Creating a box plot

Introduction to Data Visualization with Seaborn

Erin Case

Data Scientist

What is a box plot?

Shows the distribution of quantitative data
See median, spread, skewness, and outliers
Facilitates comparisons between groups

Box plot of total bill broken down by day of the week

¹ Waskom, M. L. (2021). seaborn: statistical data visualization. https://seaborn.pydata.org/

How to create a box plot

import matplotlib.pyplot as plt
import seaborn as sns

g = sns.catplot(x="time", 
                y="total_bill",
                data=tips, 
                kind="box")

plt.show()

Box plot of total bill broken down by time of day

¹ Waskom, M. L. (2021). seaborn: statistical data visualization. https://seaborn.pydata.org/

Change the order of categories

import matplotlib.pyplot as plt
import seaborn as sns

g = sns.catplot(x="time", 
                y="total_bill",
                data=tips, 
                kind="box",
                order=["Dinner", 
                       "Lunch"])

plt.show()

Box plot with dinner shown before lunch

¹ Waskom, M. L. (2021). seaborn: statistical data visualization. https://seaborn.pydata.org/

Omitting the outliers using `sym`

import matplotlib.pyplot as plt
import seaborn as sns

g = sns.catplot(x="time", 
                y="total_bill",
                data=tips, 
                kind="box",
                sym="")

plt.show()

Box plot with outliers omitted

¹ Waskom, M. L. (2021). seaborn: statistical data visualization. https://seaborn.pydata.org/

Changing the whiskers using `whis`

By default, the whiskers extend to 1.5 * the interquartile range
Make them extend to 2.0 * IQR: whis=2.0
Show the 5th and 95th percentiles: whis=[5, 95]
Show min and max values: whis=[0, 100]

Changing the whiskers using `whis`

import matplotlib.pyplot as plt
import seaborn as sns

g = sns.catplot(x="time", 
                y="total_bill",
                data=tips, 
                kind="box",
                whis=[0, 100])

plt.show()

Box plot with whiskers set to minimum and maximum

¹ Waskom, M. L. (2021). seaborn: statistical data visualization. https://seaborn.pydata.org/

Let's practice!

Introduction to Data Visualization with Seaborn