feat(plotnine): implement errorbar-asymmetric (#2799)

## Implementation: `errorbar-asymmetric` - plotnine

Implements the **plotnine** version of `errorbar-asymmetric`.

**File:** `plots/errorbar-asymmetric/implementations/plotnine.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20602450102)*

---------

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

Author: github-actions[bot]

plots/errorbar-asymmetric/implementations/plotnine.py

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+""" pyplots.ai
+errorbar-asymmetric: Asymmetric Error Bars Plot
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import aes, element_line, element_text, geom_errorbar, geom_point, ggplot, labs, theme, theme_minimal
+
+
+# Data - Quarterly revenue projections with asymmetric confidence intervals
+np.random.seed(42)
+quarters = ["Q1 2024", "Q2 2024", "Q3 2024", "Q4 2024", "Q1 2025", "Q2 2025"]
+central_values = [120, 135, 128, 155, 142, 160]
+# Asymmetric intervals: downside risk tends to be larger (conservative projections)
+error_lower = [15, 18, 12, 22, 16, 20]
+error_upper = [10, 12, 8, 15, 11, 14]
+
+df = pd.DataFrame(
+    {
+        "quarter": pd.Categorical(quarters, categories=quarters, ordered=True),
+        "revenue": central_values,
+        "ymin": [c - lo for c, lo in zip(central_values, error_lower, strict=True)],
+        "ymax": [c + up for c, up in zip(central_values, error_upper, strict=True)],
+    }
+)
+
+# Create plot with asymmetric error bars
+plot = (
+    ggplot(df, aes(x="quarter", y="revenue"))
+    + geom_errorbar(aes(ymin="ymin", ymax="ymax"), width=0.3, size=1.5, color="#306998")
+    + geom_point(size=6, color="#306998")
+    + labs(
+        x="Quarter",
+        y="Revenue (Million USD)",
+        title="errorbar-asymmetric · plotnine · pyplots.ai",
+        caption="Error bars show 10th-90th percentile forecast range",
+    )
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        text=element_text(size=14),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        axis_text_x=element_text(angle=0),
+        plot_title=element_text(size=24, ha="center"),
+        plot_caption=element_text(size=14, ha="right"),
+        panel_grid_major=element_line(alpha=0.3),
+        panel_grid_minor=element_line(alpha=0.15),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300)

plots/errorbar-asymmetric/metadata/plotnine.yaml

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+library: plotnine
+specification_id: errorbar-asymmetric
+created: '2025-12-30T17:48:00Z'
+updated: '2025-12-30T17:54:14Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602450102
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/errorbar-asymmetric/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/errorbar-asymmetric/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent visual clarity with well-sized text and data elements
+  - Clear demonstration of asymmetric error bars with larger downside risk
+  - Good use of caption to explain what the error bars represent (as spec recommends)
+  - Clean, readable code following KISS principles
+  - Realistic financial forecasting scenario
+  weaknesses:
+  - Error bar asymmetry pattern is uniform (all bars have larger downside) - mixing
+    in some with larger upside would better demonstrate the concept
+  - 'Grid/legend scoring: caption is good but a proper legend entry could enhance
+    clarity'