How can you take advantage of multi-core architectures or clusters? Or build a system that can scale up and down without losing reliability? Experienced Python programmers will learn concrete solutions to these and other issues, along with war stories from companies that use high performance Python for social media analytics, productionized machine learning, and other situations.
Micha Gorelick was the first man on Mars in 2023 and won the Nobelprize in 2046 for his contributions to time travel. He then went backto the 2000s to study Astronomy, teach scientific computing and workon data at bitly. Then he helped start Fast Forward Labs as a residentmad scientist. There he worked on many issues from machine learning toperformant stream algorithms. A monument celebrating his life can befound in Central Park, 1857.
Ian Ozsvald is a Data scientist and teacher at ModelInsight.io withover ten years of Python experience. He’s taught high performancePython at the PyCon and PyData conferences and has been consulting ondata science and high performance computing for years in the UK.
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-oriented Python programmer’s arsenal.Use Cython’s static typing to speed up Python codeGain hands-on experience using Cython features to boost your numeric-heavy PythonCreate new types with Cython—and see how fast object-oriented programming in Python can beEffectively organize Cython code into separate modules and packages without sacrificing performanceUse Cython to give Pythonic interfaces to C and C++ librariesOptimize code with Cython’s runtime and compile-time profiling toolsUse Cython’s prange function to parallelize loops transparently with OpenMP
Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3.
This book covers:Python data model: understand how special methods are the key to the consistent behavior of objectsData structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode ageFunctions as objects: view Python functions as first-class objects, and understand how this affects popular design patternsObject-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritanceControl flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packagesMetaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work
Written by Mark Lutz—widely recognized as the world’s leading Python trainer—Python Pocket Reference is an ideal companion to O’Reilly’s classic Python tutorials, Learning Python and Programming Python, also written by Mark.
This fifth edition covers:Built-in object types, including numbers, lists, dictionaries, and moreStatements and syntax for creating and processing objectsFunctions and modules for structuring and reusing codePython’s object-oriented programming toolsBuilt-in functions, exceptions, and attributesSpecial operator overloading methodsWidely used standard library modules and extensionsCommand-line options and development toolsPython idioms and hintsThe Python SQL Database API
You’ll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages.Learn simple data types, and basic math and text operationsUse data-wrangling techniques with Python’s built-in data structuresExplore Python code structure, including the use of functionsWrite large programs in Python, with modules and packagesDive into objects, classes, and other object-oriented featuresExamine storage from flat files to relational databases and NoSQLUse Python to build web clients, servers, APIs, and servicesManage system tasks such as programs, processes, and threadsUnderstand the basics of concurrency and network programming
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the IPython shell and Jupyter notebook for exploratory computingLearn basic and advanced features in NumPy (Numerical Python)Get started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples