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Python Data Visualization Cookbook

Igor Milovanović

As a developer with knowledge of Python you are already in a great position to start using data visualization. This superb cookbook shows you how in plain language and practical recipes, culminating with 3D animations.
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Book Details

ISBN 139781782163367
Paperback280 pages

About This Book

  • 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

Who This Book Is For

Python Data Visualization Cookbook is for developers that already know about Python programming in general. If you have heard about data visualization but you don't know where to start, then this book will guide you from the start and help you understand data, data formats, data visualization, and how to use Python to visualize data.

You will need to know some general programming concepts, and any kind of programming experience will be helpful, but the code in this book is explained almost line by line. You don't need maths for this book, every concept that is introduced is thoroughly explained in plain English, and references are available for further interest in the topic.

Table of Contents

Chapter 1: Preparing Your Working Environment
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
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
Chapter 3: Drawing Your First Plots and Customizing Them
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
Chapter 4: More Plots and Customizations
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
Chapter 5: Making 3D Visualizations
Creating 3D bars
Creating 3D histograms
Animating in matplotlib
Animating with OpenGL
Chapter 6: Plotting Charts with Images and Maps
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
Chapter 7: Using Right Plots to Understand Data
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
Drawing barbs
Making a box and a 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

What You Will Learn

  • 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

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.


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