{"product_id":"fast-python","title":"Fast Python","description":"\u003cp\u003e\u003cstrong\u003e\u003ci\u003eFast Python\u003c\/i\u003e is a toolbox of techniques for high performance Python including:\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eWriting efficient pure-Python code\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eOptimizing the NumPy and pandas libraries\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eRewriting critical code in Cython\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eDesigning persistent data structures\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eTailoring code for different architectures\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eImplementing Python GPU computing\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003ci\u003eFast Python\u003c\/i\u003e is your guide to optimizing every part of your Python-based data analysis process, from the pure Python code you write to managing the resources of modern hardware and GPUs. You'll learn to rewrite inefficient data structures, improve underperforming code with multithreading, and simplify your datasets without sacrificing accuracy.\u003c\/p\u003e\u003cp\u003eWritten for experienced practitioners, this book dives right into practical solutions for improving computation and storage efficiency. You'll experiment with fun and interesting examples such as rewriting games in Cython and implementing a MapReduce framework from scratch. Finally, you'll go deep into Python GPU computing and learn how modern hardware has rehabilitated some former antipatterns and made counterintuitive ideas the most efficient way of working.\u003c\/p\u003e\u003cp\u003ePurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbout the Technology\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eFace it. Slow code will kill a big data project. Fast pure-Python code, optimized libraries, and fully utilized multiprocessor hardware are the price of entry for machine learning and large-scale data analysis. What you need are reliable solutions that respond faster to computing requirements while using less resources, and saving money.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbout the Book\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003eFast Python\u003c\/i\u003e is a toolbox of techniques for speeding up Python, with an emphasis on big data applications. Following the clear examples and precisely articulated details, you’ll learn how to use common libraries like NumPy and pandas in more performant ways and transform data for efficient storage and I\/O. More importantly, \u003ci\u003eFast Python\u003c\/i\u003e takes a holistic approach to performance, so you’ll see how to optimize the whole system, from code to architecture\u003ci\u003e.\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eWhat’s Inside\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eRewriting critical code in Cython\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eDesigning persistent data structures\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eTailoring code for different architectures\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eImplementing Python GPU computing\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eAbout the Reader\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eFor intermediate Python programmers familiar with the basics of concurrency.\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"color: rgb(34, 34, 34); font-size: 24px; font-weight: 700;\"\u003eAbout the Author\u003c\/span\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTiago Antao\u003c\/b\u003e works in the field of genetics, analyzing very large datasets and implementing complex algorithms to process the data. He leverages Python with all its libraries to do scientific computing and data engineering tasks. He is one of the co-authors of Biopython, a major bioinformatics package written in Python. He holds a BE in informatics and a PhD in bioinformatics.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTable of Contents:\u003cbr\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003cstrong\u003ePART 1 - FOUNDATIONAL APPROACHES\u003c\/strong\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e1 An urgent need for efficiency in data processing\u003cbr\u003e2 Extracting maximum performance from built-in features\u003cbr\u003e3 Concurrency, parallelism, and asynchronous processing\u003cbr\u003e4 High-performance NumPy\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePART 2 - HARDWARE\u003cbr\u003e\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e5 Re-implementing critical code with Cython\u003cbr\u003e6 Memory hierarchy, storage, and networking\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePART 3 - APPLICATIONS AND LIBRARIES FOR MODERN DATA PROCESSING\u003cbr\u003e\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e7 High-performance pandas and Apache Arrow\u003cbr\u003e8 Storing big data\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePART 4 - ADVANCED TOPICS\u003cbr\u003e\u003c\/strong\u003e9 Data analysis using GPU computing\u003cbr\u003e10 Analyzing big data with Dask\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEbook License\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eEnd-User Warranty And License Agreement\u003c\/p\u003e\u003cp\u003e1. 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