Visualizing Data - Data Science from Scratch: First Principles with Python (2015)

Data Science from Scratch: First Principles with Python (2015)

Chapter 3. Visualizing Data

I believe that visualization is one of the most powerful means of achieving personal goals.

Harvey Mackay

A fundamental part of the data scientist’s toolkit is data visualization. Although it is very easy to create visualizations, it’s much harder to produce good ones.

There are two primary uses for data visualization:

§ To explore data

§ To communicate data

In this chapter, we will concentrate on building the skills that you’ll need to start exploring your own data and to produce the visualizations we’ll be using throughout the rest of the book. Like most of our chapter topics, data visualization is a rich field of study that deserves its own book. Nonetheless, we’ll try to give you a sense of what makes for a good visualization and what doesn’t.


A wide variety of tools exists for visualizing data. We will be using the matplotlib library, which is widely used (although sort of showing its age). If you are interested in producing elaborate interactive visualizations for the Web, it is likely not the right choice, but for simple bar charts, line charts, and scatterplots, it works pretty well.

In particular, we will be using the matplotlib.pyplot module. In its simplest use, pyplot maintains an internal state in which you build up a visualization step by step. Once you’re done, you can save it (with savefig()) or display it (with show()).

For example, making simple plots (like Figure 3-1) is pretty simple:

from matplotlib import pyplot as plt
years = [1950, 1960, 1970, 1980, 1990, 2000, 2010]
gdp = [300.2, 543.3, 1075.9, 2862.5, 5979.6, 10289.7, 14958.3]
# create a line chart, years on x-axis, gdp on y-axis
plt.plot(years, gdp, color='green', marker='o', linestyle='solid')
# add a title
plt.title("Nominal GDP")
# add a label to the y-axis
plt.ylabel("Billions of $")

A simple line chart.

Figure 3-1. A simple line chart

Making plots that look publication-quality good is more complicated and beyond the scope of this chapter. There are many ways you can customize your charts with (for example) axis labels, line styles, and point markers. Rather than attempt a comprehensive treatment of these options, we’ll just use (and call attention to) some of them in our examples.


Although we won’t be using much of this functionality, matplotlib is capable of producing complicated plots within plots, sophisticated formatting, and interactive visualizations. Check out its documentation if you want to go deeper than we do in this book.

Bar Charts

A bar chart is a good choice when you want to show how some quantity varies among some discrete set of items. For instance, Figure 3-2 shows how many Academy Awards were won by each of a variety of movies:

movies = ["Annie Hall", "Ben-Hur", "Casablanca", "Gandhi", "West Side Story"]
num_oscars = [5, 11, 3, 8, 10]
# bars are by default width 0.8, so we'll add 0.1 to the left coordinates
# so that each bar is centered
xs = [i + 0.1 for i, _ in enumerate(movies)]
# plot bars with left x-coordinates [xs], heights [num_oscars], num_oscars)
plt.ylabel("# of Academy Awards")
plt.title("My Favorite Movies")
# label x-axis with movie names at bar centers
plt.xticks([i + 0.5 for i, _ in enumerate(movies)], movies)

A simple bar chart.

Figure 3-2. A simple bar chart

A bar chart can also be a good choice for plotting histograms of bucketed numeric values, in order to visually explore how the values are distributed, as in Figure 3-3:

grades = [83,95,91,87,70,0,85,82,100,67,73,77,0]
decile = lambda grade: grade // 10 * 10
histogram = Counter(decile(grade) for grade in grades)[x - 4 for x in histogram.keys()], # shift each bar to the left by 4
        histogram.values(),                # give each bar its correct height
        8)                                 # give each bar a width of 8
plt.axis([-5, 105, 0, 5])                  # x-axis from -5 to 105,
                                           # y-axis from 0 to 5
plt.xticks([10 * i for i in range(11)])    # x-axis labels at 0, 10, ..., 100
plt.ylabel("# of Students")
plt.title("Distribution of Exam 1 Grades")

A bar chart histogram.

Figure 3-3. Using a bar chart for a histogram

The third argument to specifies the bar width. Here we chose a width of 8 (which leaves a small gap between bars, since our buckets have width 10). And we shifted the bar left by 4, so that (for example) the “80” bar has its left and right sides at 76 and 84, and (hence) its center at 80.

