Master Python techniques and libraries to reduce run times, efficiently handle huge datasets, and optimize execution for complex machine learning applications.Fast Python is a toolbox of techniques for high performance Python including:Writing efficient pure-Python codeOptimizing the NumPy and pandas librariesRewriting critical code in CythonDesigning persistent data structuresTailoring code for different architecturesImplementing Python GPU computingFast Python 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.Written 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.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyFace 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.About the BookFast Python 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, Fast Python takes a holistic approach to performance, so you’ll see how to optimize the whole system, from code to architecture.What’s InsideRewriting critical code in CythonDesigning persistent data structuresTailoring code for different architecturesImplementing Python GPU computingAbout the ReaderFor intermediate Python programmers familiar with the basics of concurrency.About the AuthorTiago Antao is one of the co-authors of Biopython, a major bioinformatics package written in Python. Table of Contents:PART 1 - FOUNDATIONAL APPROACHES1 An urgent need for efficiency in data processing2 Extracting maximum performance from built-in features3 Concurrency, parallelism, and asynchronous processing4 High-performance NumPyPART 2 - HARDWARE5 Re-implementing critical code with Cython6 Memory hierarchy, storage, and networkingPART 3 - APPLICATIONS AND LIBRARIES FOR MODERN DATA PROCESSING7 High-performance pandas and Apache Arrow8 Storing big dataPART 4 - ADVANCED TOPICS9 Data analysis using GPU computing10 Analyzing big data with Dask