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High Performance Python
Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python’s implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productioni...
High Performance Python
"If you're an experienced Python programmer, High Performance Python will guide you through the various routes of code optimization. You'll learn how to use smarter algorithms and leverage peripheral technologies, such as numpy, cython, cpython, and various multi-threaded and multi-node strategies. There's a lack of good learning and reference material available if you want to learn Python for highly computational tasks. Because of it, fields from physics to biology and systems infrastructure to data science are hitting barriers. They need the fast prototyping nature of Python, but too few people know how to wield it"--Publisher's description
Getting Started with D3
Learn how to create beautiful, interactive, browser-based data visualizations with the D3 JavaScript library. This hands-on book shows you how to use a combination of JavaScript and SVG to build everything from simple bar charts to complex infographics. You'll learn how to use basic D3 tools by building visualizations based on real data from the New York Metropolitan Transit Authority. Using historical tables, geographical information, and other data, you'll graph bus breakdowns and accidents and the percentage of subway trains running on time, among other examples. By the end of the book, you'll be prepared to build your own web-based data visualizations with D3. Join a dataset with element...
High Performance Python
Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python’s implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productioni...
Cython
Build software that combines Python’s expressivity with the performance and control of C (and C++). It’s possible with Cython, the compiler and hybrid programming language used by foundational packages such as NumPy, and prominent in projects including Pandas, h5py, and scikits-learn. In this practical guide, you’ll learn how to use Cython to improve Python’s performance—up to 3000x— and to wrap C and C++ libraries in Python with ease. Author Kurt Smith takes you through Cython’s capabilities, with sample code and in-depth practice exercises. If you’re just starting with Cython, or want to go deeper, you’ll learn how this language is an essential part of any performance-ori...
Software Engineering for Data Scientists
Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to: Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more
Architecture Patterns with Python
As Python continues to grow in popularity, projects are becoming larger and more complex. Many Python developers are 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
Using Asyncio in Python
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 AP...
AI for Everyone: benefitting from and building trust in the technology
If governed adequately, AI (artificial intelligence) has the potential to benefit humankind enormously. However, if mismanaged, it also has the potential to harm humanity catastrophically. The title of this book reflects the belief that access to the benefits of AI, awareness about the nature of the technology, governance of the technology and its development process with a focus on responsible development, should be transparent, open, understood by and accessible to all people regardless of their geographic, generational, economic, cultural and/or other social background. The book is the result of a discussion series organized by the Association of Pacific Rim Universities (APRU) which was financially supported by Google.
Fluent Python
Don't waste time bending Python to fit patterns you've learned in other languages. Python's simplicity lets you become productive quickly, but often this means you aren't using everything the language has to offer. With the updated edition of this hands-on guide, you'll learn how to write effective, modern Python 3 code by leveraging its best ideas. Discover and apply idiomatic Python 3 features beyond your past experience. Author Luciano Ramalho guides you through Python's core language features and libraries and teaches you how to make your code shorter, faster, and more readable. Complete with major updates throughout, this new edition features five parts that work as five short books wit...
