Book Image

Learning Jupyter 5 - Second Edition

Book Image

Learning Jupyter 5 - Second Edition

Overview of this book

The 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.
Table of Contents (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Spark primes


We can run a series of numbers through a filter to determine whether each number is prime or not. We can use this script:

import pyspark
if not 'sc' in globals():
    sc = pyspark.SparkContext()

def is_it_prime(number):

    #make sure n is a positive integer
    number = abs(number)

    #simple tests
    if number < 2:
        return False

    #2 is special case
    if number == 2:
        return True

    #all other even numbers are not prime
    if not number & 1:
        return False

    #divisible into it's square root
    for x in range(3, int(number**0.5)+1, 2):
        if number % x == 0:
            return False

    #must be a prime
    return True

# pick a good range
numbers = sc.parallelize(range(100000))

# see how many primes are in that range
print(numbers.filter(is_it_prime).count())

The script generates numbers up to 100000.

 

 

 

 

We then loop over each of the numbers and pass it to our filter. If the filter returns True, we get a record. Then, we just...