Python for Programmers

Plotting with Matplotlib

Overview

Teaching: 5 min
Exercises: 5 min
Questions

  • How can we visualize data?

Objectives

  • Create plots from data

What is matplotlib?

Matplotlib is the most popular and mature library for plotting data using Python. It has all of the functionality you would expect, including the ability to control the formatting of plots and figures at a very fine level.

The official matplotlib documentation is at http://matplotlib.org/ The matplotlib gallery is at http://matplotlib.org/gallery.html

Importing matplotlib

Matplotlib is often used through ‘pyplot’, which provides a high-level interface for plotting.

import matplotlib.pyplot as plt

# I almost always also import numpy at this point
import numpy as np
# The inline flag means that images will be shown here in the notebooks, rather
# than in pop-up windows.
%matplotlib inline

2. Creating Figures

There are two major challenges with creating figures. First is understanding the syntax to actually make the basic plot appear. Second is formatting the basic plot to look exactly how you would like it to look. In general, the formatting will probably take you longer…

Within pyplot (currently imported as ‘plt’), there are two basic ways to go about making plots - using the Matlab-like clone (we saw that briefly before with a call to plt.plot), and using the object-oriented approach. The latter provides better control over plot features, while only requiring slightly more typing. It’s easy to quickly outgrow the Matlab clone, so we’ll go right to the object-oriented syntax.

A first plot

In simple matplotlib plotting, there are two concepts to distinguish:

For example, here’s how to make one figure with two subplots, the second of which contains two lines.

# Make some data to plot
x = np.linspace(0, 2*np.pi)
y1 = np.sin(x)
y2 = np.cos(x)

# First, create an empty figure with 2 subplots
# - The arguments (1, 2) indicate 1 row and 2 cols
# - The function plt.subplots returns an object for the figure and for each axes
# - There are multiple ways to accomplish this same goal, but this is probably the
#   simplest - notice that each subplot is associated with one of the axes objects.
fig, (ax1, ax2) = plt.subplots(1, 2)

# Next, put one line on the first axis and both lines on the second axis
# - On the second axes, add a legend to distinguish the two lines
ax1.plot(x, y1)

ax2.plot(x, y1, label='sin')  # The labels are what appear in the legend
ax2.plot(x, y2, label='cos')
ax2.legend()

# Finally, save the figure as a png file
fig.savefig('myfig.png')

Simple formatting

There are lots of formatting options to play with. Modify the code above to make some changes to the formatting of these plots.

First, make some changes to the axes. HINT: These adjustments are methods to > the ax1 and ax2 objects, and (conveniently) they all start with the text ‘set_’. Try typing ‘ax1.set_’ and hitting tab to see some options.

Second, make some changes to the lines that you plotted using ax1.plot(…). These changes can be made by looking at the various arguments that you can give to the plot method. You can do this easily by typing ax1.plot? in the cell below and running it - this will give you pop-up help for the plot method.

Bonus: Eliminate the box around the legend on the second subplot.

Other types of plots

In the example above, we used the plot method to make line plots. There are also methods to make scatter plots, barplots, histograms, loglog plots, semilog plots, etc.

# Make some data to plot
x = np.arange(0, 100)
y = np.random.rand(100)  # 100 random numbers

# Make a figure with 6 subplots and axes
fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2)

# Add data to each axis. Optional arguments to each method will customize each plot.
ax1.plot(x, y)
ax2.hist(y)
ax3.scatter(x, y)
ax4.boxplot(y)
ax5.loglog(x, y)
ax6.semilogx(x, y)

Plotting images

Matplotlib also makes it easy to plot images. For this, you can use the plot method imshow (syntax borrowed from Matlab).

For this, you can download some data from here. Unzip the folder in the directory in which you are currently working. Then:

# Read an image file for first subplot, generate random array for second
image_data = np.load('brain/slice1.npy')
# If you have images in a standard format (e.g. jpg), you can do this:
# img1 = plt.imread('image.jpg')
img2 = np.random.rand(128, 128)

# Make figure
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(img1)
ax2.imshow(img2)

ax1.set_axis_off()  # Hide "spines" on first axis

Smoothing and matshow

Note that imshow applies smoothing to the image. You can turn off this smoothing, using the interpolation key-word argument. Another option is to use the matshow function instead. That one does not apply smoothing.

It can be very intimidating to try to craft exactly the figure that you want, especially if you are used to being able to adjust things visually using a program like Excel.

If you get stuck and don’t know where to start, or just want to learn more about what matplotlib can do, a great option is to have a look at the matplotlib gallery, which can be found at http://matplotlib.org/gallery.html. A good way to get started is to find a figure here that sort of looks like what you want, copy the code, and modify it for your own needs.

Have a look at the matplotlib gallery, find a cool looking figure, copy the code a cell in your notebook and modify it. Note that some of the examples might require packages that are not installed on your machine (in particular those that make maps) - if this is the case, pick another example for the purposes of this exercise.

In Jupyter, you can use the %load. Type %loadpy and then the URL of the py file containing the code, and it will automatically copy it into a cell below. Run the cell with the code to see the figure.

Tweak the figure to your liking – use your creativity!

Key Points