feat(altair): implement scatter-categorical (#2626)

## Implementation: `scatter-categorical` - altair

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20594556298)*

---------

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

Author: github-actions[bot]

plots/scatter-categorical/implementations/altair.py

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+""" pyplots.ai
+scatter-categorical: Categorical Scatter Plot
+Library: altair 6.0.0 | Python 3.13.11
+Quality: 100/100 | Created: 2025-12-30
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - Iris-like measurements by species
+np.random.seed(42)
+
+# Generate measurements for 3 plant species
+n_per_species = 50
+species_names = ["Setosa", "Versicolor", "Virginica"]
+
+data = []
+for i, species in enumerate(species_names):
+    # Different distributions for each species
+    base_petal_length = [1.4, 4.2, 5.5][i]
+    base_petal_width = [0.2, 1.3, 2.0][i]
+    spread = [0.2, 0.5, 0.6][i]
+
+    petal_length = np.random.normal(base_petal_length, spread, n_per_species)
+    petal_width = np.random.normal(base_petal_width, spread * 0.5, n_per_species)
+
+    for pl, pw in zip(petal_length, petal_width, strict=True):
+        data.append({"Petal Length (cm)": pl, "Petal Width (cm)": pw, "Species": species})
+
+df = pd.DataFrame(data)
+
+# Create chart
+chart = (
+    alt.Chart(df)
+    .mark_point(size=200, opacity=0.7, filled=True)
+    .encode(
+        x=alt.X("Petal Length (cm):Q", title="Petal Length (cm)", scale=alt.Scale(zero=False)),
+        y=alt.Y("Petal Width (cm):Q", title="Petal Width (cm)", scale=alt.Scale(zero=False)),
+        color=alt.Color(
+            "Species:N",
+            scale=alt.Scale(domain=species_names, range=["#306998", "#FFD43B", "#6B8E23"]),
+            legend=alt.Legend(title="Species", titleFontSize=20, labelFontSize=18, symbolSize=200),
+        ),
+        shape=alt.Shape("Species:N", legend=None),
+        tooltip=["Species:N", "Petal Length (cm):Q", "Petal Width (cm):Q"],
+    )
+    .properties(width=1600, height=900, title=alt.Title("scatter-categorical · altair · pyplots.ai", fontSize=28))
+    .configure_axis(labelFontSize=18, titleFontSize=22, gridOpacity=0.3)
+    .configure_view(strokeWidth=0)
+)
+
+# Save as PNG (4800 x 2700 with scale_factor=3)
+chart.save("plot.png", scale_factor=3.0)
+
+# Save as HTML for interactive version
+chart.save("plot.html")

plots/scatter-categorical/metadata/altair.yaml

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+library: altair
+specification_id: scatter-categorical
+created: '2025-12-30T10:38:39Z'
+updated: '2025-12-30T10:49:22Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20594556298
+issue: 0
+python_version: 3.13.11
+library_version: 6.0.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/altair/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/altair/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/altair/plot.html
+quality_score: 100
+review:
+  strengths:
+  - Perfect use of redundant encoding (color + shape) for maximum accessibility
+  - Well-chosen Iris-inspired data with three clearly separable clusters
+  - Excellent font sizing across all text elements (title, axes, legend)
+  - Good use of Altair declarative features including tooltips for interactivity
+  - Clean code structure following KISS principles
+  weaknesses: []