Python Seaborn - Catplot

Introduction to Seaborn Catplot

Seaborn’s catplot() is a figure-level function designed for visualizing relationships between categorical and numerical data. It wraps around Matplotlib and pandas, offering a high-level yet flexible interface to produce clean, informative plots.

Loading Data and Basic Plot

Start by importing a dataset with categorical variables. For example, the “tips” dataset from Seaborn:

import seaborn as sns
tips = sns.load_dataset("tips")
sns.catplot(x="day", y="total_bill", data=tips)

This creates a default strip plot, showing individual points jittered along the categorical axis.

Changing Plot Types with kind

The kind argument lets you switch between different categorical visualizations:

sns.catplot(x="day", y="total_bill", kind="box", data=tips)
sns.catplot(x="day", y="total_bill", kind="violin", data=tips)
sns.catplot(x="day", y="total_bill", kind="bar", data=tips)
sns.catplot(x="day", kind="count", data=tips)

This covers box, violin, bar, and count plots—each highlighting data differently.

Adding Hue and Facets

You can introduce additional categorical dimensions with hue, col, and row. This splits the data into facets and colors by category:

sns.catplot(x="day", y="total_bill", hue="smoker", kind="bar", data=tips)
sns.catplot(x="time", y="pulse", hue="kind", col="diet",
            kind="strip", data=sns.load_dataset("exercise"))

Adjusting Size & Aspect

To better fit multiple facets, define the plot’s size and shape:

sns.catplot(x="day", y="total_bill", kind="violin",
            height=4, aspect=1.2, data=tips)

Fine-Tuning with FacetGrid Methods

catplot() returns a FacetGrid object. You can refine labels, titles, and axes using its methods:

g = sns.catplot(x="time", y="pulse", hue="kind",
                col="diet", kind="box", data=tips)
g.set_axis_labels("Time of Day", "Pulse Rate")
g.set_titles("{col_name} Diet")
g.despine(left=True)

Customization Tips

• Use palette (e.g. “Set2”, “viridis”) to modify colors.
• Set orient='h' for horizontal layouts.
• Control jitter, split violins, or add saturation for styling details.

Putting It All Together

Here’s a complete example combining facets, styling, and plot type:

sns.set_style("whitegrid")
g = sns.catplot(
    x="day", y="total_bill", hue="smoker",
    col="time", kind="violin",
    height=3, aspect=1.1,
    palette="Set2",
    data=tips
)
g.set_titles("{col_name}")
g.set_axis_labels("Day", "Total Bill ($)")
g.despine(left=True)

Conclusion

Seaborn’s catplot() is a versatile and user-friendly tool for exploring categorical relationships. Whether you need box plots, count plots, or violin plots—combined with grouping via hue or faceting—you can easily produce polished, insightful visualizations with minimal code.