feat(seaborn): implement cat-box-strip (#2735)

## Implementation: `cat-box-strip` - seaborn

Implements the **seaborn** version of `cat-box-strip`.

**File:** `plots/cat-box-strip/implementations/seaborn.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20601055897)*

---------

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

Author: github-actions[bot]

plots/cat-box-strip/implementations/seaborn.py

@@ -0,0 +1,80 @@
+""" pyplots.ai
+cat-box-strip: Box Plot with Strip Overlay
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Data - product quality scores across manufacturing batches
+np.random.seed(42)
+
+# Create groups with different distributions to show boxplot features
+batch_a = np.random.normal(75, 8, 40)  # Centered, moderate spread
+batch_b = np.random.normal(82, 5, 35)  # Higher center, tight spread
+batch_c = np.concatenate(
+    [
+        np.random.normal(68, 6, 30),  # Main distribution
+        [45, 48, 95, 97],  # Outliers
+    ]
+)
+batch_d = np.random.normal(70, 12, 45)  # Wide spread
+
+# Combine into DataFrame
+df = pd.DataFrame(
+    {
+        "Batch": ["Batch A"] * len(batch_a)
+        + ["Batch B"] * len(batch_b)
+        + ["Batch C"] * len(batch_c)
+        + ["Batch D"] * len(batch_d),
+        "Quality Score": np.concatenate([batch_a, batch_b, batch_c, batch_d]),
+    }
+)
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Box plot with boxes in Python Blue
+sns.boxplot(
+    data=df,
+    x="Batch",
+    y="Quality Score",
+    hue="Batch",
+    palette=["#306998"] * 4,
+    width=0.5,
+    linewidth=2,
+    fliersize=0,  # Hide box plot outliers (strip will show them)
+    legend=False,
+    ax=ax,
+)
+
+# Strip plot overlay with Python Yellow
+sns.stripplot(
+    data=df,
+    x="Batch",
+    y="Quality Score",
+    color="#FFD43B",
+    size=10,
+    alpha=0.7,
+    jitter=0.2,
+    edgecolor="#333333",
+    linewidth=0.5,
+    ax=ax,
+)
+
+# Styling
+ax.set_title("cat-box-strip · seaborn · pyplots.ai", fontsize=24)
+ax.set_xlabel("Manufacturing Batch", fontsize=20)
+ax.set_ylabel("Quality Score (points)", fontsize=20)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, axis="y", alpha=0.3, linestyle="--")
+
+# Set y-axis limits with some padding
+ax.set_ylim(35, 105)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/cat-box-strip/metadata/seaborn.yaml

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+library: seaborn
+specification_id: cat-box-strip
+created: '2025-12-30T16:28:36Z'
+updated: '2025-12-30T16:54:19Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20601055897
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/cat-box-strip/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/cat-box-strip/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent data design showing varied distributions (different medians, spreads,
+    and outliers) that demonstrate all boxplot features
+  - Clean visual hierarchy with Python Blue boxes and contrasting Yellow strip points
+  - Proper use of modern seaborn API (hue parameter with palette, legend=False)
+  - Smart handling of outliers by hiding boxplot fliers (fliersize=0) since strip
+    plot shows all points
+  - Good jitter (0.2) and alpha (0.7) settings for strip points to show density without
+    excessive overlap
+  - Realistic manufacturing quality scenario with appropriate value ranges
+  weaknesses:
+  - Grid styling could include both x and y axes for complete reference (minor)