Book Image

Python Data Visualization Cookbook (Second Edition)

Book Image

Python Data Visualization Cookbook (Second Edition)

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 (16 chapters)
Python Data Visualization Cookbook Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Drawing simple sine and cosine plots


This recipe will go over basics of plotting mathematical functions and several things that are related to math graphs such as writing Greek symbols in labels and on curves.

Getting ready

The most common graph we will use is the line plot command, which draws the given (x,y) coordinates on a figure plot.

How to do it...

We start with computing sine and cosine functions over the same linear interval—from Pi to Pi with 256 points in between and we plot the values for sin(x) and cos(x) over the same plot as shown here:

import matplotlib.pyplot as pl
import numpy as np

x = np.linspace(-np.pi, np.pi, 256, endpoint=True)

y = np.cos(x)
y1 = np.sin(x)

pl.plot(x,y)
pl.plot(x, y1)

pl.show()

That will give us the following graph:

Following this simple plot, we can customize more to give more information and be more precise about axes and boundaries as shown here:

from pylab import *
import numpy as np

# generate uniformly distributed
# 256 points from -pi to pi, inclusive...