Unlocking Python
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Unlocking Python
A fun and practical guide to learning Python with a special focus on data science, web scraping, and web applications In Unlocking Python: A Comprehensive Guide for Beginners, veteran software engineer, educator, and author Ryan Mitchell delivers an intuitive, engaging, and practical roadmap to Python programming. The author walks you through the vocabulary, tools, foundational knowledge, and occasional pop-culture references you'll need to hone your skills with this popular programming language. You'll learn how to install and run Python on your own machine, get up and coding with the language quickly, and best practices for programming both independently and in the workplace. You'll also find: Key concepts in computer and data science explained from the ground up Advanced Python topics such as logging, unit testing, multiprocessing, and interacting with databases. Introductions to some of Python's most popular third-party libraries: Flask, Django, Scrapy, Scikit-Learn, Numpy, and Pandas Amusing anecdotes from the trenches of industry Perfect for tech-savvy professionals at any stage of their careers who are interested in diving into Python programming. Unlocking Python is also a must-read for readers who work in a technical role but are interested in getting more directly involved with programming, as well as non-Python programmers who want to apply their technical skill to a new language.
Mastering Python Concurrency and Parallelism: Unlock the Secrets of Expert-Level Skills
Unlock the full potential of your Python programming with "Mastering Python Concurrency and Parallelism: Unlock the Secrets of Expert-Level Skills." This comprehensive guide is crafted for experienced developers ready to elevate their expertise in concurrent and parallel computing. Through detailed exploration of threading, asyncio, and multiprocessing, you'll gain the insights needed to optimize your software for today's multi-core processors, ensuring peak performance and efficiency in your applications. Navigate through the nuanced world of Python concurrency with expertly organized chapters covering essential concepts, real-world applications, and advanced techniques. From demystifying the Global Interpreter Lock (GIL) to designing sophisticated concurrent data structures, this book offers unparalleled clarity and practical knowledge. Each chapter builds on the previous one, providing a seamless learning curve that empowers you to master the intricacies of writing robust, scalable concurrent code. Whether you're developing high-demand web servers, crafting precise financial models, or engineering responsive IoT systems, this book equips you with the tools to succeed. Real-world case studies and best practices accentuate the theoretical, allowing you to apply concepts to your unique challenges. Redefine your development capabilities and achieve new heights in software performance with this essential resource for mastering concurrency in Python.
Python and HDF5
Author: Andrew Collette
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2013-10-21
Gain hands-on experience with HDF5 for storing scientific data in Python. This practical guide quickly gets you up to speed on the details, best practices, and pitfalls of using HDF5 to archive and share numerical datasets ranging in size from gigabytes to terabytes. Through real-world examples and practical exercises, you’ll explore topics such as scientific datasets, hierarchically organized groups, user-defined metadata, and interoperable files. Examples are applicable for users of both Python 2 and Python 3. If you’re familiar with the basics of Python data analysis, this is an ideal introduction to HDF5. Get set up with HDF5 tools and create your first HDF5 file Work with datasets by learning the HDF5 Dataset object Understand advanced features like dataset chunking and compression Learn how to work with HDF5’s hierarchical structure, using groups Create self-describing files by adding metadata with HDF5 attributes Take advantage of HDF5’s type system to create interoperable files Express relationships among data with references, named types, and dimension scales Discover how Python mechanisms for writing parallel code interact with HDF5