The most useful little guide to visualization I've ever found is the decision tree created by Andrew Abela, which you can find here. Do you want to show a comparison, a distribution, a relationship, or a composition? If it's a comparison, is it among items or over time? Each choice leads to a different kind of plot, and while you may not agree with all the choices, it makes the reasoning behind them concrete.

One day, I hope someone will turn that chart into a half-day lesson on visualization for Software Carpentry. Before then, though, I have a little visualization challenge that I think some of you might enjoy. This small data set shows population, number of workshop attendees, and number of instructors by country:

Country,       Population,  Attendees,  Instructors
Australia,       23737000,        592,           11
Brazil,         203850000,        116,            1
Canada,          35675000,       1215,           46
China,         1368090000,         24,            1
Cyprus,            858000,         25,            0
Denmark,          5655000,         20,            1
France,          66092000,         72,            4
Germany,         80767000,        183,            6
Ghana,           27043000,         24,            0
India,         1266580000,          0,            1
Ireland,          4609000,          0,            1
Israel,           8296000,          0,            1
Italy,           60782000,         81,            1
Japan,          127020000,          0,            1
Jordan,           6688000,         34,            0
Lebanon,          4104000,         25,            0
Netherlands,     16888000,         39,            0
New Zealand,      4560000,         19,            1
Norway,           5156000,         90,            2
Poland,          38496000,         60,            5
Saudi Arabia,    31521000,         20,            0
Singapore,        5469000,          0,            1
South Africa,    54002000,         90,            2
Spain,           46464000,          0,            2
Sweden,           9743000,         54,            3
Switzerland,      8211000,         63,            0
Thailand,        64871000,          0,            1
United Kingdom,  64105000,       1231,           48
United States,  320354000,       5253,          166

I'd like a scatter plot comparing workshop attendees per capita to instructors per capita. This little Python program does that:

import sys
import csv
import numpy as np
from matplotlib import pyplot as plt

countries = []
populations = []
attendees = []
instructors = []
with open(sys.argv[1], 'r') as raw:
    cooked = csv.reader(raw)
    for (c, p, a, i) in cooked:
        countries.append(c)
        populations.append(float(p))
        attendees.append(float(a))
        instructors.append(float(i))

populations = np.array(populations) / 1e6
attendees = np.array(attendees) / populations
instructors = np.array(instructors) / populations

plt.scatter(attendees, instructors)
plt.xlabel('Attendees per million pop')
plt.ylabel('Instructors per million pop')

for (label, x, y) in zip(countries, attendees, instructors):
    plt.annotate(label, xy = (x, y))

plt.show()

(You can download the data and program here and here, and yes, the CSV has all those extra spaces taken out.) The plot produced by this program is:

Comparing Workshop Attendees and Instructors per Capita by Country

It's not particularly useful (which is Canadian for "it's awful"): labels overlap, values crowd near the origin, and so on. My challenge to readers is to create something better. More specifically, add a comment to this post with your code (in any language) and a link to the picture it produces, and explain briefly why you think it's better. And while you're thinking about how to do that, have a look at Ned Gulley's In Praise of Tweaking — if there's a way to do for visualization what his programming contest does for performance, I'd like to give it a try.

This post originally appeared in the Software Carpentry blog.