feat(seaborn): implement bar-categorical (#2649)

## Implementation: `bar-categorical` - seaborn

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20595333274)*

---------

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plots/bar-categorical/implementations/seaborn.py

@@ -0,0 +1,49 @@
+""" pyplots.ai
+bar-categorical: Categorical Count Bar Chart
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+
+# Data - Survey responses about preferred programming languages
+np.random.seed(42)
+languages = ["Python", "JavaScript", "Java", "C++", "Go", "Rust", "TypeScript"]
+# Weighted probabilities to create realistic distribution (Python most popular)
+weights = [0.28, 0.22, 0.15, 0.12, 0.10, 0.07, 0.06]
+responses = np.random.choice(languages, size=500, p=weights)
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Use seaborn countplot for automatic frequency counting
+sns.countplot(
+    x=responses,
+    order=["Python", "JavaScript", "Java", "C++", "Go", "Rust", "TypeScript"],
+    hue=responses,
+    hue_order=["Python", "JavaScript", "Java", "C++", "Go", "Rust", "TypeScript"],
+    palette=["#306998", "#FFD43B", "#5382A1", "#00599C", "#00ADD8", "#DEA584", "#3178C6"],
+    legend=False,
+    ax=ax,
+)
+
+# Add count labels on top of bars
+for container in ax.containers:
+    ax.bar_label(container, fontsize=16, padding=5)
+
+# Styling
+ax.set_xlabel("Programming Language", fontsize=20)
+ax.set_ylabel("Number of Responses", fontsize=20)
+ax.set_title("bar-categorical · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(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/seaborn.yaml

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+library: seaborn
+specification_id: bar-categorical
+created: '2025-12-30T11:21:28Z'
+updated: '2025-12-30T11:24:36Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595333274
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/bar-categorical/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bar-categorical/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 93
+review:
+  strengths:
+  - Excellent use of sns.countplot for automatic frequency counting - the core seaborn
+    feature for this plot type
+  - Clean, professional appearance with removed top/right spines
+  - Well-chosen custom color palette with distinct colors for each programming language
+  - Count labels on bars enhance readability
+  - Properly handles seaborn 0.14+ API by using hue parameter with palette
+  weaknesses:
+  - Could use seaborn built-in themes (sns.set_theme or sns.set_style) for more distinctive
+    seaborn styling
+  - The order parameter manually specifies the order rather than computing it from
+    the data