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feat(seaborn): implement venn-basic (#2454)
## Implementation: `venn-basic` - seaborn Implements the **seaborn** version of `venn-basic`. **File:** `plots/venn-basic/implementations/seaborn.py` --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20584328752)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/venn-basic/implementations/seaborn.py‎

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""" pyplots.ai
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venn-basic: Venn Diagram
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Library: seaborn 0.13.2 | Python 3.13.11
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Quality: 90/100 | Created: 2025-12-29
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"""
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import matplotlib.patches as mpatches
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import matplotlib.pyplot as plt
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import numpy as np
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import seaborn as sns
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np.random.seed(42)
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# Data - Survey results on programming language preferences
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# Set A: Python users, Set B: JavaScript users, Set C: SQL users
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set_labels = ["Python", "JavaScript", "SQL"]
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set_sizes = [100, 80, 60] # Total in each group
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# Overlaps: AB=30, AC=20, BC=25, ABC=10
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intersections = {"AB": 30, "AC": 20, "BC": 25, "ABC": 10}
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# Calculate exclusive counts for each region
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only_a = set_sizes[0] - intersections["AB"] - intersections["AC"] + intersections["ABC"]
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only_b = set_sizes[1] - intersections["AB"] - intersections["BC"] + intersections["ABC"]
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only_c = set_sizes[2] - intersections["AC"] - intersections["BC"] + intersections["ABC"]
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ab_only = intersections["AB"] - intersections["ABC"]
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ac_only = intersections["AC"] - intersections["ABC"]
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bc_only = intersections["BC"] - intersections["ABC"]
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abc = intersections["ABC"]
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# Total unique respondents using inclusion-exclusion principle
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total_respondents = sum(set_sizes) - sum(intersections.values()) + intersections["ABC"]
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# Set seaborn style with custom context
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sns.set_theme(style="white", context="talk", font_scale=1.2)
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# Create figure (square for symmetric diagram)
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fig, ax = plt.subplots(figsize=(12, 12))
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# Get colors from seaborn palette - using colorblind-safe Set2
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palette = sns.color_palette("Set2", n_colors=3)
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colors = list(palette)
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# Circle positions (equilateral triangle arrangement)
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r = 1.5 # Circle radius
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center_offset = 0.9 # Distance from center
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# Calculate centers for three overlapping circles
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centers = [
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(0, center_offset), # Top (A - Python)
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(-center_offset * np.cos(np.pi / 6), -center_offset * np.sin(np.pi / 6)), # Bottom-left (B - JavaScript)
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(center_offset * np.cos(np.pi / 6), -center_offset * np.sin(np.pi / 6)), # Bottom-right (C - SQL)
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]
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# Draw circles with transparency
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circles = []
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for center, color, label in zip(centers, colors, set_labels, strict=True):
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circle = mpatches.Circle(center, r, alpha=0.4, facecolor=color, edgecolor=color, linewidth=3, label=label)
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ax.add_patch(circle)
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circles.append(circle)
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# Position labels outside circles
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label_offset = 2.3
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label_positions = [
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(0, label_offset), # Top
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(-label_offset * np.cos(np.pi / 6) - 0.3, -label_offset * np.sin(np.pi / 6) - 0.3), # Bottom-left
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(label_offset * np.cos(np.pi / 6) + 0.3, -label_offset * np.sin(np.pi / 6) - 0.3), # Bottom-right
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]
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for pos, label, size in zip(label_positions, set_labels, set_sizes, strict=True):
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ax.text(
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pos[0],
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pos[1],
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f"{label}\n(n={size})",
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ha="center",
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va="center",
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fontsize=22,
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fontweight="bold",
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color="#333333",
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)
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# Add counts to each region
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# Region positions (approximate centers of each region)
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region_positions = {
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"A": (0, 1.3), # Only Python
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"B": (-1.2, -0.8), # Only JavaScript
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"C": (1.2, -0.8), # Only SQL
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"AB": (-0.55, 0.3), # Python & JavaScript
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"AC": (0.55, 0.3), # Python & SQL
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"BC": (0, -0.7), # JavaScript & SQL
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"ABC": (0, 0), # All three
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}
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region_counts = {"A": only_a, "B": only_b, "C": only_c, "AB": ab_only, "AC": ac_only, "BC": bc_only, "ABC": abc}
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for region, pos in region_positions.items():
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count = region_counts[region]
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pct = count / total_respondents * 100
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ax.text(
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pos[0],
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pos[1],
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f"{count}\n({pct:.0f}%)",
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ha="center",
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va="center",
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fontsize=20,
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fontweight="bold",
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color="#333333",
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bbox={"boxstyle": "round,pad=0.3", "facecolor": "white", "edgecolor": "none", "alpha": 0.8},
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)
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# Set axis properties - tighter bounds for better canvas utilization
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ax.set_xlim(-3.0, 3.0)
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ax.set_ylim(-2.8, 3.0)
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ax.set_aspect("equal")
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ax.axis("off")
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# Title
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ax.set_title("venn-basic · seaborn · pyplots.ai", fontsize=24, fontweight="bold", pad=20)
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# Add subtitle annotation explaining data context (using computed total)
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fig.text(
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0.5,
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0.02,
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f"Developer Survey 2024: Language preferences among {total_respondents} respondents",
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ha="center",
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va="bottom",
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fontsize=14,
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style="italic",
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color="#666666",
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)
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plt.tight_layout()
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plt.savefig("plot.png", dpi=300, bbox_inches="tight")

‎plots/venn-basic/metadata/seaborn.yaml‎

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library: seaborn
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specification_id: venn-basic
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created: '2025-12-29T22:47:29Z'
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updated: '2025-12-29T23:14:11Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20584328752
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issue: 0
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python_version: 3.13.11
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library_version: 0.13.2
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preview_url: https://storage.googleapis.com/pyplots-images/plots/venn-basic/seaborn/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/venn-basic/seaborn/plot_thumb.png
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preview_html: null
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quality_score: 90
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review:
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strengths:
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- Excellent text legibility with white background boxes ensuring readability against
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all overlay colors
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- Mathematically correct implementation using inclusion-exclusion principle for
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region counts
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- Good use of seaborn Set2 colorblind-safe palette and styling context
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- Clear labeling with both counts and percentages in each region
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- Clean code structure following KISS principles
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weaknesses:
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- Minor asymmetry in external label positioning (manual -0.3 offsets for bottom
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labels)
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- Limited use of seaborn-specific features (seaborn has no native Venn support,
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so matplotlib patches are required)

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