Learning Jupyter 5

Create and share livecode equations visualizations and explanatory text in both a single document and a web browser with JupyterKey FeaturesLearn how to use Jupyter 5.x features such as cell tagging and attractive table stylesLeverage big data tools and datasets with different Python packagesExplore multiple-user Jupyter Notebook serversBook DescriptionThe 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.What you will learnInstall and run the Jupyter Notebook system on your machineImplement programming languages such as R Python Julia and JavaScript with the Jupyter NotebookUse interactive widgets to manipulate and visualize data in real timeStart sharing your Notebook with colleaguesInvite your colleagues to work with you on the same NotebookOrganize your Notebook using Jupyter namespacesAccess big data in Jupyter for dealing with large datasets using SparkWho this book is forLearning Jupyter 5 is for developers data scientists machine learning users and anyone working on data analysis or data science projects across different teams. Data science professionals will also find this book useful for performing technical and scientific computing collaboratively.