Python 2 And 3 Compatibility
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Python 2 and 3 Compatibility
Discover clean ways to write code that will run on both Python 2 and 3.This book is tutorial-oriented with detailed examples of how to convert existing Python 2-compatible code to code that will run reliably on both Python 2 and 3. Although Python 3 is considered the future of Python, Python 2.x will be maintained for several more years, alongside Python 3, which is not backwards compatible. For those who need to support both versions, this book guides you through the process. Python 2 and 3 Compatibility explains the syntactical differences between Python 2 and 3, and how to use Python packages Python-Future and Six to implement neutral compatibility. Developers working on either small, medium, or large projects will appreciate the author's clear explanations, detailed examples, and clean techniques to help them extend support for both versions to their existing Python 2-compatible projects. What You'll Learn Understand the syntactical differences between Python 2 and 3 Use the Six and Future libraries Review the new features in Python 3 Choose which Python versions to support when doing neutral support Decide on whether to port or provide support for both versions Who This Book Is For Professional Python developers and enthusiasts that want to implement Python 3 support for their existing Python 2 compatible code.
Professional Python
Master the secret tools every Python programmer needs to know Professional Python goes beyond the basics to teach beginner- and intermediate-level Python programmers the little-known tools and constructs that build concise, maintainable code. Design better architecture and write easy-to-understand code using highly adoptable techniques that result in more robust and efficient applications. Coverage includes Decorators, Context Managers, Magic Methods, Class Factories, Metaclasses, Regular Expressions, and more, including advanced methods for unit testing using asyncio and CLI tools. Each topic includes an explanation of the concept and a discussion on applications, followed by hands-on tutorials based on real-world scenarios. The "Python 3 first" approach covers multiple current versions, while ensuring long-term relevance. Python offers many tools and techniques for writing better code, but often confusing documentation leaves many programmers in the dark about how to use them. This book shines a light on these incredibly useful methods, giving you clear guidance toward building stronger applications. Learn advanced Python functions, classes, and libraries Utilize better development and testing tools Understand the "what," "when," "why," and "how" More than just theory or a recipe-style walk-through, this guide helps you learn — and understand — these little-known tools and techniques. You'll streamline your workflow while improving the quality of your output, producing more robust applications with cleaner code and stronger architecture. If you're ready to take your Python skills to the next level, Professional Python is the invaluable guide that will get you there.
Large Scale Machine Learning with Python
Author: Bastiaan Sjardin
language: en
Publisher: Packt Publishing Ltd
Release Date: 2016-08-03
Learn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. What You Will Learn Apply the most scalable machine learning algorithms Work with modern state-of-the-art large-scale machine learning techniques Increase predictive accuracy with deep learning and scalable data-handling techniques Improve your work by combining the MapReduce framework with Spark Build powerful ensembles at scale Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine In Detail Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. Style and Approach This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly. Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.