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Practical Data Analysis Cookbook

You're reading from   Practical Data Analysis Cookbook Over 60 practical recipes on data exploration and analysis

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781783551668
Length 384 pages
Edition 1st Edition
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Author (1):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Preface 1. Preparing the Data FREE CHAPTER 2. Exploring the Data 3. Classification Techniques 4. Clustering Techniques 5. Reducing Dimensions 6. Regression Methods 7. Time Series Techniques 8. Graphs 9. Natural Language Processing 10. Discrete Choice Models 11. Simulations Index

Creating multivariate charts

What the previous recipe showed is that only a handful of houses with less than two bedrooms were sold in the Sacramento area. In the Visualizing the interactions between features recipe, we used D3.js to present the relationship between the price and floor area. In this recipe, we will add an another dimension to the two-dimensional chart, the number of bedrooms.

Getting ready

To execute this recipe, you will need the pandas, SQLAlchemy, and Bokeh modules installed. No other prerequisites are required.

How to do it…

Bokeh is a module that marries Seaborn and D3.js: it produces visually appealing data visualizations (just like Seaborn) and allows you to interact with the chart, using D3.js in the backend of the produced HTML file. Producing a similar chart in D3.js would require more coding. The source code for this recipe is contained in the data_multivariate_charts.py file:

# prepare the query to extract the data from the database
query = 'SELECT beds...
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