#### 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.
Learning Jupyter
Credits
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
Jupyter and Big Data

## Estimate Pi

We can use map/reduce to estimate the Pi. Suppose we have code like this:

```import pyspark
import random
if not 'sc' in globals():
sc = pyspark.SparkContext()
NUM_SAMPLES = 1000
def sample(p):
x,y = random.random(),random.random()
return 1 if x*x + y*y < 1 else 0
count = sc.parallelize(xrange(0, NUM_SAMPLES)) \
.map(sample) \
.reduce(lambda a, b: a + b)
print "Pi is roughly %f" % (4.0 * count / NUM_SAMPLES)
```

This code has the same preamble. We are using the `random` Python package. There is a constant for the number of samples to attempt.

We are building an RDD called `count`. We call upon the `parallelize` function to split up this process over the nodes available. The code just maps the result of the `sample` function call. Finally, we reduce the generated map set by adding all the samples.

The `sample` function gets two random numbers and returns a `1` or a `0` depending on where the two numbers end up in size. We are looking for random numbers in a small...