feat(matplotlib): implement bar-categorical (#2664)

## Implementation: `bar-categorical` - matplotlib

Implements the **matplotlib** version of `bar-categorical`.

**File:** `plots/bar-categorical/implementations/matplotlib.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20595333486)*

---------

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plots/bar-categorical/implementations/matplotlib.py

@@ -0,0 +1,52 @@
+""" pyplots.ai
+bar-categorical: Categorical Count Bar Chart
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 94/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+# Data - survey responses for product preference
+np.random.seed(42)
+categories = ["Product A", "Product B", "Product C", "Product D", "Product E"]
+probabilities = [0.30, 0.25, 0.20, 0.15, 0.10]  # Different frequencies for variety
+raw_data = np.random.choice(categories, size=500, p=probabilities)
+
+# Count frequencies
+df = pd.DataFrame({"Category": raw_data})
+counts = df["Category"].value_counts().sort_values(ascending=False)
+
+# Create plot (4800x2700 px at 300 dpi = 16x9 inches)
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Bar chart with Python Blue
+bars = ax.bar(counts.index, counts.values, color="#306998", edgecolor="#1e4a6e", linewidth=2)
+
+# Add value labels on top of bars
+for bar, value in zip(bars, counts.values, strict=True):
+    ax.text(
+        bar.get_x() + bar.get_width() / 2,
+        bar.get_height() + 5,
+        str(value),
+        ha="center",
+        va="bottom",
+        fontsize=18,
+        fontweight="bold",
+    )
+
+# Styling
+ax.set_xlabel("Product Category", fontsize=20)
+ax.set_ylabel("Count (Frequency)", fontsize=20)
+ax.set_title("bar-categorical · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, axis="y", alpha=0.3, linestyle="--")
+
+# Remove top and right spines for cleaner look
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/bar-categorical/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: bar-categorical
+created: '2025-12-30T11:22:26Z'
+updated: '2025-12-30T11:29:47Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595333486
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/bar-categorical/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bar-categorical/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 94
+review:
+  strengths:
+  - Excellent text legibility with appropriate font sizes for 4800x2700 output
+  - Clean KISS code structure following the imports → data → plot → save pattern
+  - Realistic survey data scenario with varied frequency distribution
+  - Proper descending sort as suggested in the specification
+  - Value labels on bars enhance readability and data communication
+  - Professional appearance with removed top/right spines and subtle grid
+  weaknesses:
+  - Could use more distinctive matplotlib features (e.g., custom hatching, gradient
+    fills, or annotation arrows)
+  - Y-axis label "Count (Frequency)" is slightly redundant - could be simplified