The call to plt.axis indicates that we want the x-axis to range from -5 to 105 (so that the “0” and “100” bars are fully shown), and that the y-axis should range from 0 to 5. And the call to plt.xticks puts x-axis labels at 0, 10, 20, …, 100.

Be judicious when using plt.axis(). When creating bar charts it is considered especially bad form for your y-axis not to start at 0, since this is an easy way to mislead people (Figure 3-4):

mentions = [500, 505]
years = [2013, 2014][2012.6, 2013.6], mentions, 0.8)
plt.ylabel("# of times I heard someone say 'data science'")
# if you don't do this, matplotlib will label the x-axis 0, 1
# and then add a +2.013e3 off in the corner (bad matplotlib!)
# misleading y-axis only shows the part above 500
plt.title("Look at the 'Huge' Increase!")

Misleading y-axis.

Figure 3-4. A chart with a misleading y-axis

In Figure 3-5, we use more-sensible axes, and it looks far less impressive:

plt.title("Not So Huge Anymore")

Non-misleading y-axis.

Figure 3-5. The same chart with a nonmisleading y-axis

Line Charts

As we saw already, we can make line charts using plt.plot(). These are a good choice for showing trends, as illustrated in Figure 3-6:

variance     = [1, 2, 4, 8, 16, 32, 64, 128, 256]
bias_squared = [256, 128, 64, 32, 16, 8, 4, 2, 1]
total_error  = [x + y for x, y in zip(variance, bias_squared)]
xs = [i for i, _ in enumerate(variance)]
# we can make multiple calls to plt.plot
# to show multiple series on the same chart
plt.plot(xs, variance,     'g-',  label='variance')    # green solid line
plt.plot(xs, bias_squared, 'r-.', label='bias^2')      # red dot-dashed line
plt.plot(xs, total_error,  'b:',  label='total error') # blue dotted line
# because we've assigned labels to each series
# we can get a legend for free
# loc=9 means "top center"
plt.xlabel("model complexity")
plt.title("The Bias-Variance Tradeoff")

Several line charts with a legend.

Figure 3-6. Several line charts with a legend


A scatterplot is the right choice for visualizing the relationship between two paired sets of data. For example, Figure 3-7 illustrates the relationship between the number of friends your users have and the number of minutes they spend on the site every day:

friends = [ 70,  65,  72,  63,  71,  64,  60,  64,  67]
minutes = [175, 170, 205, 120, 220, 130, 105, 145, 190]
labels =  ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i']
plt.scatter(friends, minutes)
# label each point
for label, friend_count, minute_count in zip(labels, friends, minutes):
        xy=(friend_count, minute_count), # put the label with its point
        xytext=(5, -5),                  # but slightly offset
        textcoords='offset points')
plt.title("Daily Minutes vs. Number of Friends")
plt.xlabel("# of friends")
plt.ylabel("daily minutes spent on the site")

A scatterplot of friends and time on the site.

Figure 3-7. A scatterplot of friends and time on the site

If you’re scattering comparable variables, you might get a misleading picture if you let matplotlib choose the scale, as in Figure 3-8:

test_1_grades = [ 99, 90, 85, 97, 80]
test_2_grades = [100, 85, 60, 90, 70]
plt.scatter(test_1_grades, test_2_grades)
plt.title("Axes Aren't Comparable")
plt.xlabel("test 1 grade")
plt.ylabel("test 2 grade")

A scatterplot with uncomparable axes.

Figure 3-8. A scatterplot with uncomparable axes

If we include a call to plt.axis("equal"), the plot (Figure 3-9) more accurately shows that most of the variation occurs on test 2.

That’s enough to get you started doing visualization. We’ll learn much more about visualization throughout the book.

A scatterplot with equal axes.

Figure 3-9. The same scatterplot with equal axes

For Further Exploration

§ seaborn is built on top of matplotlib and allows you to easily produce prettier (and more complex) visualizations.

§ D3.js is a JavaScript library for producing sophisticated interactive visualizations for the web. Although it is not in Python, it is both trendy and widely used, and it is well worth your while to be familiar with it.

§ Bokeh is a newer library that brings D3-style visualizations into Python.

§ ggplot is a Python port of the popular R library ggplot2, which is widely used for creating “publication quality” charts and graphics. It’s probably most interesting if you’re already an avid ggplot2 user, and possibly a little opaque if you’re not.