Visualizing Tabular Data

Overview

Teaching: 15 min
Exercises: 0 min

Questions

• How can I visualize tabular data in Python?

• How can I group several plots together?

Objectives

• Plot simple graphs from data.

• Group several graphs in a single figure.

Visualizing data

The mathematician Richard Hamming once said, “The purpose of computing is insight, not numbers,” and the best way to develop insight is often to visualize data. Visualization deserves an entire lecture of its own, but we can explore a few features of Python’s matplotlib library here. While there is no official Python plotting library, matplotlib is the de facto standard. First, we will import the pyplot module from matplotlib and use two of its functions to create and display a heat map of our data:

from matplotlib import pyplot

Note the slightly different grammar this time for the import statement. Using from we import the library matplotlib.pyplot so that we don’t import every module in matplotlib but just the plotting module.

Furthermore, with this grammar we only have to call it pyplot. We could have abbreviated more by using a shortcut, e.g., to refer to the matplotlib.pyplot library as just plt, we could use either:

from matplotlib import pyplot as plt

import matplotlib.pyplot as plt

Now let’s plot the medical data we read previously:

heatmap = pyplot.imshow(data)
pyplot.show()

Blue pixels in this heat map represent low values, while yellow pixels represent high values. As we can see, inflammation rises and falls over a 40-day period.

More About Objects

You may have thought it peculiar that we typed this line:

heatmap = pyplot.imshow(data)

The function pyplot.imshow() took as an argument our array data, and the name suggests it shows an image. How can we assign that to a variable heatmap? What is happening?

Solution

Recall that everything created in Python is an object. When we make that assignment with the equals sign, heatmap is not a variable, but the name of the object that is the result of the function pyplot.imshow plotting the contents of the array named data. There are attributes, methods, and all kinds of information contained in an object assigned to the name heatmap. Specifically, heatmap is a graphical object - it contains information used to render a shaded plot of the information in the argument passed to pyplot.imshow, from the object called ‘data’.

The subsequent function pyplot.show() takes any graphical objects that have been created and displays them.

To continue, close the plot window (click the “X”).

Let’s take a look at the average inflammation over time:

ave_inflammation = numpy.mean(data, axis=0)
ave_plot = pyplot.plot(ave_inflammation)
pyplot.show()

Here, we have put the average inflammation per day across all patients in the variable ave_inflammation, then asked matplotlib.pyplot to create and display a line graph of those values using the function named plot().
The result is a roughly linear rise and fall, which seems rather suspicious: we might instead expect a sharper rise and slower fall. Let’s have a look at two other statistics. Close the plot window and proceed:

max_plot = pyplot.plot(numpy.max(data, axis=0))
pyplot.show()

What is this showing? Can you deduce it from the name of the numpy function?

How about if you try the numpy.std function?

std_plot = pyplot.plot(numpy.std(data, axis=0))
pyplot.show()

Strictly speaking, you do not need to assign object names to each plot. The act of calling a plotting function such as pyplot.imshow or pyplot.plot creates an open figure for each call. They remain open until pyplot.show is called, which displays all open figures and closes them.

However, there are times when you will want to create a named graphical object. The information in the object could be useful, e.g., if you want to arrange multiple plots as panels on a page. We will see such applications later.

For now, try calling multiple plotting functions before calling pyplot.show - what do you think will happen?

pyplot.plot(numpy.min(data, axis=0))
pyplot.plot(numpy.max(data, axis=0))
pyplot.plot(numpy.median(data, axis=0))
pyplot.show()

Key Points

• Use the pyplot module from the matplotlib library for creating simple visualizations.