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
Contributors
Preface
Index

Create a Python dashboard


We will use somewhat similar graphics and data derived from the same dataset as before to produce a dashboard based on Python coding.

How to do it...

We have the coding as follows.

We first load all the imports used. We also set up matplotlib to draw graphics inline in our Notebook. We also preconfigure the image sizes:

import pandas as pd
import numpy as np
import statsmodels.formula.api as sm
import matplotlib.pylab as plt
%matplotlib inline
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 15, 6

We read in the data and display the first few records:

data = pd.read_csv("Documents/grapeJuice.csv")
data.head()

The following is the output:

We scale down the sales figures as the other factors are much smaller. Then we produce a scatter plot of the set:

data["sales"] = data["sales"] / 20
plt.plot(data); #suppresses extraneous matplotlib messages

The following is the output:

Next, we produce a regression analysis on the data:

Y = data['sales'][:-1]
X = data[['price...