Data Visualization

In this lesson, we'll learn two data visualization libraries matplotlib and seaborn. By the end of this lesson, students will be able to:

• Skim library documentation to identify relevant examples and usage information.
• Apply seaborn and matplotlib to create and customize relational and regression plots.
• Describe data visualization principles as they relate the effectiveness of a plot.

Just like how we like to import pandas as pd, we'll import matplotlib.pyplot as plt and seaborn as sns.

Seaborn is a Python data visualization library based on matplotlib. Behind the scenes, seaborn uses matplotlib to draw its plots. When importing seaborn, make sure to call sns.set_theme() to apply the recommended seaborn visual style.

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

sns.set_theme()

Let's load this uniquely-formatted pokemon dataset.

pokemon = pd.read_csv("pokemon_viz.csv", index_col="Num")
pokemon

	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Stage	Legendary
Num
1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	2	False
3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	3	False
4	Charmander	Fire	NaN	309	39	52	43	60	50	65	1	False
5	Charmeleon	Fire	NaN	405	58	64	58	80	65	80	2	False
...	...	...	...	...	...	...	...	...	...	...	...	...
147	Dratini	Dragon	NaN	300	41	64	45	50	50	50	1	False
148	Dragonair	Dragon	NaN	420	61	84	65	70	70	70	2	False
149	Dragonite	Dragon	Flying	600	91	134	95	100	100	80	3	False
150	Mewtwo	Psychic	NaN	680	106	110	90	154	90	130	1	True
151	Mew	Psychic	NaN	600	100	100	100	100	100	100	1	False

151 rows × 12 columns

Figure-level versus axes-level functions

One way to draw a scatter plot comparing every pokemon's Attack and Defense stats is by calling sns.scatterplot. Because this plotting function has so many parameters, it's good practice to specify keyword arguments that tell Python which argument should go to which parameter.

sns.scatterplot(pokemon, x="Attack", y="Defense")

<Axes: xlabel='Attack', ylabel='Defense'>

The return type of sns.scatterplot is a matplotlib feature called axes that can be used to compose multiple plots into a single visualization. We can show two plots side-by-side by placing them on the same axes. For example, we could compare the attack and defense stats for two different groups of pokemon: not-Legendary and Legendary.

fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2) # Nested tuple unpacking!
ax1.set_title("Not Legendary")
sns.scatterplot(pokemon[~pokemon["Legendary"]], x="Attack", y="Defense", ax=ax1)
ax2.set_title("Legendary")
sns.scatterplot(pokemon[pokemon["Legendary"]], x="Attack", y="Defense", ax=ax2)
fig.show()

Each problem in the plot above can be fixed manually by repeatedly editing and running the code until you get a satisfactory result, but it's a tedious process. Seaborn was invented to make our data visualization experience less tedious. Methods like sns.scatterplot are considered axes-level functions designed for interoperability with the rest of matplotlib, but they come at the cost of forcing you to deal with the tediousness of tweaking matplotlib.

Instead, the recommended way to create plots in seaborn is to use figure-level functions like sns.relplot as in relational plot. Figure-level functions return specialized seaborn objects (such as FacetGrid) that are intended to provide more usable results without tweaking.

sns.relplot(pokemon, x="Attack", y="Defense", col="Legendary")

<seaborn.axisgrid.FacetGrid at 0x7c2bfc374490>

By default, relational plots produce scatter plots but they can also produce line plots by specifying the keyword argument kind="line".

