Additional catplot() options

Working with Categorical Data in Python

Kasey Jones

Research Data Scientist

Difficulties with categorical plots

A count plot from created using seaborn that counts occurrences of reviews by user continent and traveler review type.

Using the catplot() facetgrid

An example of using seaborn to create the same visualization across different categories of a column. This visualization contains the same information as the previous graphic but now there is one graphic per user continent category.

Using different arguments

sns.catplot(x="Traveler type", kind="count",

    col="User continent",

    col_wrap=3,

    palette=sns.color_palette("Set1"), data=reviews)

x: "Traveler type"
kind: "count"
col: "User continent"
col_wrap: 3
palette: sns.color_palette("Set1")
Common colors: "Set", "Set2", "Tab10", "Paired"

¹ http://seaborn.pydata.org/tutorial/color_palettes.html

One more look

An updated visualization from a previous slide that now has a different color scheme.This visualization contains a count plot for each user continent.

Updating plots

Setup: save your graphic as an object: ax
Plot title: ax.fig.suptitle("My title")
Axis labels: ax.set_axis_labels("x-axis-label", "y-axis-label")
Title height: plt.subplots_adjust(top=.9)

ax = sns.catplot(x="Traveler type", col="User continent", col_wrap=3,
    kind="count", palette=sns.color_palette("Set1"), data=reviews)
ax.fig.suptitle("Hotel Score by Traveler Type & User Continent")
ax.set_axis_labels("Traveler Type", "Number of Reviews")
plt.subplots_adjust(top=.9)
plt.show()

Finished product

Our final visualization for this chapter. It features updated axis labels and an updated title.

catplot() practice

Working with Categorical Data in Python