How to change the figure size of a seaborn axes or figure level plot

Quick setup for A4-sized plots

Getting ready for print? Here's how you set up your Seaborn plots for A4 paper size:

a4_dims = (11.7, 8.27)  # A4 size dimensions in inches
sns.set(rc={'figure.figsize':a4_dims})  # "A4" setting for all your plots


Or before specific plots for clarity and control:

fig, ax = plt.subplots(figsize=a4_dims)  # Adjust size of your artboard
sns.barplot(..., ax=ax)  # Start painting 🎨


Customization using rcParams

Want to strut consistency over all your matplotlib plots? Befriend the rcParams dictionary:

from matplotlib import rcParams
rcParams['figure.figsize'] = a4_dims  # All subsequent plots will be this size


Persistently pleasing plots

Let's create a plot that fits perfectly to your ratio preferences:

aspect_ratio = 1.5
height_inches = 8  # Preferred height

figsize = (height_inches * aspect_ratio, height_inches)
plt.figure(figsize=figsize)  # Presto! 🎩


Getting cold feet after creating the plot? No worries, you're in control:

fig = plt.gcf()  # get current figure
fig.set_size_inches(11.7, 8.27)  # tweak until it's just right


Sizing figure-level plots

Dive deeper into Seaborn's figure-level functions like lmplot, catplot, and jointplot;

sns.catplot(..., size=4, aspect=2)  # Size - the height of facet in inches, Aspect - ratio of width to height 


But, if you seek absolute control over your plot sizes, let's turn to Matplotlib's methods:

g = sns.jointplot(...)
g.fig.set_size_inches(11.7, 8.27)  # Seaborn, meet Matplotlib 🤝


Post-customization

After sizing, let's trim artwork for some visual polish.

sns.despine()  # Trimming unnecessary spines for a neat look


All set? Save it for posterity:

fig.savefig('plot_a4.png', format='png', dpi=300)  # Preserving your masterpiece 🎭


Keeping your plot game strong

Here are some aspects to keep in mind for pristine plots:

Loading data

Load your dataset and then straight into Seaborn plots:

data = custom_load_data('data.csv')  # Your data loading function
sns.lineplot(x='time', y='value', data=data)
plt.show()


Mix it up with plot types

Unleash the power of various Seaborn plot types:

• sns.violinplot() for comparing distributions.
• sns.scatterplot() for relationships revelation.
• sns.heatmap() for those hot correlation matrices.

Join the plot discussion

Keep up-to-date with GitHub issues and StackOverflow discussions. They're treasure troves for tips, troubleshooting and memes.