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

Python graphics in Jupyter


How do Python graphics work in Jupyter?

I started another view for this named Python graphics so as to distinguish the work.

If we were to build a sample dataset of baby names and the number of births in a year of that name, we could then plot the data.

The Python coding is simple:

import pandas 
import matplotlib 
 
%matplotlib inline 
 
# define our two columns of data 
baby_name = ['Alice','Charles','Diane','Edward'] 
number_births = [96, 155, 66, 272] 
 
# create a dataset from the to sets 
dataset = list(zip(baby_name,number_births)) 
dataset 
 
# create a Python dataframe from the dataset 
df = pandas.DataFrame(data = dataset, columns=['Name', 'Number']) 
df 
 
# plot the data 
df['Number'].plot() 

The steps for the script are as follows:

  1. Import the graphics library (and data library) we need
  2. Define our data
  3. Convert the data into a format that allows for an easy graphical display
  4. Plot the data

We would expect a resultant graph of the number of births by baby name.

Taking...