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Python Data Visualization Cookbook (Second Edition)

You're reading from   Python Data Visualization Cookbook (Second Edition) Visualize data using Python's most popular libraries

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781784396695
Length 302 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

Preface 1. Preparing Your Working Environment FREE CHAPTER 2. Knowing Your Data 3. Drawing Your First Plots and Customizing Them 4. More Plots and Customizations 5. Making 3D Visualizations 6. Plotting Charts with Images and Maps 7. Using the Right Plots to Understand Data 8. More on matplotlib Gems 9. Visualizations on the Clouds with Plot.ly Index

Importing data from a database


Very often, our work on data analysis and visualization is at the consumer end of the data pipeline. We most often use the already produced data rather than producing the data ourselves. A modern application, for example, holds different datasets inside relational databases (or other databases like MongoDB), and we use these databases to produce beautiful graphs.

This recipe will show you how to use SQL drivers from Python to access data.

We will demonstrate this recipe using a SQLite database because it requires the least effort to set up, but the interface is similar to most other SQL-based database engines (MySQL and PostgreSQL). There are, however, differences in the SQL dialect that those database engines support. This example uses simple SQL language and should be reproducible on most common SQL database engines.

Getting ready

To be able to execute this recipe, we need to install the SQLite library as shown here:

$ sudo apt-get install sqlite3

Python support...

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