feat(plotly): implement cat-box-strip (#2756)

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

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20601056000)*

---------

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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/plotly.py

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+""" pyplots.ai
+cat-box-strip: Box Plot with Strip Overlay
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+
+
+# Data - Performance scores across different training methods
+np.random.seed(42)
+
+categories = ["Method A", "Method B", "Method C", "Method D"]
+n_per_group = [35, 40, 30, 45]
+
+data = []
+# Method A: Normal distribution, moderate spread
+data.extend([{"Category": "Method A", "Score": v} for v in np.random.normal(72, 8, n_per_group[0])])
+# Method B: Higher scores, tighter spread
+data.extend([{"Category": "Method B", "Score": v} for v in np.random.normal(85, 5, n_per_group[1])])
+# Method C: Lower scores with some outliers
+scores_c = np.concatenate([np.random.normal(58, 10, n_per_group[2] - 3), [25, 28, 95]])
+data.extend([{"Category": "Method C", "Score": v} for v in scores_c])
+# Method D: Bimodal distribution
+scores_d = np.concatenate(
+    [np.random.normal(65, 6, n_per_group[3] // 2), np.random.normal(80, 6, n_per_group[3] - n_per_group[3] // 2)]
+)
+data.extend([{"Category": "Method D", "Score": v} for v in scores_d])
+
+df = pd.DataFrame(data)
+
+# Colors
+python_blue = "#306998"
+python_yellow = "#FFD43B"
+
+# Create figure
+fig = go.Figure()
+
+# Add box plots for each category
+for cat in categories:
+    cat_data = df[df["Category"] == cat]["Score"]
+    fig.add_trace(
+        go.Box(
+            y=cat_data,
+            x=[cat] * len(cat_data),
+            name=cat,
+            marker_color=python_blue,
+            fillcolor="rgba(48, 105, 152, 0.4)",
+            line=dict(color=python_blue, width=2),
+            boxmean=False,
+            boxpoints=False,
+            showlegend=False,
+            width=0.5,
+        )
+    )
+
+# Add strip (scatter) points for each category with jitter
+np.random.seed(123)  # Separate seed for jitter
+for cat in categories:
+    cat_data = df[df["Category"] == cat]["Score"]
+    jitter_vals = np.random.uniform(-0.15, 0.15, len(cat_data))
+
+    fig.add_trace(
+        go.Scatter(
+            x=[cat] * len(cat_data),
+            y=cat_data,
+            mode="markers",
+            name=cat,
+            marker=dict(color=python_yellow, size=10, opacity=0.7, line=dict(color=python_blue, width=1)),
+            showlegend=False,
+            hovertemplate=f"{cat}<br>Score: %{{y:.1f}}<extra></extra>",
+            customdata=jitter_vals,
+        )
+    )
+
+# Apply jitter by offsetting x positions
+for i, cat in enumerate(categories):
+    trace_idx = len(categories) + i
+    cat_data = df[df["Category"] == cat]["Score"]
+    jitter_vals = fig.data[trace_idx].customdata
+    # Convert category to position and add jitter
+    fig.data[trace_idx].x = [i + j for j in jitter_vals]
+
+# Update x-axis to use category positions
+fig.update_layout(
+    title=dict(text="cat-box-strip · plotly · pyplots.ai", font=dict(size=28), x=0.5, xanchor="center"),
+    xaxis=dict(
+        title=dict(text="Training Method", font=dict(size=22)),
+        tickfont=dict(size=18),
+        tickmode="array",
+        tickvals=list(range(len(categories))),
+        ticktext=categories,
+        range=[-0.5, len(categories) - 0.5],
+    ),
+    yaxis=dict(
+        title=dict(text="Performance Score", font=dict(size=22)),
+        tickfont=dict(size=18),
+        gridcolor="rgba(0, 0, 0, 0.1)",
+        gridwidth=1,
+    ),
+    template="plotly_white",
+    plot_bgcolor="white",
+    showlegend=False,
+    margin=dict(l=80, r=50, t=100, b=80),
+)
+
+# Update box plots to use numeric positions
+for i, cat in enumerate(categories):
+    fig.data[i].x = [i] * len(df[df["Category"] == cat])
+
+# Save as PNG and HTML
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+fig.write_html("plot.html", include_plotlyjs=True, full_html=True)

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

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+library: plotly
+specification_id: cat-box-strip
+created: '2025-12-30T16:30:35Z'
+updated: '2025-12-30T16:38:41Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20601056000
+issue: 0
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/cat-box-strip/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/cat-box-strip/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/cat-box-strip/plotly/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent visual design with Python blue/yellow color scheme providing strong
+    contrast
+  - Box plots and strip points are well-separated visually while maintaining their
+    relationship
+  - Good jitter implementation prevents point overlap while keeping points associated
+    with their categories
+  - Data demonstrates multiple distribution characteristics (normal, tight spread,
+    outliers, bimodal)
+  - Correct title format and clear axis labels
+  - Clean, linear code structure following KISS principles
+  weaknesses:
+  - Axis labels lack units (e.g., "Performance Score (%)" would be clearer)
+  - Could use Plotly native boxpoints parameter instead of manually adding scatter
+    traces for a more elegant solution
+  - Grid alpha is perhaps too subtle, making it hard to read precise values