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feat(seaborn): implement subplot-mosaic (#3029)
## Implementation: `subplot-mosaic` - seaborn Implements the **seaborn** version of `subplot-mosaic`. **File:** `plots/subplot-mosaic/implementations/seaborn.py` **Parent Issue:** #3002 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20617508029)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/subplot-mosaic/implementations/seaborn.py‎

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""" pyplots.ai
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subplot-mosaic: Mosaic Subplot Layout with Varying Sizes
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Library: seaborn 0.13.2 | Python 3.13.11
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Quality: 91/100 | Created: 2025-12-31
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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# Data
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np.random.seed(42)
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# Time series data for main overview (Panel A - wide)
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dates = pd.date_range("2024-01-01", periods=100, freq="D")
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revenue = np.cumsum(np.random.randn(100) * 500 + 200) + 50000
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df_overview = pd.DataFrame({"Date": dates, "Revenue ($)": revenue})
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# Scatter data for panel B (top right)
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df_scatter = pd.DataFrame(
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{
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"Marketing Spend ($K)": np.random.uniform(10, 100, 50),
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"Conversions": np.random.uniform(100, 1000, 50) + np.random.randn(50) * 100,
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}
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)
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# Bar data for panel C (middle left detail)
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categories = ["Online", "Retail", "Partner", "Direct"]
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df_bar = pd.DataFrame({"Channel": categories, "Sales ($K)": [450, 320, 180, 275]})
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# Histogram data for panel D (middle right detail)
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response_times = np.concatenate([np.random.normal(45, 10, 300), np.random.normal(120, 25, 100)])
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df_hist = pd.DataFrame({"Response Time (ms)": response_times})
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# Line data for panel E (bottom left metric)
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hours = np.arange(24)
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cpu_usage = 30 + 20 * np.sin(hours * np.pi / 12) + np.random.randn(24) * 5
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df_cpu = pd.DataFrame({"Hour": hours, "CPU Usage (%)": cpu_usage})
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# Line data for panel F (bottom center metric)
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memory_usage = 55 + 15 * np.sin(hours * np.pi / 10 + 2) + np.random.randn(24) * 3
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df_memory = pd.DataFrame({"Hour": hours, "Memory Usage (%)": memory_usage})
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# Box data for panel G (bottom right metric)
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regions = ["North", "South", "East", "West"]
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df_box = pd.DataFrame(
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{
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"Region": np.repeat(regions, 30),
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"Latency (ms)": np.concatenate(
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[
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np.random.normal(25, 5, 30),
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np.random.normal(35, 8, 30),
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np.random.normal(28, 4, 30),
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np.random.normal(40, 10, 30),
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]
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),
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}
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)
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# Create mosaic layout: "AAB;CCD;EFG" pattern
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# A spans 2 cols (wide overview), B is 1 col (scatter)
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# C spans 2 cols (bar chart middle), D is 1 col (histogram)
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# E, F, G each 1 col (three small metrics)
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fig, axes = plt.subplot_mosaic(
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[["A", "A", "B"], ["C", "C", "D"], ["E", "F", "G"]], figsize=(16, 9), height_ratios=[1.2, 1, 0.8]
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)
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# Panel A: Revenue Overview (Line plot - wide)
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sns.lineplot(data=df_overview, x="Date", y="Revenue ($)", ax=axes["A"], color="#306998", linewidth=2.5)
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axes["A"].set_title("Revenue Trend Overview", fontsize=18, fontweight="bold")
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axes["A"].set_xlabel("Date", fontsize=14)
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axes["A"].set_ylabel("Revenue ($)", fontsize=14)
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axes["A"].tick_params(axis="both", labelsize=11)
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axes["A"].xaxis.set_major_locator(plt.MaxNLocator(6))
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axes["A"].grid(True, alpha=0.3, linestyle="--")
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# Panel B: Marketing vs Conversions (Scatter)
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sns.scatterplot(
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data=df_scatter,
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x="Marketing Spend ($K)",
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y="Conversions",
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ax=axes["B"],
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color="#FFD43B",
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s=100,
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alpha=0.7,
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edgecolor="#306998",
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linewidth=1,
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)
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axes["B"].set_title("Marketing ROI", fontsize=16, fontweight="bold")
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axes["B"].set_xlabel("Marketing Spend ($K)", fontsize=13)
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axes["B"].set_ylabel("Conversions", fontsize=13)
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axes["B"].tick_params(axis="both", labelsize=10)
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axes["B"].grid(True, alpha=0.3, linestyle="--")
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# Panel C: Sales by Channel (Bar - wide)
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sns.barplot(
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data=df_bar,
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x="Channel",
