Venn Diagram — Seaborn

A Venn diagram visualizes the logical relationships between two or three sets using overlapping circles. Each circle represents a set, and overlapping regions show elements shared between sets. This classic visualization is ideal for showing intersections, unions, and exclusive memberships, making abstract set relationships immediately intuitive.
Python source (Seaborn)

""" anyplot.ai
venn-basic: Venn Diagram
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-11
"""

import os

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns


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"

# Okabe-Ito palette (first series always #009E73)
IMPRINT = ["#009E73", "#C475FD", "#4467A3"]

np.random.seed(42)

# Data - Cloud platform adoption across organizations
# Set A: AWS users, Set B: Google Cloud users, Set C: Azure users
set_labels = ["AWS", "Google Cloud", "Azure"]
set_sizes = [120, 95, 75]  # Total in each group
# Overlaps: AB=35, AC=28, BC=22, ABC=12
intersections = {"AB": 35, "AC": 28, "BC": 22, "ABC": 12}

# Calculate exclusive counts for each region
only_a = set_sizes[0] - intersections["AB"] - intersections["AC"] + intersections["ABC"]
only_b = set_sizes[1] - intersections["AB"] - intersections["BC"] + intersections["ABC"]
only_c = set_sizes[2] - intersections["AC"] - intersections["BC"] + intersections["ABC"]
ab_only = intersections["AB"] - intersections["ABC"]
ac_only = intersections["AC"] - intersections["ABC"]
bc_only = intersections["BC"] - intersections["ABC"]
abc = intersections["ABC"]

# Total unique organizations using inclusion-exclusion principle
total_orgs = sum(set_sizes) - sum(intersections.values()) + intersections["ABC"]

# Set seaborn style with theme-adaptive colors
sns.set_theme(
    style="white",
    rc={"figure.facecolor": PAGE_BG, "axes.facecolor": PAGE_BG, "axes.labelcolor": INK, "text.color": INK},
)

# Create figure (square for symmetric diagram)
fig, ax = plt.subplots(figsize=(12, 12), facecolor=PAGE_BG)

# Circle positions (equilateral triangle arrangement)
r = 1.5  # Circle radius
center_offset = 0.9  # Distance from center

# Calculate centers for three overlapping circles
centers = [
    (0, center_offset),  # Top (A - AWS)
    (-center_offset * np.cos(np.pi / 6), -center_offset * np.sin(np.pi / 6)),  # Bottom-left (B - Google Cloud)
    (center_offset * np.cos(np.pi / 6), -center_offset * np.sin(np.pi / 6)),  # Bottom-right (C - Azure)
]

# Draw circles with transparency using Okabe-Ito colors
circles = []
for center, color, label in zip(centers, IMPRINT, set_labels, strict=True):
    circle = mpatches.Circle(center, r, alpha=0.4, facecolor=color, edgecolor=color, linewidth=3, label=label)
    ax.add_patch(circle)
    circles.append(circle)

# Position labels outside circles
label_offset = 2.3
label_positions = [
    (0, label_offset),  # Top
    (-label_offset * np.cos(np.pi / 6) - 0.3, -label_offset * np.sin(np.pi / 6) - 0.3),  # Bottom-left
    (label_offset * np.cos(np.pi / 6) + 0.3, -label_offset * np.sin(np.pi / 6) - 0.3),  # Bottom-right
]

for pos, label, size in zip(label_positions, set_labels, set_sizes, strict=True):
    ax.text(pos[0], pos[1], f"{label}\n(n={size})", ha="center", va="center", fontsize=22, fontweight="bold", color=INK)

# Add counts to each region
# Region positions (approximate centers of each region)
region_positions = {
    "A": (0, 1.3),  # Only AWS
    "B": (-1.2, -0.8),  # Only Google Cloud
    "C": (1.2, -0.8),  # Only Azure
    "AB": (-0.55, 0.3),  # AWS & Google Cloud
    "AC": (0.55, 0.3),  # AWS & Azure
    "BC": (0, -0.7),  # Google Cloud & Azure
    "ABC": (0, 0),  # All three
}

region_counts = {"A": only_a, "B": only_b, "C": only_c, "AB": ab_only, "AC": ac_only, "BC": bc_only, "ABC": abc}

for region, pos in region_positions.items():
    count = region_counts[region]
    pct = count / total_orgs * 100
    ax.text(
        pos[0],
        pos[1],
        f"{count}\n({pct:.0f}%)",
        ha="center",
        va="center",
        fontsize=20,
        fontweight="bold",
        color=INK,
        bbox={"boxstyle": "round,pad=0.3", "facecolor": ELEVATED_BG, "edgecolor": INK_SOFT, "alpha": 0.8},
    )

# Set axis properties - tighter bounds for better canvas utilization
ax.set_xlim(-3.0, 3.0)
ax.set_ylim(-2.8, 3.0)
ax.set_aspect("equal")
ax.axis("off")

# Title
ax.set_title("venn-basic · seaborn · anyplot.ai", fontsize=24, fontweight="bold", pad=20, color=INK)

# Add subtitle annotation explaining data context
fig.text(
    0.5,
    0.02,
    f"Cloud Platform Adoption: {total_orgs} organizations surveyed",
    ha="center",
    va="bottom",
    fontsize=14,
    style="italic",
    color=INK_SOFT,
)

plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Venn Diagram on anyplot.ai.
Venn Diagram — Seaborn

Python source (Seaborn)

Other implementations
