Mastering Large Datasets with Python: Parallelize and Distribute Your Python Code

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With an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You'll get familiar with Python's functional built-ins like the functools operator and itertools modules, as well as the tools library. Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you'll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level! Key features• An introduction to functional and parallel programming • Data science workflow • Profiling code for better performance • Fulfilling different quality objectives for a single unifying task • Python multiprocessing • Practical exercises including full-scale distributed applications AudienceReaders should have intermediate Python programming skills. About the technologyPython is a data scientist's dream-come-true, thanks to readily available libraries that support tasks like data analysis, machine learning, visualization, and numerical computing.