feat(plotnine): implement box-horizontal (#2565)

## Implementation: `box-horizontal` - plotnine

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20593349974)*

---------

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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/plotnine.py

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+""" pyplots.ai
+box-horizontal: Horizontal Box Plot
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import aes, coord_flip, element_text, geom_boxplot, ggplot, labs, scale_fill_manual, theme, theme_minimal
+
+
+# Data - Response times (ms) by service type
+np.random.seed(42)
+
+services = [
+    "Authentication Service",
+    "Database Query Handler",
+    "File Storage API",
+    "Email Notification",
+    "Payment Gateway",
+]
+
+data = []
+for service in services:
+    if service == "Authentication Service":
+        values = np.random.normal(120, 25, 80)
+    elif service == "Database Query Handler":
+        values = np.random.normal(85, 40, 80)
+        values = np.append(values, [220, 250, 280])  # Add outliers
+    elif service == "File Storage API":
+        values = np.random.normal(200, 50, 80)
+    elif service == "Email Notification":
+        values = np.random.normal(150, 30, 80)
+    else:  # Payment Gateway
+        values = np.random.normal(180, 60, 80)
+        values = np.append(values, [350, 380])  # Add outliers
+
+    for v in values:
+        data.append({"Service": service, "Response Time": max(10, v)})
+
+df = pd.DataFrame(data)
+
+# Sort by median response time for easier comparison
+medians = df.groupby("Service")["Response Time"].median().sort_values()
+df["Service"] = pd.Categorical(df["Service"], categories=medians.index, ordered=True)
+
+# Colors - Python palette
+colors = ["#306998", "#FFD43B", "#4B8BBE", "#646464", "#FFE873"]
+
+# Create horizontal box plot using coord_flip()
+plot = (
+    ggplot(df, aes(x="Service", y="Response Time", fill="Service"))
+    + geom_boxplot(alpha=0.8, size=0.8, outlier_size=3, outlier_alpha=0.7)
+    + coord_flip()
+    + scale_fill_manual(values=colors)
+    + labs(title="box-horizontal · plotnine · pyplots.ai", x="", y="Response Time (ms)")
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        plot_title=element_text(size=24, face="bold"),
+        axis_title_x=element_text(size=20),
+        axis_title_y=element_text(size=20),
+        axis_text_x=element_text(size=16),
+        axis_text_y=element_text(size=16),
+        legend_position="none",
+        panel_grid_major_y=element_text(color="#cccccc"),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

plots/box-horizontal/metadata/plotnine.yaml

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+library: plotnine
+specification_id: box-horizontal
+created: '2025-12-30T09:31:06Z'
+updated: '2025-12-30T09:39:06Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20593349974
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/box-horizontal/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/box-horizontal/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent data design with realistic service response time scenario showing varied
+    distributions and outliers
+  - Services sorted by median response time as recommended in the spec for easier
+    comparison
+  - Clean KISS code structure following plotnine grammar of graphics idiom
+  - Good visual hierarchy with appropriately sized text elements
+  - Python-themed color palette adds personality while remaining readable
+  weaknesses:
+  - 'Minor code issue: panel_grid_major_y uses element_text() instead of element_line()
+    which is incorrect'
+  - Y-axis label is empty string instead of descriptive label like Service Type
+  - Could use more distinctive plotnine features like faceting or statistical transformations