Reader small image

You're reading from  Python Data Visualization Cookbook

Product typeBook
Published inNov 2013
Reading LevelIntermediate
PublisherPackt
ISBN-139781782163367
Edition1st Edition
Languages
Right arrow
Author (1)
Igor Milovanovic
Igor Milovanovic
author image
Igor Milovanovic

Igor Milovanović is an experienced developer, with strong background in Linux system knowledge and software engineering education. He is skilled in building scalable data-driven distributed software rich systems. An evangelist for high-quality systems design, he has a strong interest in software architecture and development methodologies. Igor is always committed to advocating methodologies that promote high-quality software, such as test-driven development, one-step builds, and continuous integration. He also possesses solid knowledge of product development. With field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa. Igor is most grateful to his girlfriend for letting him spend hours on work instead with her and being an avid listener to his endless book monologues. He thanks his brother for being the strongest supporter. He is also thankful to his parents for letting him develop in various ways to become a person he is today.
Read more about Igor Milovanovic

Right arrow

Drawing a simple sine and cosine plot


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:

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:

from pylab import *
import numpy as np

# generate uniformly distributed
# 256 points from -pi to pi, inclusive
x = np.linspace(-np...
lock icon
The rest of the page is locked
Previous PageNext Page
You have been reading a chapter from
Python Data Visualization Cookbook
Published in: Nov 2013Publisher: PacktISBN-13: 9781782163367

Author (1)

author image
Igor Milovanovic

Igor Milovanović is an experienced developer, with strong background in Linux system knowledge and software engineering education. He is skilled in building scalable data-driven distributed software rich systems. An evangelist for high-quality systems design, he has a strong interest in software architecture and development methodologies. Igor is always committed to advocating methodologies that promote high-quality software, such as test-driven development, one-step builds, and continuous integration. He also possesses solid knowledge of product development. With field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa. Igor is most grateful to his girlfriend for letting him spend hours on work instead with her and being an avid listener to his endless book monologues. He thanks his brother for being the strongest supporter. He is also thankful to his parents for letting him develop in various ways to become a person he is today.
Read more about Igor Milovanovic