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Jupyter Cookbook

You're reading from   Jupyter Cookbook Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788839440
Length 238 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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 Toomey Toomey
Author Profile Icon Toomey
Toomey
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Toc

Table of Contents (17) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Installation and Setting up the Environment FREE CHAPTER 2. Adding an Engine 3. Accessing and Retrieving Data 4. Visualizing Your Analytics 5. Working with Widgets 6. Jupyter Dashboards 7. Sharing Your Code 8. Multiuser Jupyter 9. Interacting with Big Data 10. Jupyter Security 11. Jupyter Labs 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...
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