Python Data Visualization Cookbook

Python Data Visualization Cookbook

By : Igor Milovanovic

Buy this Book

Python Data Visualization Cookbook

By: Igor Milovanovic

Buy this Book

Overview of this book

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 ‚Äì 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.

Python Data Visualization Cookbook

Credits

About the Author

About the Reviewers

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 datafiles

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

Drawing Your First Plots and Customizing Them

Introduction

Defining plot types – bar, line, and stacked charts

Drawing a simple sine and cosine plot

Defining axis lengths and limits

Defining plot line styles, properties, and format strings

Setting ticks, labels, and grids

Adding a 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

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

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 an 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

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

Reading files in chunks

Python is very good at handling reading and writing files or file-like objects. For example, if you try to load big files, say a few hundred MB, assuming you have a modern machine with at least 2 GB of RAM, Python will be able to handle it without any issue. It will not try to load everything at once, but play smart and load it as needed.

So even with decent file sizes, doing something as simple as the following code will work straight out of the box:

with open('/tmp/my_big_file', 'r') as bigfile:
    for line in bigfile:
        # line based operation, like 'print line'

But if we want to jump to a particular place in the file or do other nonsequential reading, we will need to use the handcrafted approach and use IO functions such as seek(), tell(), read(), and next() that allow enough flexibility for most users. Most of these functions are just bindings to C implementations (and are OS-specific), so they are fast, but their behavior can vary based on the OS we are running...

Python Data Visualization Cookbook

By : Igor Milovanovic

Python Data Visualization Cookbook

By: Igor Milovanovic

Overview of this book

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Python Data Visualization Cookbook

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