Data Science with Python and Dask

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Dask is a native parallel analytics tool that integrates seamlessly with existing libraries like Pandas, NumPy, and Scikit-Learn, allowing you to work with large datasets using familiar tools. This guide shows how to leverage Dask for data projects without altering your workflow. The print book purchase includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications, with registration instructions provided inside. Efficient data pipelines are crucial for successful data science projects. Dask offers a flexible library for parallel computing in Python, enabling intuitive workflows for ingesting and analyzing large, distributed datasets. It features dynamic task scheduling and parallel collections that enhance the capabilities of NumPy, Pandas, and Scikit-Learn, allowing users to scale their code from a single laptop to a cluster of hundreds of machines effortlessly. The book teaches you to build scalable projects capable of handling massive datasets. You'll explore the Dask framework, analyze the NYC Parking Ticket database, and use DataFrames for streamlined processes. You'll also create machine learning models with Dask-ML, develop interactive visualizations, and build clusters using AWS and Docker. This resource is intended for data scientists and developers familiar with Python and the PyData stack. The author, Jesse Daniel, is an experienced Python developer and educator, leading a team of data scienti