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Practice

Multi-Plot Grids (FacetGrid, lmplot)

Seaborn lets you build multi-plot grids by faceting data into categories, making side-by-side comparisons easy.

Two figure-level tools are especially helpful for this: FacetGrid and lmplot.


FacetGrid Overview​

A FacetGrid lays out a grid of subplots using the unique values of one or more categorical variables.

You can set:

  • row: split plots vertically
  • col: split plots horizontally
  • hue (optional): color by category

This breaks a complex dataset into smaller, comparable views.

FacetGrid with Scatter Plots
import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

g = sns.FacetGrid(tips, row="sex", col="time", hue="smoker", margin_titles=True)
g.map_dataframe(sns.scatterplot, x="total_bill", y="tip")
g.add_legend()
plt.show()
FacetGrid with Histograms
g = sns.FacetGrid(tips, col="day")
g.map_dataframe(sns.histplot, x="total_bill", bins=20)
plt.show()

lmplot Overview​

lmplot is a figure-level function that combines a regression plot with FacetGrid faceting.

It fits a trend line and can facet and color by category in a single call.

lmplot with Categories and Facets
sns.lmplot(
    data=tips,
    x="total_bill",
    y="tip",
    hue="sex",
    col="time",
    height=4,
    scatter_kws={"alpha": 0.6}
)
plt.show()

When to Use Each​

  • Use FacetGrid for a flexible grid where you’ll map different plot functions (scatter, hist, KDE, bar).
  • Use lmplot for regression lines with optional faceting and coloring in one step.

Tips for Clear Multi-Plot Grids​

  • Limit facets: too many categories make grids hard to read.
  • Keep axes consistent: shared scales (sharex, sharey) make comparisons easier.
  • Add legends and titles: use add_legend() and margin_titles=True for clarity.

Now explore these patterns step by step in the notebook on the right side of the screen.

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