feat(altair): implement box-horizontal (#2573)

## Implementation: `box-horizontal` - altair

Implements the **altair** version of `box-horizontal`.

**File:** `plots/box-horizontal/implementations/altair.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20593349773)*

---------

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

Author: github-actions[bot]

plots/box-horizontal/implementations/altair.py

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+""" pyplots.ai
+box-horizontal: Horizontal Box Plot
+Library: altair 6.0.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - Response times by service type
+np.random.seed(42)
+
+data = []
+# Create distributions with different characteristics
+distributions = {
+    "Database Query": (120, 40, 5),  # mean, std, n_outliers
+    "API Gateway": (85, 25, 3),
+    "Authentication": (45, 15, 2),
+    "File Storage": (200, 60, 4),
+    "Cache Lookup": (15, 8, 2),
+    "Email Service": (150, 50, 3),
+}
+
+for service, (mean, std, n_outliers) in distributions.items():
+    # Main distribution
+    values = np.random.normal(mean, std, 50)
+    values = np.clip(values, 5, None)  # No negative response times
+    # Add some outliers
+    outliers = np.random.uniform(mean + 3 * std, mean + 5 * std, n_outliers)
+    all_values = np.concatenate([values, outliers])
+    for v in all_values:
+        data.append({"Service": service, "Response Time (ms)": v})
+
+df = pd.DataFrame(data)
+
+# Sort by median for easier comparison
+medians = df.groupby("Service")["Response Time (ms)"].median().sort_values()
+df["Service"] = pd.Categorical(df["Service"], categories=medians.index, ordered=True)
+
+# Create horizontal box plot
+chart = (
+    alt.Chart(df)
+    .mark_boxplot(
+        box=alt.MarkConfig(color="#306998"),
+        median=alt.MarkConfig(color="#FFD43B", size=3),
+        outliers=alt.MarkConfig(color="#306998", size=80),
+        ticks=alt.MarkConfig(color="#306998"),
+        rule=alt.MarkConfig(color="#306998"),
+    )
+    .encode(
+        x=alt.X("Response Time (ms):Q", title="Response Time (ms)", scale=alt.Scale(zero=False)),
+        y=alt.Y("Service:N", title="Service Type", sort=list(medians.index)),
+        tooltip=["Service:N", "Response Time (ms):Q"],
+    )
+    .properties(
+        width=1400, height=800, title=alt.Title("box-horizontal · altair · pyplots.ai", fontSize=28, anchor="middle")
+    )
+    .configure_axis(labelFontSize=18, titleFontSize=22, labelLimit=300)
+    .configure_view(strokeWidth=0)
+)
+
+# Save as PNG and HTML
+chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.html")

plots/box-horizontal/metadata/altair.yaml

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+library: altair
+specification_id: box-horizontal
+created: '2025-12-30T09:31:33Z'
+updated: '2025-12-30T09:40:56Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20593349773
+issue: 0
+python_version: 3.13.11
+library_version: 6.0.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/box-horizontal/altair/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/box-horizontal/altair/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/box-horizontal/altair/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent data design with 6 service types showing varied distributions, outliers,
+    and realistic response time ranges
+  - Categories sorted by median value for easier comparison as recommended in spec
+  - Clean implementation with proper Altair encoding types (Q for quantitative, N
+    for nominal)
+  - Good customization of box plot components (box color, median color, outlier styling)
+  - Proper title format following pyplots.ai convention
+  weaknesses:
+  - Missing grid lines which could help readers trace values across the wide x-axis
+  - Could leverage Altair interactive() method for tooltip exploration in the HTML
+    output
+  - Slight excess whitespace on right side due to outlier-extended scale