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 - evaluating history data

In this example, we combine the previous sections to look at some historical data and determine some useful attributes.

The historical data we are using is the guest list for The Jon Stewart Show. A typical record from the data looks like this:

1999,actor,1/11/99,Acting,Michael J. Fox

It contains the year, occupation of the guest, date of appearance, logical grouping of the occupation, and the name of the guest.

For our analysis, we will be looking at number of appearances per year, the most appearing occupation, and the most appearing personality.

We will be using this script:

import pyspark
import csv
import operator
import itertools
import collections
if not 'sc' in globals():
    sc = pyspark.SparkContext()
years = {}
occupations = {}
guests = {}
#The file header contains these column descriptors
with open('daily_show_guests.csv', 'rb') as csvfile:    
    reader = csv.DictReader(csvfile)
    for row...