#### 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

## Python random numbers in Jupyter

For many analyses, we are interested in calculating repeatable results. However, a lot of analysis relies on random numbers being used. In Python, you can set the seed for the random number generator to achieve repeatable results with the `random_seed()` function.

In this example, we simulate rolling a pair of dice and looking at the outcome.

The script we are using is this:

```import pylab
import random
random.seed(113)
samples = 1000
dice = []
for i in range(samples):
total = random.randint(1,6) + random.randint(1,6)
dice.append(total)
pylab.hist(dice, bins= pylab.arange(1.5,12.6,1.0))
pylab.show()
```

Once we have the script in Jupyter and execute it, we have this result:

I had added some more statistics. I'm not sure I would have counted on such a high standard deviation. If we increased the number of samples, this would increase.

The resulting graph was opened in a new window, much as it would be if you ran this script in another Python development environment...