Book Image

Jupyter Cookbook

By : Dan Toomey
Book Image

Jupyter Cookbook

By: Dan Toomey

Overview of this book

Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Generating a line graph using Python

We will use a basic plot() to show how graphics work in Python to generate a line graph. Then, we will use several other libraries for other interesting visualizations available from Python.

For this example, we are using made-up data to determine the number of births that have the same name and producing a line plot of the data.

How to do it...

We can use this Python script:

import pandas
import matplotlib
%matplotlib inline

baby_name = ['Alice','Charles','Diane','Edward']
number_births = [96, 155, 66, 272]
dataset = list(zip(baby_name,number_births))
df = pandas.DataFrame(data = dataset, columns=['Name', 'Number'])

With the resulting plot as:

This is fictitious data, but it does show a good, clean graphic.

How it works...

pandas is the built-in Python library for dealing with a dataset. matplotlib is the Python library that will plot our data. We use the command %matplotlib inline to have the plot show up in our notebook. Otherwise, Python...