Data science at scale with python and dask

Description

SummaryDask is a native parallel analytics tool designed to integrate seamlessly with the libraries you're already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. AndData Science with Python and Daskis your guide to using Dask for your data projects without changing the way you work!Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. You'll find registration instructions inside the print book.About the TechnologyAn efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease.About the BookData Science with Python and Daskteaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you'll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you'll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker.What's insideWorking with large, structured and unstructured datasetsVisualization with Seaborn and DatashaderImplementing your own algorithmsBuilding distributed apps with Dask DistributedPackaging and deploying Dask appsAbout the ReaderFor data scientists and developers with experience using Python and the PyData stack.About the AuthorJesse Danielis an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company.Table of ContentsPART 1 - The Building Blocks of scalable computingWhy scalable computing mattersIntroducing DaskPART 2 - Working with Structured Data using Dask DataFramesIntroducing Dask DataFramesLoading data into DataFramesCleaning and transforming DataFramesSummarizing and analyzing DataFramesVisualizing DataFrames with SeabornVisualizing location data with DatashaderPART 3 - Extending and deploying DaskWorking with Bags and ArraysMachine learning with Dask-MLScaling and deploying Dask