Библиотека Python разработчика | Книги по питону

Разработка веб-приложений с использованием Фласк на языке Python Гринберг М. В этой книге вы изучите популярный микрофреймворк Flask на пошаговых примерах создания законченного приложения социального блогинга. Автор книги Мигель Гринберг познакомит вас с основными функциональными возможностями фреймворка и покажет, как расширять приложения дополнительными веб-технологиями, такими как поддержка миграции базы данных и взаимодействия с веб-службами. Вместо того чтобы навязывать строгие правила, как это делают другие фреймворки, Flask оставляет за вами свободу принятия решений. Если вы имеете опыт программирования на языке Python, данная книга покажет вам, как можно воспользоваться такой свободой творчества!

Pythonic geodynamics implementations for fast computing Morra, Gabriele This book addresses students and young researchers who want to learn to use numerical modeling to solve problems in geodynamics. Intended as an easy-to-use and self-learning guide, readers only need a basic background in calculus to approach most of the material. The book difficulty increases very gradually, through four distinct parts. The first is an introduction to the Python techniques necessary to visualize and run vectorial calculations. The second is an overview with several examples on classical Mechanics with examples taken from standard introductory physics books. The third part is a detailed description of how to write Lagrangian, Eulerian and Particles in Cell codes for solving linear and non-linear continuum mechanics problems. Finally the last one address advanced techniques like tree-codes, Boundary Elements, and illustrates several applications to Geodynamics.

Algorithmic Trading with Interactive Brokers (Python and C++) Matthew Scarpino Through Interactive Brokers, software developers can write applications that read financial data, scan for contracts, and submit orders automatically. Individuals can now take advantage of the same high-speed decision making and order placement that professional trading firms use.

PYTHON FOR DATA SCIENCE: The Ultimate Beginners’ Guide to Learning Python Data Science Step by Step Ethan Williams This book is a comprehensive guide for beginners to learn Python Programming, especially its application for Data Science. While the lessons in this book are targeted at the absolute beginner to programming, people at various levels of proficiency in Python, or any other programming languages can also learn some basics and concepts of data science. A few Python libraries are introduced, including NumPy, Pandas, Matplotlib, and Seaborn for data analysis and visualisation. To make the lessons more intuitive and relatable, practical examples and applications of each lesson are given.

Django for APIs: Build Web APIs with Python & Django William S. Vincent Take a modern API-first approach to creating 3 different Django back-ends: a Library API, Todo API, and a Blog API with user authentication, permissions, proper documentation, and more. You'll even learn how to connect them to a React front-end for a truly full-stack web application.

Introducing Python: Modern Computing in Simple Packages Bill Lubanovic Easy to understand and fun to read, this updated edition of Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you’ve learned.

Forging Python: Best practices and life lessons developing Python Miki Tebeka Life lessons and best practices developing Python and programming in general brought in unique and old way.

Machine Learning with Spark and Python: Essential Techniques for Predictive Analytic Michael Bowles Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark―a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code.