feat(matplotlib): implement facet-grid (#2753)

## Implementation: `facet-grid` - matplotlib

Implements the **matplotlib** version of `facet-grid`.

**File:** `plots/facet-grid/implementations/matplotlib.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20601057118)*

---------

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/facet-grid/implementations/matplotlib.py

@@ -0,0 +1,83 @@
+""" pyplots.ai
+facet-grid: Faceted Grid Plot
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+# Data
+np.random.seed(42)
+
+# Create sample data with two categorical variables for faceting
+regions = ["North", "South", "East"]
+seasons = ["Spring", "Summer", "Fall", "Winter"]
+
+data = []
+for region in regions:
+    for season in seasons:
+        n_points = 25
+        # Generate temperature vs energy consumption relationship
+        # Different patterns per region/season combination
+        base_temp = {"North": 10, "South": 25, "East": 18}[region]
+        season_offset = {"Spring": 5, "Summer": 15, "Fall": 0, "Winter": -10}[season]
+
+        temp = np.random.normal(base_temp + season_offset, 5, n_points)
+        # Energy consumption varies with temperature
+        energy = 100 + (temp - 15) ** 2 * 0.3 + np.random.normal(0, 10, n_points)
+
+        for t, e in zip(temp, energy, strict=True):
+            data.append({"Temperature": t, "Energy": e, "Region": region, "Season": season})
+
+df = pd.DataFrame(data)
+
+# Plot - Faceted grid: rows = regions, columns = seasons
+fig, axes = plt.subplots(nrows=len(regions), ncols=len(seasons), figsize=(16, 9), sharex=True, sharey=True)
+
+# Colors for regions
+colors = {"North": "#306998", "South": "#FFD43B", "East": "#4DAF4A"}
+
+# Create scatter plots in each facet
+for i, region in enumerate(regions):
+    for j, season in enumerate(seasons):
+        ax = axes[i, j]
+        subset = df[(df["Region"] == region) & (df["Season"] == season)]
+
+        ax.scatter(
+            subset["Temperature"],
+            subset["Energy"],
+            s=120,
+            alpha=0.7,
+            color=colors[region],
+            edgecolor="white",
+            linewidth=0.5,
+        )
+
+        # Add grid
+        ax.grid(True, alpha=0.3, linestyle="--")
+
+        # Column headers (top row only)
+        if i == 0:
+            ax.set_title(season, fontsize=18, fontweight="bold")
+
+        # Row labels (rightmost column only)
+        if j == len(seasons) - 1:
+            ax.annotate(
+                region, xy=(1.05, 0.5), xycoords="axes fraction", fontsize=16, fontweight="bold", va="center", ha="left"
+            )
+
+        # Tick parameters
+        ax.tick_params(axis="both", labelsize=12)
+
+# Shared axis labels
+fig.supxlabel("Temperature (°C)", fontsize=20, y=0.02)
+fig.supylabel("Energy Consumption (kWh)", fontsize=20, x=0.02)
+
+# Main title
+fig.suptitle("facet-grid · matplotlib · pyplots.ai", fontsize=24, y=0.98)
+
+plt.tight_layout(rect=[0.04, 0.04, 0.95, 0.95])
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/facet-grid/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: facet-grid
+created: '2025-12-30T16:30:15Z'
+updated: '2025-12-30T16:38:05Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20601057118
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/facet-grid/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/facet-grid/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent two-way faceting implementation with clear row and column structure
+  - Realistic temperature vs energy consumption scenario that varies meaningfully
+    across regions and seasons
+  - Clean KISS code structure with appropriate use of pandas for data management
+  - Good color palette that distinguishes regions while remaining colorblind-accessible
+  - Proper use of shared axes (sharex=True, sharey=True) for easy comparison across
+    facets
+  - Effective use of supxlabel/supylabel for shared axis labels
+  - White edge on markers improves visibility when points overlap
+  weaknesses:
+  - Grid and legend score could be improved by adding a small legend explaining region
+    colors
+  - Row labels positioned with xy=(1.05, 0.5) are functional but tight_layout rect
+    may clip them slightly
+  - South summer temperatures reaching ~50°C are on the extreme end for realistic
+    data