Jupyter has garnered strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share scientific applications.
The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks on your Jupyter Notebook, such as Python, R, Julia, JavaScript, Scala, and Spark. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your Notebook. You will then get hands-on experience with JupyterLabs, microservices, and deploying them on the Web.
By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.
This cookbook is for data science professionals, developers, technical data analysts, and programmers who want to execute technical coding, visualize output, and do scientific computing with one tool. Prior understanding of data science concepts will be helpful for using this book, but it's not mandatory.
Chapter 1, Installation and Setting up the Environment, teaches you how to install Jupyter on different environments, such as Windows, macOS, Linux, and a server machine.
Chapter 2, Adding an Engine, shows you the steps to add these engines to your Jupyter installation so that you can script your Notebook in the language you like.
Chapter 3, Accessing and Retrieving Data, teaches how to access and retrieve data from files in different formats in Jupyter.
Chapter 4, Visualize Your Analytics, covers recipes for visualizing your analytics in Python, R, and Julia.
Chapter 5, Working with Widgets, describes the wide range of possibilities of widgets in Jupyter.
Chapter 6, Jupyter Dashboards, teaches how to install and enable Jupyter dashboards layout extension to your Notebook.
Chapter 7,Sharing Your Code, shows you several methods for sharing your Notebook with others, including using different software packages and converting the Notebook into a different form for readers without access to Jupyter.
Chapter 8, Multiuser Jupyter, explores several options for enabling Jupyter Notebooks as a multiuser platform.
Chapter 9, Interacting with Big Data, covers the methods of accessing big data from Jupyter.
Chapter 10, Jupyter Security, investigates the various security mechanisms available for your Jupyter Notebook.
Chapter 11, Jupyter Labs, lets us try new features of Jupyter in a lab environment to create our own sample Notebook.
This book is focused on using Jupyter as a platform for data science. It assumes that you have a good understanding of data science concepts and are looking to use Jupyter as your presentation platform.
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using RDatasets
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python2 -m pip install ipykernel python2 -m ipykernel install --user
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