Scatter Plot with Marginal Distributions — Seaborn

A scatter plot enhanced with marginal distribution plots (histograms or kernel density estimates) on the top and right axes. This composite visualization simultaneously shows the bivariate relationship between two variables in the central scatter plot while displaying each variable's univariate distribution along its respective axis. It provides a comprehensive view of both the joint and marginal distributions in a single figure.

Scatter Plot with Marginal Distributions rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
scatter-marginal: Scatter Plot with Marginal Distributions
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-09
"""

import numpy as np
import seaborn as sns


# Data - correlated bivariate data to demonstrate scatter + marginal distributions
np.random.seed(42)
n_points = 200
x = np.random.randn(n_points) * 15 + 50
y = 0.7 * x + np.random.randn(n_points) * 10 + 20

# Create jointplot with scatter and marginal histograms+KDE
sns.set_context("talk", font_scale=1.4)
g = sns.jointplot(
    x=x,
    y=y,
    kind="scatter",
    height=12,
    ratio=5,
    marginal_kws={"bins": 25, "kde": True, "color": "#306998", "alpha": 0.7},
    joint_kws={"s": 150, "alpha": 0.65, "color": "#306998", "edgecolor": "white", "linewidth": 0.5},
)

# Style the central scatter plot
g.ax_joint.set_xlabel("X Value", fontsize=22)
g.ax_joint.set_ylabel("Y Value", fontsize=22)
g.ax_joint.tick_params(axis="both", labelsize=16)
g.ax_joint.grid(True, alpha=0.3, linestyle="--")

# Style marginal plots
g.ax_marg_x.tick_params(axis="both", labelsize=14)
g.ax_marg_y.tick_params(axis="both", labelsize=14)

# Add title to figure
g.figure.suptitle("scatter-marginal · seaborn · pyplots.ai", fontsize=26, y=0.98)
g.figure.subplots_adjust(top=0.92)

# Save at high resolution
g.figure.savefig("plot.png", dpi=300, bbox_inches="tight")

Part of Scatter Plot with Marginal Distributions on anyplot.ai.

Other implementations