feat(seaborn): implement bar-error

plots/bar-error/implementations/seaborn.py

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+""" pyplots.ai
+bar-error: Bar Chart with Error Bars
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-27
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Data - A/B test results with confidence intervals
+np.random.seed(42)
+
+categories = ["Control", "Variant A", "Variant B", "Variant C", "Variant D", "Variant E"]
+# Conversion rates (percentage)
+values = [4.2, 5.1, 4.8, 6.3, 5.5, 4.0]
+# 95% CI error margins (asymmetric for realistic percentage data)
+errors_lower = [0.3, 0.4, 0.35, 0.5, 0.45, 0.25]
+errors_upper = [0.35, 0.45, 0.4, 0.55, 0.5, 0.3]
+
+df = pd.DataFrame(
+    {"Category": categories, "Conversion Rate (%)": values, "Error Lower": errors_lower, "Error Upper": errors_upper}
+)
+
+# Create figure
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Create bar plot using seaborn
+colors = ["#306998", "#FFD43B", "#306998", "#FFD43B", "#306998", "#FFD43B"]
+sns.barplot(
+    data=df,
+    x="Category",
+    y="Conversion Rate (%)",
+    hue="Category",
+    palette=colors,
+    legend=False,
+    ax=ax,
+    edgecolor="black",
+    linewidth=1.5,
+    width=0.7,
+)
+
+# Add error bars with caps
+x_positions = np.arange(len(categories))
+ax.errorbar(
+    x_positions,
+    values,
+    yerr=[errors_lower, errors_upper],
+    fmt="none",
+    ecolor="black",
+    elinewidth=2.5,
+    capsize=12,
+    capthick=2.5,
+)
+
+# Styling for large canvas (4800x2700 px at dpi=300)
+ax.set_xlabel("Test Group", fontsize=20)
+ax.set_ylabel("Conversion Rate (%)", fontsize=20)
+ax.set_title("bar-error · seaborn · pyplots.ai", fontsize=24, fontweight="bold")
+ax.tick_params(axis="both", labelsize=16)
+
+# Add annotation explaining error bars
+ax.annotate(
+    "Error bars: 95% CI",
+    xy=(0.98, 0.95),
+    xycoords="axes fraction",
+    fontsize=14,
+    ha="right",
+    va="top",
+    bbox={"boxstyle": "round,pad=0.3", "facecolor": "white", "edgecolor": "gray", "alpha": 0.8},
+)
+
+# Subtle grid
+ax.yaxis.grid(True, alpha=0.3, linestyle="--")
+ax.set_axisbelow(True)
+
+# Set y-axis to start from 0 for proper bar comparison
+ax.set_ylim(0, max(values) * 1.25)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/bar-error/metadata/seaborn.yaml

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+library: seaborn
+specification_id: bar-error
+created: '2025-12-27T19:21:22Z'
+updated: '2025-12-27T19:29:06Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20543280392
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/bar-error/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bar-error/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent text sizing following the 24/20/16pt guideline for large canvas
+  - Clean alternating color scheme that is colorblind-accessible
+  - Proper use of asymmetric error bars for percentage data as spec suggests
+  - Good annotation explaining what error bars represent (95% CI)
+  - Modern seaborn API usage with hue parameter to avoid deprecation warnings
+  - Y-axis correctly starts at 0 for proper bar comparison
+  - Realistic A/B testing scenario with plausible conversion rate values
+  weaknesses:
+  - Error bars added via matplotlib errorbar() rather than exploring seaborn native
+    statistical capabilities
+  - The annotation box could be positioned closer to the data area rather than isolated
+    in the corner
+  - Feature coverage could show more variation (e.g., one notably different group
+    or wider CI range)