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Python Data Visualization Cookbook (Second Edition)

You're reading from   Python Data Visualization Cookbook (Second Edition) Visualize data using Python's most popular libraries

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781784396695
Length 302 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

Preface 1. Preparing Your Working Environment FREE CHAPTER 2. Knowing Your Data 3. Drawing Your First Plots and Customizing Them 4. More Plots and Customizations 5. Making 3D Visualizations 6. Plotting Charts with Images and Maps 7. Using the Right Plots to Understand Data 8. More on matplotlib Gems 9. Visualizations on the Clouds with Plot.ly 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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