Scatter Plot with Marginal Distributions — Matplotlib

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 Matplotlib

Python source (Matplotlib)

""" anyplot.ai
scatter-marginal: Scatter Plot with Marginal Distributions
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-09
"""

import os

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.gridspec import GridSpec


# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
BRAND = "#009E73"

# Data - correlated bivariate data with realistic pattern
np.random.seed(42)
n = 200

# Create correlated data with bimodal structure
x = np.concatenate([np.random.normal(30, 8, n // 2), np.random.normal(60, 10, n // 2)])
y = 0.7 * x + np.random.normal(0, 8, n) + 10

# Create figure with GridSpec for layout
fig = plt.figure(figsize=(16, 9), facecolor=PAGE_BG)
gs = GridSpec(4, 4, figure=fig, hspace=0.05, wspace=0.05)

# Main scatter plot (lower-left, 3x3)
ax_main = fig.add_subplot(gs[1:4, 0:3], facecolor=PAGE_BG)
ax_main.scatter(x, y, s=100, alpha=0.65, color=BRAND, edgecolors=PAGE_BG, linewidth=0.5)
ax_main.set_xlabel("Feature A", fontsize=20, color=INK)
ax_main.set_ylabel("Feature B", fontsize=20, color=INK)
ax_main.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax_main.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)
for s in ("left", "bottom"):
    ax_main.spines[s].set_color(INK_SOFT)
ax_main.spines["top"].set_visible(False)
ax_main.spines["right"].set_visible(False)

# Top marginal histogram (aligned with main x-axis)
ax_top = fig.add_subplot(gs[0, 0:3], sharex=ax_main, facecolor=PAGE_BG)
ax_top.hist(x, bins=25, color=BRAND, alpha=0.5, edgecolor=PAGE_BG, linewidth=0.5)
ax_top.tick_params(axis="x", labelbottom=False, colors=INK_SOFT)
ax_top.tick_params(axis="y", labelsize=14, colors=INK_SOFT)
ax_top.set_ylabel("Count", fontsize=16, color=INK)
ax_top.spines["top"].set_visible(False)
ax_top.spines["right"].set_visible(False)
for s in ("left", "bottom"):
    ax_top.spines[s].set_color(INK_SOFT)
ax_top.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)

# Right marginal histogram (aligned with main y-axis)
ax_right = fig.add_subplot(gs[1:4, 3], sharey=ax_main, facecolor=PAGE_BG)
ax_right.hist(y, bins=25, orientation="horizontal", color=BRAND, alpha=0.5, edgecolor=PAGE_BG, linewidth=0.5)
ax_right.tick_params(axis="y", labelleft=False, colors=INK_SOFT)
ax_right.tick_params(axis="x", labelsize=14, colors=INK_SOFT)
ax_right.set_xlabel("Count", fontsize=16, color=INK)
ax_right.spines["top"].set_visible(False)
ax_right.spines["right"].set_visible(False)
for s in ("left", "bottom"):
    ax_right.spines[s].set_color(INK_SOFT)
ax_right.xaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)

# Title
fig.text(
    0.5,
    0.98,
    "scatter-marginal · matplotlib · anyplot.ai",
    fontsize=24,
    ha="center",
    va="top",
    color=INK,
    fontweight="medium",
)

plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

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

Scatter Plot with Marginal Distributions — Matplotlib

Python source (Matplotlib)

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