Alongside relplot, seaborn provides several other useful figure-level plotting functions:

• relplot for relational plots, such as scatter plots and line plots.
• displot for distribution plots, such as histograms and kernel density estimates.
• catplot for categorical plots, such as strip plots, box plots, violin plots, and bar plots.
• lmplot for relational plots with a regression fit, such as the scatter plot with regression fit below.

sns.lmplot(pokemon, x="Attack", y="Defense", col="Legendary")

<seaborn.axisgrid.FacetGrid at 0x7c2bfc7c2650>

We will primarily use figure-level plots in this course. On the relative merits of figure-level functions in the seaborn documentation:

On balance, the figure-level functions add some additional complexity that can make things more confusing for beginners, but their distinct features give them additional power. The tutorial documentation mostly uses the figure-level functions, because they produce slightly cleaner plots, and we generally recommend their use for most applications. The one situation where they are not a good choice is when you need to make a complex, standalone figure that composes multiple different plot kinds. At this point, it’s recommended to set up the figure using matplotlib directly and to fill in the individual components using axes-level functions.

Customizing a FacetGrid plot

relplot, displot, catplot, and lmplot all return a FacetGrid, a specialized seaborn object that represents a data visualization canvas. As we've seen above, a FacetGrid can put two plots side-by-side and manage their axes by removing the y-axis labels on the right plot because they are the same as the plot on the left.

However, there are still many instances where we might want to customize a plot by changing labels or adding titles. We might want to create a bar plot to count the number of each type of pokemon.

sns.catplot(pokemon, x="Type 1", kind="count")

<seaborn.axisgrid.FacetGrid at 0x7c2bfc383350>

The pokemon types on the x-axis are hardly readable, the y-axis label "count" could use capitalization, and the plot could use a title. To modify the attributes of a plot, we can assign the returned FacetGrid to a variable like grid and then call tick_params or set.

grid = sns.catplot(pokemon, x="Type 1", kind="count")
grid.tick_params(axis="x", rotation=60)
grid.set(title="Count of each primary pokemon type", xlabel="Primary Type", ylabel="Count")

<seaborn.axisgrid.FacetGrid at 0x7c2bfc25b650>

Practice: Life expectancy versus health expenditure

Seaborn includes a repository of example datasets that we can load into a DataFrame by calling sns.load_dataset.

life_expectancy = sns.load_dataset("healthexp", index_col=["Year", "Country"])
life_expectancy

		Spending_USD	Life_Expectancy
Year	Country
1970	Germany	252.311	70.6
	France	192.143	72.2
	Great Britain	123.993	71.9
	Japan	150.437	72.0
	USA	326.961	70.9
...	...	...	...
2020	Germany	6938.983	81.1
	France	5468.418	82.3
	Great Britain	5018.700	80.4
	Japan	4665.641	84.7
	USA	11859.179	77.0

274 rows × 2 columns

Write a seaborn expression to create a line plot comparing the Year (x-axis) to the Life_Expectancy (y-axis) colored with hue="Country".

sns.relplot(life_expectancy, x="Year", y="Life_Expectancy", hue="Country", kind="line")

<seaborn.axisgrid.FacetGrid at 0x7c2bfc3b1050>

# We do not want to use the axes-level function for reasons discussed previously
sns.lineplot(life_expectancy, x="Year", y="Life_Expectancy", hue="Country")

<Axes: xlabel='Year', ylabel='Life_Expectancy'>

life_expectancy.plot(x="Year", y="Life_Expectancy", hue="Country", kind="line")

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File /opt/conda/lib/python3.11/site-packages/pandas/core/indexes/base.py:3805, in Index.get_loc(self, key)
   3804 try:
-> 3805     return self._engine.get_loc(casted_key)
   3806 except KeyError as err:

File index.pyx:167, in pandas._libs.index.IndexEngine.get_loc()

File index.pyx:196, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/hashtable_class_helper.pxi:7081, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas/_libs/hashtable_class_helper.pxi:7089, in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'Year'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
Cell In[24], line 1
----> 1 life_expectancy.plot(x="Year", y="Life_Expectancy", hue="Country")

File /opt/conda/lib/python3.11/site-packages/pandas/plotting/_core.py:995, in PlotAccessor.__call__(self, *args, **kwargs)
    993 if is_integer(x) and not data.columns._holds_integer():
    994     x = data_cols[x]
--> 995 elif not isinstance(data[x], ABCSeries):
    996     raise ValueError("x must be a label or position")
    997 data = data.set_index(x)

