Matplotlib Tutorial: 1. Basic Plot Interface

In this notebook, we will explore the basic plot interface using plt.plot and plt.scatter. We will also discuss the difference between the pyplot interface, which offers plotting with the feel of Matlab. In the following sections, we will introduce the object-oriented interface, which offers more flexibility and will be used throughout the remainter of the tutorial.

This tutorial is written for Python 3.3; to make it work with Python 2 we'll do some future imports:



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from __future__ import print_function, division

Setting up IPython

IPython has a built-in mode to work cleanly with matplotlib figures. The most common way to invoke it is to use the following magic command:



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%matplotlib inline

A first plot: the PyPlot interface

Now we're ready for a plot. The %pylab mode we entered above does a few things, among which is the import of pylab into the current namespace. For clarity, we'll do this directly here. We'll also import numpy in order to easily manipulate the arrays we'll plot:



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import matplotlib.pyplot as plt
import numpy as np

Let's make some simple data to plot: a sinusoid



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x = np.linspace(0, 20, 1000)  # 100 evenly-spaced values from 0 to 50
y = np.sin(x)

plt.plot(x, y);

Customizing the plot: Axes Limits

Let's play around with this a bit: first we can change the axis limits using xlim() and ylim()



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plt.plot(x, y)
plt.xlim(5, 15)
plt.ylim(-1.2, 1.2);

Customizing the plot: Axes Labels and Titles

We can label the axes and add a title:



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plt.plot(x, y)

plt.xlabel('this is x!')
plt.ylabel('this is y!')
plt.title('My First Plot');

Labels can also be rendered using LaTeX symbols:



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y = np.sin(2 * np.pi * x)
plt.plot(x, y)
plt.title(r'$\sin(2 \pi x)$')  # the `r` before the string indicates a "raw string";

Customizing the plot: Line Styles

We can vary the line color or the line symbol:



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plt.plot(x, y, '-r')  # solid red line ('r' comes from RGB color scheme)
plt.xlim(0, 10)
plt.ylim(-1.2, 1.2)

plt.xlabel('this is x!')
plt.ylabel('this is y!')
plt.title('My First Plot');

Other options for the color characters are:

 'r' = red
 'g' = green
 'b' = blue
 'c' = cyan
 'm' = magenta
 'y' = yellow
 'k' = black
 'w' = white

Options for line styles are

 '-' = solid
 '--' = dashed
 ':' = dotted
 '-.' = dot-dashed
 '.' = points
 'o' = filled circles
 '^' = filled triangles

and many, many more.

For more information, view the documentation of the plot function. In IPython, this can be accomplished using the ? functionality:



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plt.plot?

Also see the online version of this help: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot

Cusomizing the Plot: Legends

Multiple lines can be shown on the same plot. In this case, you can use a legend to label the two lines:



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x = np.linspace(0, 20, 1000)
y1 = np.sin(x)
y2 = np.cos(x)

plt.plot(x, y1, '-b', label='sine')
plt.plot(x, y2, '-r', label='cosine')
plt.legend(loc='upper right')
plt.ylim(-1.5, 2.0);

Exercise: Linestyles & Plot Customization

Below are two sets of arrays x1, y1, and x2, y2. Create a plot where x1 and y1 are represented by blue circles, and x2 and y2 are represented by a dotted black line. Label the symbols "sampled" and "continuous", and add a legend. Adjust the y limits to suit your taste.



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x1 = np.linspace(0, 10, 20)
y1 = np.sin(x1)

x2 = np.linspace(0, 10, 1000)
y2 = np.sin(x2)