Over 60 recipes that will enable you to learn how to create attractive visualizations using Python's most popular libraries
Overview
Learn how to set up an optimal Python environment for data visualization
Understand the topics such as importing data for visualization and formatting data for visualization
Understand the underlying data and how to use the right visualizations
In Detail
Today, data visualization is a hot topic as a direct result of the vast amount of data created every second. Transforming that data into information is a complex task for data visualization professionals, who, at the same time, try to understand the data and objectively transfer that understanding to others. This book is a set of practical recipes that strive to help the reader get a firm grasp of the area of data visualization using Python and its popular visualization and data libraries.
Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts.
Python Data Visualization Cookbook starts by showing you how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. During the book, we go from simple plots and charts to more advanced ones, thoroughly explaining why we used them and how not to use them. As we go through the book, we will also discuss 3D diagrams. We will peep into animations just to show you what it takes to go into that area. Maps are irreplaceable for displaying geo-spatial data, so we also show you how to build them. In the last chapter, we show you how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.
This book will help those who already know how to program in Python to explore a new field – one of data visualization. As this book is all about recipes that explain how to do something, code samples are abundant, and they are followed by visual diagrams and charts to help you understand the logic and compare your own results with what is explained in the book.
What you will learn from this book
Install and use iPython
Use Python's virtual environments
Install and customize NumPy and matplotlib
Draw common and advanced plots
Visualize data using maps
Create 3D animated data visualizations
Import data from various formats
Export data from various formats
Approach
This book is written in a Cookbook style targeted towards an advanced audience. It covers the advanced topics of data visualization in Python.
Author(s): Igor Milovanović
Publisher: Packt Publishing
Year: 2013
Language: English
Pages: 280
Cover
Copyright
Credits
About the Author
About the Reviewers
www.PacktPub.com
Table of Contents
Preface
Chapter 1: Preparing Your Working Environment
Introduction
Installing matplotlib, NumPy, and SciPy
Installing virtualenv and virtualenvwrapper
Installing matplotlib on Mac OS X
Installing matplotlib on Windows
Installing Python Imaging Library (PIL) for image processing
Installing a requests module
Customizing matplotlib's parameters in code
Customizing matplotlib's parameters per project
Chapter 2
: Knowing Your Data
Introduction
Importing data from CSV
Importing data from Microsoft Excel files
Importing data from fixed-width data files
Importing data from tab-delimited files
Importing data from a JSON resource
Exporting data to JSON, CSV, and Excel
Importing data from a database
Cleaning up data from outliers
Reading files in chunks
Reading streaming data sources
Importing image data into NumPy arrays
Generating controlled random datasets
Smoothing the noise in real-world data
Chapter 3
: Drawing Your
First Plots and Customizing Them
Introduction
Defining plot types – bar, line, and stacked charts
Drawing simple sine and cosine plot
Defining axis lengths and limits
Defining plot line styles, properties, and format strings
Setting ticks, labels, and grids
Adding legend and annotations
Moving spines to the center
Making histograms
Making bar charts with error bars
Making pie charts count
Plotting with filled areas
Drawing scatter plots with colored markers
Chapter 4
: More Plots and Customizations
Introduction
Setting the transparency and size of axis labels
Adding a shadow to the chart line
Adding a data table to the figure
Using subplots
Customizing grids
Creating contour plots
Filling an under-plot area
Drawing polar plots
Visualizing the file system tree using a polar bar
Chapter 5
: Making 3D Visualizations
Introduction
Creating 3D bars
Creating 3D histograms
Animating in matplotlib
Animating with OpenGL
Chapter 6
: Plotting Charts with Images and Maps
Introduction
Processing images with PIL
Plotting with images
Displaying image with other plots in the figure
Plotting data on a map using Basemap
Plotting data on a map using Google Map API
Generating CAPTCHA images
Chapter 7
: Using Right Plots to Understand Data
Introduction
Understanding logarithmic plots
Understanding spectrograms
Creating a stem plot
Drawing streamlines of vector flow
Using colormaps
Using scatter plots and histograms
Plotting the cross-correlation between two variables
Importance of autocorrelation
Chapter 8
: More on
Matplotlib Gems
Introduction
Drawing barbs
Making a box and whisker plot
Making Gantt charts
Making errorbars
Making use of text and font properties
Rendering text with LaTeX
Understanding the difference between pyplot and OO API
Index