In this chapter, we used Spark functionality via Python coding for Jupyter. First, we installed the Spark additions to Jupyter on a Windows machine and a Mac machine. We wrote an initial script that just read lines from a text file. We went further and determined the word counts in that file. We added sorting to the results. There was a script to estimate Pi. We evaluated web log files for anomalies. We determined a set of prime numbers. And we evaluated a text stream for some characteristics.
Learning Jupyter
By :
Learning Jupyter
By:
Overview of this book
Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It 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, machine learning, and much more.
This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we’ll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode.
Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book.
Table of Contents (16 chapters)
Learning Jupyter
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Introduction to Jupyter
Jupyter Python Scripting
Jupyter R Scripting
Jupyter Julia Scripting
Jupyter JavaScript Coding
Interactive Widgets
Sharing and Converting Jupyter Notebooks
Multiuser Jupyter Notebooks
Jupyter Scala
Customer Reviews