Book Image

Learning Jupyter

By : Dan Toomey
Book Image

Learning Jupyter

By: Dan Toomey

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
About the Author
About the Reviewer

Spark text file analysis

In this example, we will look through a news article to determine some basic information from it.

We will be using the following script against the 2600raid news article (from

import pyspark
if not 'sc' in globals():
    sc = pyspark.SparkContext()
sentences = sc.textFile('2600raid.txt') \
    .glom() \
    .map(lambda x: " ".join(x)) \
    .flatMap(lambda x: x.split("."))
bigrams = x:x.split()) \
    .flatMap(lambda x: [((x[i],x[i+1]),1) for i in range(0,len(x)-1)])
frequent_bigrams = bigrams.reduceByKey(lambda x,y:x+y) \
    .map(lambda x:(x[1],x[0])) \

The code reads in the article and splits up the article into sentences as determined by ending with a period. From there, the code maps out the bigrams present. A bigram is a pair of words that appear next to each other. We then sort the list and print out...