feat(bokeh): implement scatter-categorical (#2691)

## Implementation: `scatter-categorical` - bokeh

Implements the **bokeh** version of `scatter-categorical`.

**File:** `plots/scatter-categorical/implementations/bokeh.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20594556498)*

---------

Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/scatter-categorical/implementations/bokeh.py

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+""" pyplots.ai
+scatter-categorical: Categorical Scatter Plot
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+from bokeh.io import export_png, output_file, save
+from bokeh.models import ColumnDataSource, Legend
+from bokeh.plotting import figure
+
+
+# Data
+np.random.seed(42)
+
+# Generate three distinct groups with different patterns
+n_per_group = 50
+
+# Group A: Lower left cluster
+x_a = np.random.normal(25, 5, n_per_group)
+y_a = np.random.normal(30, 6, n_per_group)
+cat_a = ["Product A"] * n_per_group
+
+# Group B: Upper middle cluster
+x_b = np.random.normal(50, 7, n_per_group)
+y_b = np.random.normal(70, 8, n_per_group)
+cat_b = ["Product B"] * n_per_group
+
+# Group C: Right side, moderate y
+x_c = np.random.normal(75, 6, n_per_group)
+y_c = np.random.normal(50, 7, n_per_group)
+cat_c = ["Product C"] * n_per_group
+
+# Combine data
+x = np.concatenate([x_a, x_b, x_c])
+y = np.concatenate([y_a, y_b, y_c])
+categories = cat_a + cat_b + cat_c
+
+# Define colors for each category (Python Blue, Python Yellow, colorblind-safe red)
+color_map = {"Product A": "#306998", "Product B": "#FFD43B", "Product C": "#E74C3C"}
+
+# Create figure with interactive tools
+p = figure(
+    width=4800,
+    height=2700,
+    title="scatter-categorical · bokeh · pyplots.ai",
+    x_axis_label="Marketing Spend ($K)",
+    y_axis_label="Customer Engagement Score",
+    tools="pan,wheel_zoom,box_zoom,reset,hover,save",
+    tooltips=[("Category", "@cat"), ("X", "@x{0.1}"), ("Y", "@y{0.1}")],
+)
+
+# Plot each category separately for legend
+legend_items = []
+
+for cat, color in color_map.items():
+    mask = [categories[i] == cat for i in range(len(categories))]
+    cat_x = [x[i] for i in range(len(x)) if mask[i]]
+    cat_y = [y[i] for i in range(len(y)) if mask[i]]
+    cat_source = ColumnDataSource(data={"x": cat_x, "y": cat_y, "cat": [cat] * len(cat_x)})
+    r = p.scatter(x="x", y="y", source=cat_source, size=25, color=color, alpha=0.7, line_color="white", line_width=1)
+    legend_items.append((cat, [r]))
+
+# Add legend
+legend = Legend(items=legend_items, location="top_right")
+legend.label_text_font_size = "28pt"
+legend.glyph_height = 40
+legend.glyph_width = 40
+legend.spacing = 12
+legend.padding = 15
+legend.background_fill_alpha = 0.8
+p.add_layout(legend)
+
+# Title styling
+p.title.text_font_size = "48pt"
+p.title.text_font_style = "bold"
+
+# Axis label styling
+p.xaxis.axis_label_text_font_size = "36pt"
+p.yaxis.axis_label_text_font_size = "36pt"
+
+# Tick label styling
+p.xaxis.major_label_text_font_size = "28pt"
+p.yaxis.major_label_text_font_size = "28pt"
+
+# Grid styling
+p.grid.grid_line_alpha = 0.3
+p.grid.grid_line_dash = "dashed"
+
+# Background
+p.background_fill_color = "#fafafa"
+
+# Axis styling
+p.axis.axis_line_width = 2
+p.axis.major_tick_line_width = 2
+
+# Save PNG
+export_png(p, filename="plot.png")
+
+# Save interactive HTML
+output_file("plot.html", title="scatter-categorical · bokeh · pyplots.ai")
+save(p)

plots/scatter-categorical/metadata/bokeh.yaml

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+library: bokeh
+specification_id: scatter-categorical
+created: '2025-12-30T11:40:30Z'
+updated: '2025-12-30T11:42:42Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20594556498
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/bokeh/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent text sizing for the large canvas (48pt title, 36pt axis labels, 28pt
+    tick labels)
+  - Clean three-cluster visualization with distinct, colorblind-safe colors
+  - Good use of Bokeh interactive tools (hover tooltips with formatted values)
+  - Proper use of ColumnDataSource for data management
+  - Realistic business context (marketing spend vs engagement)
+  - Saves both PNG and interactive HTML output
+  weaknesses:
+  - Legend placement could be better integrated with the plot (currently appears slightly
+    isolated in top-right)
+  - Could demonstrate alpha transparency more effectively by having some overlapping
+    points between categories
+  - Library features usage is good but not exceptional (could use linked selections
+    or custom hover styling)