{"product_id":"data-science-with-python-and-dask","title":"Data Science with Python and Dask","description":"\u003cp\u003e\u003cb\u003eSummary\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eDask 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. And \u003ci\u003eData Science with Python and Dask\u003c\/i\u003e is your guide to using Dask for your data projects without changing the way you work!\u003c\/p\u003e\u003cp\u003ePurchase 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbout the Technology\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eAn 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbout the Book\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003eData Science with Python and Dask\u003c\/i\u003e teaches 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.\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\"\u003eWorking with large, structured and unstructured datasets\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eVisualization with Seaborn and Datashader\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eImplementing your own algorithms\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eBuilding distributed apps with Dask Distributed\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003ePackaging and deploying Dask apps\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eAbout the Reader\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eFor data scientists and developers with experience using Python and the PyData stack.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003col\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003ch4\u003ePART 1 - The Building Blocks of scalable computing\u003c\/h4\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eWhy scalable computing matters\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eIntroducing Dask\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003ch4\u003ePART 2 - Working with Structured Data using Dask DataFrames\u003c\/h4\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eIntroducing Dask DataFrames\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eLoading data into DataFrames\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eCleaning and transforming DataFrames\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eSummarizing and analyzing DataFrames\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eVisualizing DataFrames with Seaborn\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eVisualizing location data with Datashader\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003ch4\u003ePART 3 - Extending and deploying Dask\u003c\/h4\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eWorking with Bags and Arrays\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eMachine learning with Dask-ML\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003eScaling and deploying Dask\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ol\u003e\u003cp\u003e\u003ci class=\"a-icon a-icon-extender-collapse\"\u003e\u003c\/i\u003e\u003cspan style=\"color: rgb(34, 34, 34); font-size: 24px; font-weight: 700;\"\u003eAbout the Author\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eJesse Daniel\u003c\/b\u003e has five years of experience writing applications in Python, including three years working with in the PyData stack (Pandas, NumPy, SciPy, Scikit-Learn). Jesse joined the faculty of the University of Denver in 2016 as an adjunct professor of business information and analytics, where he currently teaches a Python for Data Science course.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Paperback","offer_id":50506285547590,"sku":"9781617295607","price":3939.0,"currency_code":"INR","in_stock":true},{"title":"E-Book","offer_id":50506285580358,"sku":"9781617295607-2","price":4333.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0754\/2797\/1142\/files\/41ZSueOjLaL._SX397_BO1_204_203_200.jpg?v=1790325293","url":"https:\/\/cb-india.com\/products\/data-science-with-python-and-dask","provider":"Computer Bookshop (India) Pvt Ltd","version":"1.0","type":"link"}