Box Plot with Strip Overlay — Matplotlib

A combined visualization that overlays individual data points (strip plot) on top of a box plot. This provides both summary statistics (median, quartiles, whiskers) and visibility of the actual data distribution.

Box Plot with Strip Overlay rendered with Matplotlib

Python source (Matplotlib)

""" anyplot.ai
cat-box-strip: Box Plot with Strip Overlay
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-13
"""

import os

import matplotlib.pyplot as plt
import numpy as np


# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"

BRAND = "#009E73"  # Okabe-Ito position 1

# Data - Generate groups with different distributions to showcase features
np.random.seed(42)

categories = ["Control", "Treatment A", "Treatment B", "Treatment C"]
n_points = [35, 40, 30, 45]  # Different sample sizes per group

# Create varied distributions to show boxplot features
data = {
    "Control": np.random.normal(50, 8, n_points[0]),
    "Treatment A": np.random.normal(65, 12, n_points[1]),  # Higher mean, more spread
    "Treatment B": np.concatenate(
        [  # Bimodal with outliers
            np.random.normal(45, 5, n_points[2] - 3),
            np.array([15, 80, 82]),  # Outliers
        ]
    ),
    "Treatment C": np.random.normal(55, 6, n_points[3]),  # Moderate
}

# Prepare data for plotting
box_data = [data[cat] for cat in categories]
positions = np.arange(len(categories)) + 1

# Plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

# Box plot
ax.boxplot(
    box_data,
    positions=positions,
    tick_labels=categories,
    widths=0.5,
    patch_artist=True,
    boxprops={"facecolor": BRAND, "alpha": 0.4, "linewidth": 2, "edgecolor": INK_SOFT},
    medianprops={"color": "#AE3030", "linewidth": 3},
    whiskerprops={"color": INK_SOFT, "linewidth": 2},
    capprops={"color": INK_SOFT, "linewidth": 2},
    flierprops={
        "marker": "o",
        "markerfacecolor": BRAND,
        "markersize": 10,
        "alpha": 0.7,
        "markeredgecolor": INK_SOFT,
        "markeredgewidth": 1,
    },
)

# Strip plot overlay - add jittered points
for pos, cat in zip(positions, categories, strict=True):
    y = data[cat]
    # Jitter x positions
    x = np.random.normal(pos, 0.08, len(y))
    ax.scatter(x, y, s=100, alpha=0.6, color=BRAND, edgecolor=PAGE_BG, linewidth=1, zorder=3)

# Style
ax.set_xlabel("Treatment Group", fontsize=20, color=INK)
ax.set_ylabel("Response Value (score)", fontsize=20, color=INK)
ax.set_title("cat-box-strip · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT, labelcolor=INK_SOFT)

# Spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
    ax.spines[s].set_color(INK_SOFT)

# Grid
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK)

# Adjust y-axis to show all data including outliers
ax.set_ylim(0, 100)

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

Part of Box Plot with Strip Overlay on anyplot.ai.

Box Plot with Strip Overlay — Matplotlib

Python source (Matplotlib)

Other implementations