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Learning Jupyter 5. Explore interactive computing using Python, Java, JavaScript, R, Julia, and JupyterLab - Second Edition

Język publikacji: angielskim
Autor:
Dan Toomey
Learning Jupyter 5. Explore interactive computing using Python, Java, JavaScript, R, Julia, and JupyterLab - Second Edition Dan Toomey - okladka książki

Learning Jupyter 5. Explore interactive computing using Python, Java, JavaScript, R, Julia, and JupyterLab - Second Edition Dan Toomey - okladka książki

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282
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The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples.

The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode.

By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you’ve explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization.
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O autorze książki

Dan Toomey has been developing application software for over 20 years. He has worked in a variety of industries and companies, in roles from sole contributor to VP/CTO-level. For the last few years, he has been contracting for companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corp. Dan has also written R for Data Science, Jupyter for Data Sciences, and the Jupyter Cookbook, all with Packt.

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