Python Data Visualization Cookbook (Second Edition)

Python Data Visualization Cookbook (Second Edition)

Overview of this book

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 will 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 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. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.

Python Data Visualization Cookbook Second Edition

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Preface

Free Chapter

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

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 and manipulating data with Pandas

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

Drawing Your First Plots and Customizing Them

Introduction

Defining plot types – bar, line, and stacked charts

Drawing simple sine and cosine plots

Defining axis lengths and limits

Defining plot line styles, properties, and format strings

Setting ticks, labels, and grids

Adding legends and annotations

Moving spines to the center

Making histograms

Making bar charts with error bars

Making pie charts count

Plotting with filled areas

Making stacked plots

Drawing scatter plots with colored markers

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 filesystem tree using a polar bar

Customizing matplotlib with style

Making 3D Visualizations

Introduction

Creating 3D bars

Creating 3D histograms

Animating in matplotlib

Animating with OpenGL

Plotting Charts with Images and Maps

Introduction

Processing images with PIL

Plotting with images

Displaying images with other plots in the figure

Plotting data on a map using Basemap

Plotting data on a map using the Google Map API

Generating CAPTCHA images

Using the Right Plots to Understand Data

Introduction

Understanding logarithmic plots

Understanding spectrograms

Creating stem plot

Drawing streamlines of vector flow

Using colormaps

Using scatter plots and histograms

Plotting the cross correlation between two variables

Importance of autocorrelation

Making pie charts count

Pie charts are special in many ways, the most important being that the dataset they display must sum up to 100 percent or they are just not valid.

Getting ready

Pie charts represent numerical proportions, where the arc length of each segment is proportional to the quantity it represents.

They are compact and can look very aesthetically pleasing, but they have been criticized as they can be hard to compare. Another property of pie charts that does not work in their best interest is that pie charts are presented in a specific angle (perspective) and segments use certain colors that can skew our perception and influence our conclusion about information presented.

What we will show here is different ways to use pie charts to present data.

How to do it...

Here, we create a so-called exploded pie chart:

from pylab import *

# make a square figure and axes
figure(1, figsize=(6,6))
ax = axes([0.1, 0.1, 0.8, 0.8])

# the slices will be ordered
# and plotted counter-clockwise.
labels...

Python Data Visualization Cookbook (Second Edition)

Python Data Visualization Cookbook (Second Edition)

Overview of this book

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Current Title:

Python Data Visualization Cookbook (Second Edition)

Making pie charts count

Getting ready

How to do it...