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Jupyter for Data Science

You're reading from   Jupyter for Data Science Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781785880070
Length 242 pages
Edition 1st Edition
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Table of Contents (11) Chapters Close

Preface 1. Jupyter and Data Science FREE CHAPTER 2. Working with Analytical Data on Jupyter 3. Data Visualization and Prediction 4. Data Mining and SQL Queries 5. R with Jupyter 6. Data Wrangling 7. Jupyter Dashboards 8. Statistical Modeling 9. Machine Learning Using Jupyter 10. Optimizing Jupyter Notebooks

Visualizing glyph ready data


A glyph is a symbol. In this section, we are looking to display glyphs at different points in a graph rather than the standard dot as the glyph should provide more visual information to the viewer. Often there is an attribute about a data point that can be used to turn the data point into a useful glyph, as we will see in the following examples.

The ggplot2 package is useful for visualizing data in a variety of ways. ggplot is described as a plotting system for R. We will look at an example that displays volcano data points across the globe. I used the information from the National Center for Environmental Information at https://www.ngdc.noaa.gov/nndc. I selected volcano information post-1964.

This generated a set of data that I copied into a local CSV file:

#read in the CSV file as available as described previouslyvolcanoes = read.csv("volcanoes.csv")head(volcanoes)

If we just plot out the points on a world map we can see where the volcanoes are located. We are...

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