Joint Plots

What is a joint plot?

A joint plot shows:

• A bivariate relationship (scatter/KDE/hex)
• Marginal distributions on the sides

It’s a compact EDA tool.

diagram Anatomy of a joint plot mermaid

  flowchart TD
  A["x, y columns"] --> B["Center panel:
scatter / KDE / hex"]
  A --> C["Top margin:
histogram of x"]
  A --> D["Right margin:
histogram of y"]

A joint plot combines a center panel (the relationship) with two side panels (each variable's own distribution).

Note

A joint plot is figure-level (like pairplot and lmplot), so it manages its own figure — you can’t easily drop it into an existing plt.subplots() grid the way you can with axes-level functions like regplot or scatterplot.

Joint plot (scatter + hist)

jointplot

import seaborn as sns
 
tips = sns.load_dataset("tips")
 
sns.jointplot(data=tips, x="total_bill", y="tip", kind="scatter")

Joint plot with KDE

jointplot kde

import seaborn as sns
 
tips = sns.load_dataset("tips")
 
sns.jointplot(data=tips, x="total_bill", y="tip", kind="kde", fill=True)

Visualize it

The center shows how the two variables relate; the strips along the top and right edges show each variable’s own shape, on its own:

sketch Scatter plot with marginal histograms p5.js Pause Restart

The center panel is the x-y relationship; the top and right strips are each variable's own histogram.

Tips

• For large data, kind="hex" is useful to reduce overplotting.
• Joint plots are figure-level, so customization is slightly different.

Next

Continue to Pair Plots to see every pairwise relationship across several variables at once.

🧪 Try It Yourself

Exercise 1 – A Basic Joint Plot

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Exercise 2 – Switch the Center Panel to KDE

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Exercise 3 – Reduce Overplotting With Hex

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