feat(plotnine): implement bar-categorical (#2659)

## Implementation: `bar-categorical` - plotnine

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20595336970)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/bar-categorical/implementations/plotnine.py

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+""" pyplots.ai
+bar-categorical: Categorical Count Bar Chart
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import aes, element_blank, element_text, geom_bar, ggplot, labs, scale_fill_manual, theme, theme_minimal
+
+
+# Data - Survey responses for product satisfaction
+np.random.seed(42)
+categories = ["Excellent", "Good", "Average", "Poor", "Very Poor"]
+# Create uneven distribution to show varying counts
+weights = [0.25, 0.35, 0.20, 0.12, 0.08]
+n_responses = 200
+responses = np.random.choice(categories, size=n_responses, p=weights)
+
+df = pd.DataFrame({"Satisfaction": responses})
+
+# Reorder categories for logical display
+df["Satisfaction"] = pd.Categorical(df["Satisfaction"], categories=categories, ordered=True)
+
+# Plot
+plot = (
+    ggplot(df, aes(x="Satisfaction", fill="Satisfaction"))
+    + geom_bar(width=0.7, alpha=0.9)
+    + labs(x="Satisfaction Level", y="Number of Responses", title="bar-categorical · plotnine · pyplots.ai")
+    + scale_fill_manual(values=["#306998", "#4A90C2", "#FFD43B", "#E8A838", "#CC6633"])
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        text=element_text(size=14),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        plot_title=element_text(size=24),
+        legend_position="none",
+        panel_grid_major_x=element_blank(),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

plots/bar-categorical/metadata/plotnine.yaml

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+library: plotnine
+specification_id: bar-categorical
+created: '2025-12-30T11:22:13Z'
+updated: '2025-12-30T11:28:59Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595336970
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/bar-categorical/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bar-categorical/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent use of plotnine grammar of graphics with geom_bar() for automatic count
+    computation
+  - Smart use of pd.Categorical to enforce logical ordering of satisfaction levels
+  - Clean, publication-quality visual design with appropriate font sizes for 4800x2700
+    resolution
+  - Good choice of diverging color palette that semantically matches satisfaction
+    levels (blue=good, orange=poor)
+  - Proper KISS code structure with reproducible seed
+  weaknesses:
+  - Could use scale_fill_brewer() with a diverging palette instead of manual colors
+    for better colorblind accessibility
+  - Missing count annotations on bars which would enhance readability