Visualizing Tabular Data
Overview
Teaching: 35 min
Exercises: 20 minQuestions
How can I visualize tabular data in Python?
How can I group several plots together?
Objectives
Plot simple graphs from data.
Plot multiple 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 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:
import matplotlib.pyplot
image = matplotlib.pyplot.imshow(data)
matplotlib.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. Let’s take a look at the average inflammation over time:
ave_inflammation = numpy.mean(data, axis=0)
ave_plot = matplotlib.pyplot.plot(ave_inflammation)
matplotlib.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. The result is a roughly linear rise and fall, which is suspicious: we might instead expect
a sharper rise and slower fall. Let’s have a look at two other statistics:
max_plot = matplotlib.pyplot.plot(numpy.max(data, axis=0))
matplotlib.pyplot.show()
min_plot = matplotlib.pyplot.plot(numpy.min(data, axis=0))
matplotlib.pyplot.show()
The maximum value rises and falls smoothly, while the minimum seems to be a step function. Neither trend seems particularly likely, so either there’s a mistake in our calculations or something is wrong with our data. This insight would have been difficult to reach by examining the numbers themselves without visualization tools.
Grouping plots
You can group similar plots in a single figure using subplots.
This script below uses a number of new commands. The function matplotlib.pyplot.figure()
creates a space into which we will place all of our plots. The parameter figsize
tells Python how big to make this space. Each subplot is placed into the figure using
its add_subplot
method. The add_subplot
method takes 3
parameters. The first denotes how many total rows of subplots there are, the second parameter
refers to the total number of subplot columns, and the final parameter denotes which subplot
your variable is referencing (left-to-right, top-to-bottom). Each subplot is stored in a
different variable (axes1
, axes2
, axes3
). Once a subplot is created, the axes can
be titled using the set_xlabel()
command (or set_ylabel()
).
Here are our three plots side by side:
import numpy
import matplotlib.pyplot
data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0))
axes1 = fig.add_subplot(1, 3, 1)
axes2 = fig.add_subplot(1, 3, 2)
axes3 = fig.add_subplot(1, 3, 3)
axes1.set_ylabel('average')
axes1.plot(numpy.mean(data, axis=0))
axes2.set_ylabel('max')
axes2.plot(numpy.max(data, axis=0))
axes3.set_ylabel('min')
axes3.plot(numpy.min(data, axis=0))
fig.tight_layout()
matplotlib.pyplot.savefig('inflammation.png')
matplotlib.pyplot.show()
The call to loadtxt
reads our data,
and the rest of the program tells the plotting library
how large we want the figure to be,
that we’re creating three subplots,
what to draw for each one,
and that we want a tight layout.
(If we leave out that call to fig.tight_layout()
,
the graphs will actually be squeezed together more closely.)
The call to savefig
stores the plot as a graphics file. This can be
a convenient way to store your plots for use in other documents, web
pages etc. The graphics format is automatically determined by
Matplotlib from the file name ending we specify; here PNG from
‘inflammation.png’. Matplotlib supports many different graphics
formats, including SVG, PDF, and JPEG.
Importing libraries with shortcuts
In this lesson we use the
import matplotlib.pyplot
syntax to import thepyplot
module ofmatplotlib
. However, shortcuts such asimport matplotlib.pyplot as plt
are frequently used. Importingpyplot
this way means that after the initial import, rather than writingmatplotlib.pyplot.plot(...)
, you can now writeplt.plot(...)
. Another common convention is to use the shortcutimport numpy as np
when importing the NumPy library. We then can writenp.loadtxt(...)
instead ofnumpy.loadtxt(...)
, for example.Some people prefer these shortcuts as it is quicker to type and results in shorter lines of code - especially for libraries with long names! You will frequently see Python code online using a
pyplot
function withplt
, or a NumPy function withnp
, and it’s because they’ve used this shortcut. It makes no difference which approach you choose to take, but you must be consistent as if you useimport matplotlib.pyplot as plt
thenmatplotlib.pyplot.plot(...)
will not work, and you must useplt.plot(...)
instead. Because of this, when working with other people it is important you agree on how libraries are imported.
Plot Scaling
Why do all of our plots stop just short of the upper end of our graph?
Solution
Because matplotlib normally sets x and y axes limits to the min and max of our data (depending on data range)
If we want to change this, we can use the
set_ylim(min, max)
method of each ‘axes’, for example:axes3.set_ylim(0,6)
Update your plotting code to automatically set a more appropriate scale. (Hint: you can make use of the
max
andmin
methods to help.)Solution
# One method axes3.set_ylabel('min') axes3.plot(numpy.min(data, axis=0)) axes3.set_ylim(0,6)
Solution
# A more automated approach min_data = numpy.min(data, axis=0) axes3.set_ylabel('min') axes3.plot(min_data) axes3.set_ylim(numpy.min(min_data), numpy.max(min_data) * 1.1)
Drawing Straight Lines
In the center and right subplots above, we expect all lines to look like step functions because non-integer value are not realistic for the minimum and maximum values. However, you can see that the lines are not always vertical or horizontal, and in particular the step function in the subplot on the right looks slanted. Why is this?
Solution
Because matplotlib interpolates (draws a straight line) between the points. One way to do avoid this is to use the Matplotlib
drawstyle
option:import numpy import matplotlib.pyplot data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',') fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0)) axes1 = fig.add_subplot(1, 3, 1) axes2 = fig.add_subplot(1, 3, 2) axes3 = fig.add_subplot(1, 3, 3) axes1.set_ylabel('average') axes1.plot(numpy.mean(data, axis=0), drawstyle='steps-mid') axes2.set_ylabel('max') axes2.plot(numpy.max(data, axis=0), drawstyle='steps-mid') axes3.set_ylabel('min') axes3.plot(numpy.min(data, axis=0), drawstyle='steps-mid') fig.tight_layout() matplotlib.pyplot.show()
Make Your Own Plot
Create a plot showing the standard deviation (
numpy.std
) of the inflammation data for each day across all patients.Solution
std_plot = matplotlib.pyplot.plot(numpy.std(data, axis=0)) matplotlib.pyplot.show()
Moving Plots Around
Modify the program to display the three plots on top of one another instead of side by side.
Solution
import numpy import matplotlib.pyplot data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',') # change figsize (swap width and height) fig = matplotlib.pyplot.figure(figsize=(3.0, 10.0)) # change add_subplot (swap first two parameters) axes1 = fig.add_subplot(3, 1, 1) axes2 = fig.add_subplot(3, 1, 2) axes3 = fig.add_subplot(3, 1, 3) axes1.set_ylabel('average') axes1.plot(numpy.mean(data, axis=0)) axes2.set_ylabel('max') axes2.plot(numpy.max(data, axis=0)) axes3.set_ylabel('min') axes3.plot(numpy.min(data, axis=0)) fig.tight_layout() matplotlib.pyplot.show()
Key Points
Use the
pyplot
module from thematplotlib
library for creating simple visualizations.