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y="Sales ($K)",
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ax=axes["C"],
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hue="Channel",
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palette=["#306998", "#FFD43B", "#4B8BBE", "#FFE873"],
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legend=False,
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)
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axes["C"].set_title("Sales by Channel", fontsize=16, fontweight="bold")
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axes["C"].set_xlabel("Channel", fontsize=13)
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axes["C"].set_ylabel("Sales ($K)", fontsize=13)
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axes["C"].tick_params(axis="both", labelsize=10)
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axes["C"].grid(True, axis="y", alpha=0.3, linestyle="--")
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# Panel D: Response Time Distribution (Histogram)
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sns.histplot(data=df_hist, x="Response Time (ms)", ax=axes["D"], bins=30, color="#306998", alpha=0.7, edgecolor="white")
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axes["D"].set_title("Response Times", fontsize=16, fontweight="bold")
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axes["D"].set_xlabel("Response Time (ms)", fontsize=13)
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axes["D"].set_ylabel("Count", fontsize=13)
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axes["D"].tick_params(axis="both", labelsize=10)
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axes["D"].grid(True, axis="y", alpha=0.3, linestyle="--")
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# Panel E: CPU Usage (Small line)
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sns.lineplot(data=df_cpu, x="Hour", y="CPU Usage (%)", ax=axes["E"], color="#306998", linewidth=2)
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axes["E"].set_title("CPU Usage", fontsize=14, fontweight="bold")
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axes["E"].set_xlabel("Hour", fontsize=11)
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axes["E"].set_ylabel("CPU (%)", fontsize=11)
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axes["E"].tick_params(axis="both", labelsize=9)
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axes["E"].set_xticks([0, 6, 12, 18, 23])
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axes["E"].grid(True, alpha=0.3, linestyle="--")
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# Panel F: Memory Usage (Small line)
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sns.lineplot(data=df_memory, x="Hour", y="Memory Usage (%)", ax=axes["F"], color="#FFD43B", linewidth=2)
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axes["F"].set_title("Memory Usage", fontsize=14, fontweight="bold")
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axes["F"].set_xlabel("Hour", fontsize=11)
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axes["F"].set_ylabel("Memory (%)", fontsize=11)
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axes["F"].tick_params(axis="both", labelsize=9)
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axes["F"].set_xticks([0, 6, 12, 18, 23])
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axes["F"].grid(True, alpha=0.3, linestyle="--")
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# Panel G: Latency by Region (Small box)
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sns.boxplot(
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data=df_box,
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x="Region",
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y="Latency (ms)",
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ax=axes["G"],
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hue="Region",
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palette=["#306998", "#FFD43B", "#4B8BBE", "#FFE873"],
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legend=False,
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)
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axes["G"].set_title("Latency", fontsize=14, fontweight="bold")
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axes["G"].set_xlabel("Region", fontsize=11)
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axes["G"].set_ylabel("Latency (ms)", fontsize=11)
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axes["G"].tick_params(axis="both", labelsize=9)
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axes["G"].grid(True, axis="y", alpha=0.3, linestyle="--")
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# Main title
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fig.suptitle("subplot-mosaic · seaborn · pyplots.ai", fontsize=22, fontweight="bold", y=0.98)
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plt.tight_layout(rect=[0, 0, 1, 0.95])
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plt.savefig("plot.png", dpi=300, bbox_inches="tight")

‎plots/subplot-mosaic/metadata/seaborn.yaml‎

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library: seaborn
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specification_id: subplot-mosaic
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created: '2025-12-31T10:56:16Z'
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updated: '2025-12-31T11:06:47Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20617508029
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issue: 3002
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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/subplot-mosaic/seaborn/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/subplot-mosaic/seaborn/plot_thumb.png
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preview_html: null
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quality_score: 91
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review:
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strengths:
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- Excellent implementation of complex mosaic layout demonstrating the ASCII-art
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style pattern AAB;CCD;EFG
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- Clear visual hierarchy with larger overview panel at top and smaller metric panels
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at bottom
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- Professional color scheme using consistent Python-themed blue/yellow palette
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- Good use of height_ratios to create visual emphasis on different panels
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- Clean, readable code following KISS principles
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- Realistic business dashboard scenario with appropriate data ranges
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- All seaborn plotting functions used correctly with modern API (hue parameter)
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weaknesses:
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- Main title font size (22pt) is slightly below the recommended 24pt for optimal
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legibility at 4800x2700
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- Could leverage more seaborn-distinctive features like regression lines, KDE overlays,
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or statistical annotations
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- Feature coverage could include additional plot types to better demonstrate mosaic
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flexibility

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