File /opt/conda/lib/python3.11/site-packages/pandas/core/frame.py:4102, in DataFrame.__getitem__(self, key)
   4100 if self.columns.nlevels > 1:
   4101     return self._getitem_multilevel(key)
-> 4102 indexer = self.columns.get_loc(key)
   4103 if is_integer(indexer):
   4104     indexer = [indexer]

File /opt/conda/lib/python3.11/site-packages/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key)
   3807     if isinstance(casted_key, slice) or (
   3808         isinstance(casted_key, abc.Iterable)
   3809         and any(isinstance(x, slice) for x in casted_key)
   3810     ):
   3811         raise InvalidIndexError(key)
-> 3812     raise KeyError(key) from err
   3813 except TypeError:
   3814     # If we have a listlike key, _check_indexing_error will raise
   3815     #  InvalidIndexError. Otherwise we fall through and re-raise
   3816     #  the TypeError.
   3817     self._check_indexing_error(key)

KeyError: 'Year'

Let's examine the visualization, Life expectancy vs. health expenditure, 1970 to 2020. How would we change the above code to produce a line plot like this?

sns.relplot(life_expectancy, x="Spending_USD", y="Life_Expectancy", hue="Country", kind="line")

<seaborn.axisgrid.FacetGrid at 0x7c2bf97ba110>

What makes bad figures bad?

In chapter 1 of Data Visualization, Kieran Hiely explains how data visualization is about communication and rhetoric.

While it is tempting to simply start laying down the law about what works and what doesn't, the process of making a really good or really useful graph cannot be boiled down to a list of simple rules to be followed without exception in all circumstances. The graphs you make are meant to be looked at by someone. The effectiveness of any particular graph is not just a matter of how it looks in the abstract, but also a question of who is looking at it, and why. An image intended for an audience of experts reading a professional journal may not be readily interpretable by the general public. A quick visualization of a dataset you are currently exploring might not be of much use to your peers or your students.

Bad taste

Kieran identifies three problems, the first of which is bad taste.

Kieran draws on Edward Tufte's principles (all quoted from Tufte 1983):

• have a properly chosen format and design
• use words, numbers, and drawing together
• display an accessible complexity of detail
• avoid content-free decoration, including chartjunk

In essence, these principles amount to "an encouragement to maximize the 'data-to-ink' ratio." In practice, our plotting libraries like seaborn do a fairly good job of providing defaults that generally follow these principles.

Bad data

The second problem is bad data, which can involve either cherry-picking data or presenting information in a misleading way.

In November of 2016, The New York Times reported on some research on people's confidence in the institutions of democracy. It had been published in an academic journal by the political scientist Yascha Mounk. The headline in the Times ran, "How Stable Are Democracies? ‘Warning Signs Are Flashing Red’” (Taub, 2016). The graph accompanying the article [...] certainly seemed to show an alarming decline.

This plot is one that is well-produced, and that we could reproduce by calling sns.relplot like we learned above. The x-axis shows the decade of birth for people all surveyed in the research study, the y-axis shows the percentage, and plots are shown side-by-side to enable comparison between countries.

[But] scholars who knew the World Values Survey data underlying the graph noticed something else. The graph reads as though people were asked to say whether they thought it was essential to live in a democracy, and the results plotted show the percentage of respondents who said "Yes", presumably in contrast to those who said "No". But in fact the survey question asked respondents to rate the importance of living in a democracy on a ten point scale, with 1 being "Not at all Important" and 10 being "Absolutely Important". The graph showed the difference across ages of people who had given a score of "10" only, not changes in the average score on the question. As it turns out, while there is some variation by year of birth, most people in these countries tend to rate the importance of living in a democracy very highly, even if they do not all score it as "Absolutely Important". The political scientist Erik Voeten redrew the figure [...] using the average response.

Bad perception

The third problem is bad perception, which refers to how humans process the information contained in a visualization. Let's walk through section 1.3 on "Perception and data visualization".