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  • Book Overview & Buying Python Data Visualization Cookbook (Second Edition)
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Python Data Visualization Cookbook (Second Edition)

Python Data Visualization Cookbook (Second Edition)

By : Igor Milovanovic, Dimitry Foures, Giuseppe Vettigli
4 (6)
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Python Data Visualization Cookbook (Second Edition)

Python Data Visualization Cookbook (Second Edition)

4 (6)
By: Igor Milovanovic, Dimitry Foures, Giuseppe Vettigli

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.
Table of Contents (11 chapters)
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10
Index

Defining plot types – bar, line, and stacked charts


In this recipe, we will present different basic plots and what are they used for. Most of the plots described here are used daily, and some of them present the basis for understanding more advanced concepts in data visualization.

Getting ready

We start with some common charts from the matplotlib.pyplot library with just sample datasets; we start with basic charting and lay down the foundations of the following recipes.

How to do it...

We start by creating a simple plot in IPython. IPython is great because it allows us to interactively change plots and see the results immediately. You need to follow these steps for that:

  1. Start IPython by typing the following code at the command prompt:

    $ ipython
    
  2. Import the necessary functions:

    In [1]: from matplotlib.pyplot import *
    
  3. Then type the matplotlib plot code:

    In [2]: plot([1,2,3,2,3,2,2,1])
    Out[2]: [<matplotlib.lines.Line2D at 0x412fb50>]

The plot should open in a new window displaying the default...

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