When to Use Categorical Scatterplots¶

Date published: 2018-07-22

Category: Data Visualizations

Subcategory: Best Practices

Tags: scatter plot

Often times in businesses we want to visualize the distribution from several categorical variables provided in a dataset.

To display the distribution of a category of data, typically people use a box plot or histogram. However, sometimes those visualizations may be improperly used. If you have a small number of data points for one category - often 5 - 80 points, I'd recommend you start with a categorical scatter plot for comparison.

Below, I'll illustrate a few examples.

Import Modules¶

In [1]:

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

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

Set Global Styles for Visualizations¶

In [2]:

sns.set_context('talk')
sns.set_style("darkgrid")

sns.set_context('talk') sns.set_style("darkgrid")

Example: Tips Dataset¶

In the examples below, I utilize the dataset tips provided in the Seaborn visualization library.

Each row in this dataset is a record of a meal at a restaurant. For each meal, the restuarant recorded values for the total bill amount in U.S. dollars, tip amount, gender/sex of the waiter, day, meal and table size.

Load flights Dataset¶

In [3]:

df_tips = sns.load_dataset("tips")

df_tips = sns.load_dataset("tips")

Preview the First 5 Rows of Data¶

In [4]:

df_tips.head(8)

df_tips.head(8)

Out[4]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
5	25.29	4.71	Male	No	Sun	Dinner	4
6	8.77	2.00	Male	No	Sun	Dinner	2
7	26.88	3.12	Male	No	Sun	Dinner	4

Visualize Total Bill Amount by Table Size¶

This categorical scatter plot below, more specifically called a swarm plot, helps illustrate the count of records for each table size and the distribution of bill amounts by table size.

A swarm plot is ideal here because we have so few records of meals with several table sizes such as 1, 5, and 6.

Alternatively, if we tried to use a histogram or box plot to illustrate these few records for category, we'd get a false representation of the bill amounts by table size. The reason for that is that it's bad practice to compare just 4 records for a table size of 1 to 156 record for a table size of 2. For the 4 records of table size of 1 person, those could be a false representation since they may have all made very very small purchases and therefore small total_bill amounts in our dataset.

In [5]:

sns.catplot(x="size", y="total_bill", kind="swarm", data=df_tips, height=8.5, aspect=.9)
plt.xlabel("table size [number of people]", labelpad=15)
plt.ylabel("total bill amount [$]", labelpad=15)
plt.title("Distribution of Bill Amounts by Table Size", y=1.013);

sns.catplot(x="size", y="total_bill", kind="swarm", data=df_tips, height=8.5, aspect=.9) plt.xlabel("table size [number of people]", labelpad=15) plt.ylabel("total bill amount [$]", labelpad=15) plt.title("Distribution of Bill Amounts by Table Size", y=1.013);

Interpretation of Distribution of Bill Amounts by Table Size¶

There's few records of bill amounts for table sizes of 1, 5, and 6. The restaurant mostly gets table sizes of 2 and 3.

Generally, as the table size increases, total bill amount increases.

For a table size of 2, most bill amounts are between 10 and 20 U.S. dollars.

Visualize Total Bill Amount by Table Size and Gender/Sex¶

To improve on the visualization from above, I'm curious if there's any patterns of bill amounts for tables by waiter or waitresses' gender.

In [6]:

sns.catplot(x="size", y="total_bill", kind="swarm", hue='sex', data=df_tips, height=8.5, aspect=.9)
plt.xlabel("table size [number of people]", labelpad=15)
plt.ylabel("total bill amount [$]", labelpad=15)
plt.title("Distribution of Bill Amounts by Table Size", y=1.013);

sns.catplot(x="size", y="total_bill", kind="swarm", hue='sex', data=df_tips, height=8.5, aspect=.9) plt.xlabel("table size [number of people]", labelpad=15) plt.ylabel("total bill amount [$]", labelpad=15) plt.title("Distribution of Bill Amounts by Table Size", y=1.013);

Interpration of Visualization of Total Bill Amount by Table Size and Gender/Sex¶

There's no clear pattern of bill amounts by table size and gender.

At a glance, it's likely males have served more tables than females. Males often hold the top few spots for highest bill amount by table size too.