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feat(seaborn): implement subplot-grid-custom (#2875)
## Implementation: `subplot-grid-custom` - seaborn Implements the **seaborn** version of `subplot-grid-custom`. **File:** `plots/subplot-grid-custom/implementations/seaborn.py` **Parent Issue:** #2856 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20608469916)* --------- 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-grid-custom/implementations/seaborn.py‎

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""" pyplots.ai
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subplot-grid-custom: Custom Subplot Grid Layout
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Library: seaborn 0.13.2 | Python 3.13.11
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Quality: 91/100 | Created: 2025-12-30
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"""
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import matplotlib.dates as mdates
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import matplotlib.gridspec as gridspec
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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 plot (spanning 2 columns)
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dates = pd.date_range("2024-01-01", periods=120, freq="D")
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price = 100 + np.cumsum(np.random.randn(120) * 2)
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df_main = pd.DataFrame({"Date": dates, "Price": price})
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# Volume bar data
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volume = np.random.randint(1000, 5000, size=12)
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months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
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df_volume = pd.DataFrame({"Month": months, "Volume": volume})
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# Returns histogram data
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returns = np.diff(price) / price[:-1] * 100
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df_returns = pd.DataFrame({"Returns": returns})
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# Scatter plot data
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x_scatter = np.random.randn(80) * 15 + 50
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y_scatter = x_scatter * 0.8 + np.random.randn(80) * 10 + 20
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df_scatter = pd.DataFrame({"Variable_X": x_scatter, "Variable_Y": y_scatter})
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# Category boxplot data
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categories = np.repeat(["Q1", "Q2", "Q3", "Q4"], 30)
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values = np.concatenate(
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[
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np.random.normal(50, 10, 30),
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np.random.normal(55, 12, 30),
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np.random.normal(60, 8, 30),
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np.random.normal(65, 15, 30),
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]
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)
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df_box = pd.DataFrame({"Quarter": categories, "Performance": values})
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# Create figure with GridSpec layout
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fig = plt.figure(figsize=(16, 9))
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gs = gridspec.GridSpec(3, 3, figure=fig, hspace=0.45, wspace=0.30)
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# Main plot: Time series spanning 2 columns (top-left 2x2 area)
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ax_main = fig.add_subplot(gs[0:2, 0:2])
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sns.lineplot(data=df_main, x="Date", y="Price", ax=ax_main, linewidth=3, color="#306998")
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ax_main.set_title("Daily Price Trend", fontsize=20, fontweight="bold")
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ax_main.set_xlabel("Date", fontsize=16)
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ax_main.set_ylabel("Price ($)", fontsize=16)
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ax_main.tick_params(axis="both", labelsize=12)
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ax_main.tick_params(axis="x", rotation=0)
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ax_main.grid(True, alpha=0.3, linestyle="--")
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ax_main.fill_between(df_main["Date"], df_main["Price"], alpha=0.2, color="#306998")
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ax_main.xaxis.set_major_locator(plt.MaxNLocator(5))
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ax_main.xaxis.set_major_formatter(mdates.DateFormatter("%b %d"))
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# Top-right: Scatter plot
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ax_scatter = fig.add_subplot(gs[0, 2])
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sns.scatterplot(data=df_scatter, x="Variable_X", y="Variable_Y", ax=ax_scatter, s=120, alpha=0.7, color="#FFD43B")
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ax_scatter.set_title("Correlation Analysis", fontsize=16, fontweight="bold")
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ax_scatter.set_xlabel("Variable X", fontsize=14)
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ax_scatter.set_ylabel("Variable Y", fontsize=14)
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ax_scatter.tick_params(axis="both", labelsize=12)
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ax_scatter.grid(True, alpha=0.3, linestyle="--")
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# Middle-right: Boxplot (spanning vertically)
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ax_box = fig.add_subplot(gs[1, 2])
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sns.boxplot(data=df_box, x="Quarter", y="Performance", hue="Quarter", ax=ax_box, palette="Set2", legend=False)
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ax_box.set_title("Quarterly Performance", fontsize=16, fontweight="bold")
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ax_box.set_xlabel("Quarter", fontsize=14)
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ax_box.set_ylabel("Performance Score", fontsize=14)
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ax_box.tick_params(axis="both", labelsize=12)
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# Bottom-left: Volume bar chart
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ax_volume = fig.add_subplot(gs[2, 0])
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sns.barplot(data=df_volume, x="Month", y="Volume", hue="Month", ax=ax_volume, palette="viridis", legend=False)
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ax_volume.set_title("Monthly Volume", fontsize=16, fontweight="bold")
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ax_volume.set_xlabel("Month", fontsize=14)
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ax_volume.set_ylabel("Volume (Units)", fontsize=14)
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ax_volume.tick_params(axis="x", labelsize=10, rotation=45)
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ax_volume.tick_params(axis="y", labelsize=12)
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# Bottom-center: Returns histogram
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ax_hist = fig.add_subplot(gs[2, 1])
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sns.histplot(data=df_returns, x="Returns", kde=True, ax=ax_hist, color="#306998", alpha=0.7, bins=20)
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ax_hist.set_title("Returns Distribution", fontsize=16, fontweight="bold")
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ax_hist.set_xlabel("Daily Returns (%)", fontsize=14)
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ax_hist.set_ylabel("Frequency", fontsize=14)
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ax_hist.tick_params(axis="both", labelsize=12)
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ax_hist.axvline(x=0, color="#FFD43B", linewidth=2, linestyle="--", alpha=0.8)
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# Bottom-right: Summary heatmap (correlation-style)
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correlation_data = np.array([[1.0, 0.65, 0.42], [0.65, 1.0, 0.58], [0.42, 0.58, 1.0]])
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labels = ["Price", "Volume", "Returns"]
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ax_heatmap = fig.add_subplot(gs[2, 2])
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sns.heatmap(
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correlation_data,
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annot=True,
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fmt=".2f",
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xticklabels=labels,
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yticklabels=labels,
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ax=ax_heatmap,
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cmap="RdBu_r",
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center=0,
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vmin=-1,
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vmax=1,
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annot_kws={"size": 14},
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cbar_kws={"shrink": 0.8},
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)
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ax_heatmap.set_title("Correlation Matrix", fontsize=16, fontweight="bold")
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ax_heatmap.tick_params(axis="both", labelsize=12)
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# Main title
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fig.suptitle("subplot-grid-custom · seaborn · pyplots.ai", fontsize=24, fontweight="bold", y=0.98)
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plt.savefig("plot.png", dpi=300, bbox_inches="tight")

‎plots/subplot-grid-custom/metadata/seaborn.yaml‎

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library: seaborn
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specification_id: subplot-grid-custom
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created: '2025-12-30T23:54:20Z'
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updated: '2025-12-30T23:58:58Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20608469916
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issue: 2856
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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-grid-custom/seaborn/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/subplot-grid-custom/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 dashboard layout demonstrating GridSpec with main plot spanning 2x2
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cells
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- Good variety of seaborn plot types (6 different visualizations) showcasing library
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breadth
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- Financial dashboard scenario is realistic and coherent with interconnected data
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(price, volume, returns, correlations)
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- Proper use of seaborn 0.14+ API with hue parameter for palettes
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- Clean color scheme with colorblind-safe palettes (viridis, Set2, RdBu_r)
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weaknesses:
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- Missing rowspan example - spec mentions cells spanning multiple rows (e.g., tall
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sidebar plot) but all cells only span columns
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- Heatmap y-axis label text appears concatenated/overlapping due to tight spacing

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