as your codebase expands and gets tougher to debug and maintain? Python is an easy language to learn and use, but that also means systems can quickly grow beyond comprehension. Thankfully, Python has features to help developers overcome maintainability woes.

In this practical book, author Patrick Viafore shows you how to use Python's type system to the max. You'll look at user-defined types, such as classes and enums, and Python's type hinting system. You'll also learn how to make Python extensible and how to use a comprehensive testing strategy as a safety net. With these tips and techniques, you'll write clearer and more maintainable code.

- Learn why types are essential in modern development ecosystems
- Understand how type choices such as classes, dictionaries, and enums reflect specific intents
- Make Python extensible for the future without adding bloat
- Use popular Python tools to increase the safety and robustness of your codebase
- Evaluate current code to detect common maintainability gotchas
- Build a safety net around your codebase with linters and tests

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Does it seem like your Python projects are getting bigger and bigger? Are you feeling the pain as your codebase expands and gets tougher to debug and maintain? Python is an easy… Развернуть 

"Как пасти котов" - это книга о лидерстве и руководстве, о том, как первое совмещать со вторым. Это, если хотите, словарь трудных случаев управления IT-проектами. Программист подобен кошке, которая гуляет сама по себе. Так уж исторически сложилось. Именно поэтому так непросто быть руководителем команды программистов. Даже если вы еще месяц назад были блестящим и дисциплинированным программистом и вдруг оказались в роли менеджера, вряд ли вы знаете, с чего надо начать, какой выбрать стиль руководства, как нанимать и увольнять сотрудников, проводить совещания, добиваться своевременного выполнения задач. В таком случае без этой книги вам не обойтись. А может быть, вы - опытный менеджер, желающий пересмотреть свои принципы лидерства? Тогда, опять же, эта книга для вас. Вне зависимости от возраста, пола и социального статуса, она поможет вам укрепить свои позиции в роли лидера программистов. Материал изложен довольно компактно и легко укладывается в голове. Стоя в книжном магазине и раздумывая, что же купить, задайте себе один простой вопрос: "Нужно ли мне совершенствовать свои лидерские навыки?" Полагаю, вы ответите: "Да", - а значит, данная книга окажется для вас небесполезной.

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"Как пасти котов" - это книга о лидерстве и руководстве, о том, как первое совмещать со вторым. Это, если хотите, словарь трудных случаев управления IT-проектами. Программист… Развернуть 

Python 3.4 introduced the asyncio library, and Python 3.5 produced the async and await keywords to use it palatably. These new additions allow so-called asynchronous programming.

All of these new features, which I’ll refer to under the single name Asyncio, have been received by the Python community somewhat warily; a segment of the community seems to see them as complex and difficult to understand. This view is not limited to beginners: several high-profile contributors to the Python community have expressed doubts about the complexity of the Asyncio API in Python, and educators in the community have expressed concern about how best to teach Asyncio to students.

Most people with a few years’ experience with Python have used threads before, and even if you haven’t, you are still likely to have experienced blocking. For example, if you’ve written programs using the wonderful requests library, you will surely have noticed that your program pauses for a bit while it does requests.get(url); this is blocking behavior.

For one-off tasks, this is fine; but if you want to fetch ten thousand URLs simultaneously, it’s going to be difficult to use requests. Large-scale concurrency is one big reason to learn and use Asyncio, but the other big attraction of Asyncio over preemptive threading is safety: it will be much easier for you to avoid race condition bugs with Asyncio.

My goal with this book is to give you a basic understanding of why these new features have been introduced and how to use them in your own projects. More specifically, I aim to provide the following:

- A critical comparison of asyncio and threading for concurrent network programming
- An understanding of the new async/await language syntax
- A general overview of the new asyncio standard library features in Python
- Detailed, extended case studies with code, showing how to use a few of the more popular Asyncio-compatible third-party libraries

We’ll begin with a story that illustrates the shift in thinking that must accompany a transition from threaded to async programming. Then, we’ll take a look at the changes that were made in the Python language itself to accommodate async programming. Finally, we’ll explore some of the ways in which these new features can be used most effectively.

The new Asyncio features are not going to radically change the way you write programs. They provide specific tools that make sense only for specific situations; but in the right situations, asyncio is exceptionally useful. In this book, we’re going to explore those situations and how you can best approach them by using the new Asyncio features.

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Python 3.4 introduced the asyncio library, and Python 3.5 produced the async and await keywords to use it palatably. These new additions allow so-called asynchronous programming.… Развернуть