feat(plotnine): implement subplot-grid (#2824)

## Implementation: `subplot-grid` - plotnine

Implements the **plotnine** version of `subplot-grid`.

**File:** `plots/subplot-grid/implementations/plotnine.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20602450179)*

---------

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>

Author: github-actions[bot]

plots/subplot-grid/implementations/plotnine.py

@@ -0,0 +1,132 @@
+""" pyplots.ai
+subplot-grid: Subplot Grid Layout
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    element_line,
+    element_rect,
+    element_text,
+    geom_bar,
+    geom_histogram,
+    geom_line,
+    geom_point,
+    ggplot,
+    labs,
+    scale_color_manual,
+    scale_fill_manual,
+    scale_x_continuous,
+    stat_smooth,
+    theme,
+    theme_minimal,
+)
+
+
+# Data - Product performance dashboard
+np.random.seed(42)
+
+# Daily product metrics
+n_days = 40
+days = pd.date_range("2024-01-01", periods=n_days, freq="D")
+products = ["A", "B"]
+
+# Generate time series data
+data_list = []
+for product in products:
+    base = 100 if product == "A" else 85
+    trend = 0.5 if product == "A" else 0.8
+    sales = base + np.arange(n_days) * trend + np.random.randn(n_days) * 10
+    data_list.append(pd.DataFrame({"date": days, "sales": sales, "product": product}))
+
+df_timeseries = pd.concat(data_list, ignore_index=True)
+df_timeseries["day_num"] = (df_timeseries["date"] - df_timeseries["date"].min()).dt.days
+
+# Category breakdown data
+categories = ["Q1", "Q2", "Q3", "Q4"]
+revenues = [45, 32, 28, 18]
+df_category = pd.DataFrame({"category": categories, "revenue": revenues})
+df_category["category"] = pd.Categorical(df_category["category"], categories=categories, ordered=True)
+
+# Product distribution data
+df_prod_a = df_timeseries[df_timeseries["product"] == "A"]["sales"]
+
+# Scatter data - relationship between units sold and profit margin
+units = np.random.uniform(100, 500, 60)
+margin = 20 + 0.03 * units + np.random.randn(60) * 5
+df_scatter = pd.DataFrame({"units": units, "margin": margin})
+
+# Define colors
+colors = ["#306998", "#FFD43B"]
+
+# Shared theme for all plots - sized for 4800x2700 canvas
+base_theme = theme_minimal() + theme(
+    plot_title=element_text(size=22, face="bold", ha="center", margin={"b": 15}),
+    axis_title=element_text(size=18),
+    axis_text=element_text(size=14),
+    legend_text=element_text(size=14),
+    legend_title=element_text(size=16, face="bold"),
+    panel_grid_major=element_line(color="#cccccc", alpha=0.3),
+    panel_grid_minor=element_line(color="#eeeeee", alpha=0.2),
+    panel_background=element_rect(fill="white"),
+    plot_margin=0.02,
+)
+
+# Plot 1: Sales trend over time (Line chart)
+p1 = (
+    ggplot(df_timeseries, aes(x="day_num", y="sales", color="product"))
+    + geom_line(size=1.5)
+    + geom_point(size=3, alpha=0.7)
+    + stat_smooth(method="lm", se=False, linetype="dashed", size=1.0)
+    + scale_color_manual(values=colors)
+    + labs(title="Sales Trend", x="Day", y="Sales (Units)", color="Product")
+    + base_theme
+    + theme(legend_position="right")
+)
+
+# Plot 2: Revenue by category (Bar chart)
+p2 = (
+    ggplot(df_category, aes(x="category", y="revenue", fill="category"))
+    + geom_bar(stat="identity", width=0.7, show_legend=False)
+    + scale_fill_manual(values=["#306998", "#4A8BBF", "#6BA3D6", "#FFD43B"])
+    + labs(title="Quarterly Revenue", x="Quarter", y="Revenue (k$)")
+    + base_theme
+)
+
+# Plot 3: Sales distribution histogram for Product A
+df_hist = pd.DataFrame({"sales": df_prod_a.values})
+p3 = (
+    ggplot(df_hist, aes(x="sales"))
+    + geom_histogram(bins=10, fill="#306998", color="white", alpha=0.8)
+    + scale_x_continuous(breaks=[90, 105, 120])
+    + labs(title="Sales Distribution (Product A)", x="Sales (Units)", y="Frequency")
+    + base_theme
+)
+
+# Plot 4: Units vs Margin scatter plot
+p4 = (
+    ggplot(df_scatter, aes(x="units", y="margin"))
+    + geom_point(size=4, color="#306998", alpha=0.7)
+    + stat_smooth(method="lm", color="#FFD43B", se=True, fill="#FFD43B", alpha=0.2)
+    + labs(title="Units vs Margin", x="Units Sold", y="Profit Margin (%)")
+    + base_theme
+)
+
+# Compose into 2x2 grid using plotnine's composition operators
+# | = beside (columns), / = stack (rows)
+top_row = p1 | p2
+bottom_row = p3 | p4
+grid = top_row / bottom_row
+
+# Draw the grid and add a main figure title using matplotlib's suptitle
+fig = grid.draw()
+# Resize figure with extra height for the main title (16:9 base + title space)
+fig.set_size_inches(16, 11)
+# Compress subplots significantly to make room for the main title at the top
+fig.subplots_adjust(top=0.76, bottom=0.08, hspace=0.40, wspace=0.25)
+# Add main title using suptitle positioned well above all subplot titles
+fig.suptitle("subplot-grid · plotnine · pyplots.ai", fontsize=28, fontweight="bold", y=0.90)
+fig.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/subplot-grid/metadata/plotnine.yaml

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+library: plotnine
+specification_id: subplot-grid
+created: '2025-12-30T17:50:18Z'
+updated: '2025-12-30T18:22:27Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602450179
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/subplot-grid/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/subplot-grid/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent use of plotnine native patchwork-style composition operators (| and
+    /) for grid layout
+  - Clean implementation of 4 distinct plot types demonstrating subplot-grid versatility
+  - Good color scheme with consistent blue-yellow palette across all subplots
+  - Proper use of theme customization with shared base_theme for consistency
+  - Realistic business dashboard scenario with coherent metrics
+  weaknesses:
+  - Main title positioning overlaps slightly with Sales Trend subplot title - could
+    benefit from more vertical spacing
+  - The legend in the sales trend plot consumes plot area; consider positioning outside