Customizing with matplotlib

Intermediate Data Visualization with Seaborn

Chris Moffitt

Instructor

Matplotlib Axes

Most customization available through matplotlib Axes objects
Axes can be passed to seaborn functions

fig, ax = plt.subplots()
sns.histplot(df['Tuition'], ax=ax)
ax.set(xlabel='Tuition 2013-14')

Example axes plot

Further Customizations

The axes object supports many common customizations

fig, ax = plt.subplots()
sns.histplot(df['Tuition'], ax=ax)
ax.set(xlabel="Tuition 2013-14",
       ylabel="Distribution", xlim=(0, 50000), 
title="2013-14 Tuition and Fees Distribution")

Additional axes example

Combining Plots

It is possible to combine and configure multiple plots

fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, 
                               sharey=True, figsize=(7,4))

sns.histplot(df['Tuition'], stat='density', ax=ax0)
sns.histplot(df.query('State == "MN"')['Tuition'], stat='density', ax=ax1)

ax1.set(xlabel='Tuition (MN)', xlim=(0, 70000))
ax1.axvline(x=20000, label='My Budget', linestyle='--')
ax1.legend()

Combining Plots

Side by side plots

Let's practice!

Intermediate Data Visualization with Seaborn