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Python Data Visualization Cookbook

You're reading from   Python Data Visualization Cookbook 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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Product type Paperback
Published in Nov 2013
Publisher Packt
ISBN-13 9781782163367
Length 280 pages
Edition 1st Edition
Languages
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Author (1):
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Igor Milovanovic Igor Milovanovic
Author Profile Icon Igor Milovanovic
Igor Milovanovic
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Table of Contents (15) Chapters Close

Python Data Visualization Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
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 Right Plots to Understand Data 8. More on matplotlib Gems Index

Customizing matplotlib's parameters in code


The Library we will use the most throughout this book is matplotlib; it provides the plotting capabilities. Default values for most properties are already set inside the configuration file for matplotlib, called.rc file. This recipe describes how to modify matplotlib properties from our application code.

Getting ready

As we already said, matplotlib configuration is read from a configuration file. This file provides a place to set up permanent default values for certain matplotlib properties, well, for almost everything in matplotlib.

How to do it...

There are two ways to change parameters during code execution: using the dictionary of parameters (rcParams) or calling the matplotlib.rc() command. The former enables us to load already existing dictionary into rcParams, while the latter enables a call to a function using tuple of keyword arguments.

If we want to restore the dynamically changed parameters, we can use matplotlib.rcdefaults() call to restore the standard matplotlib settings.

The following two code samples illustrate previously explained behaviors:

Example for matplotlib.rcParams:

import matplotlib as mp
mpl.rcParams['lines.linewidth'] = 2
mpl.rcParams['lines.color'] = 'r'

Example for the matplotlib.rc() call:

import matplotlib as mpl
mpl.rc('lines', linewidth=2, color='r')

Both examples are semantically the same. In the second sample, we define that all subsequent plots will have lines with line width of 2 points. The last statement of the previous code defines that the color of every line following this statement will be red, unless we override it by local settings. See the following example:

import matplotlib.pyplot as plt
import numpy as np

t = np.arange(0.0, 1.0, 0.01)

s = np.sin(2 * np.pi * t)
# make line red
plt.rcParams['lines.color'] = 'r'
plt.plot(t,s)

c = np.cos(2 * np.pi * t)
# make line thick
plt.rcParams['lines.linewidth'] = '3
plt.plot(t,c)

plt.show()

How it works...

First, we import matplotlib.pyplot and NumPy to allow us to draw sine and cosine graphs. Before plotting the first graph, we explicitly set line color to red using plt.rcParams['lines.color'] = 'r'.

Next, we go to the second graph (cosine function), and explicitly set line width to 3 points using plt.rcParams['lines.linewidth'] = '3'.

If we want to reset specific settings, we should call matplotlib.rcdefaults().

You have been reading a chapter from
Python Data Visualization Cookbook
Published in: Nov 2013
Publisher: Packt
ISBN-13: 9781782163367
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