update(bullet-basic): seaborn — comprehensive quality review

Comprehensive quality review improving library feature usage, visual design, and data quality.

Author: MarkusNeusinger

plots/bullet-basic/implementations/seaborn.py

@@ -1,21 +1,18 @@
-""" pyplots.ai
+"""pyplots.ai
 bullet-basic: Basic Bullet Chart
-Library: seaborn 0.13.2 | Python 3.13.11
-Quality: 92/100 | Created: 2025-12-23
+Library: seaborn 0.13.2 | Python 3.14.3
+Quality: /100 | Updated: 2026-02-22
 """
 
 import matplotlib.patches as mpatches
 import matplotlib.pyplot as plt
-import numpy as np
 import pandas as pd
 import seaborn as sns
 
 
 # Data - Multiple KPIs with actual values, targets, and qualitative ranges
-np.random.seed(42)
-
 metrics = ["Revenue", "Customer\nSatisfaction", "Efficiency", "Quality\nScore"]
-actuals = [78, 85, 62, 91]
+actuals = [78, 85, 35, 91]
 targets = [90, 80, 75, 85]
 # Ranges define thresholds for qualitative bands (poor/satisfactory/good)
 ranges_list = [
@@ -25,41 +22,51 @@
     [70, 85, 100],  # Quality Score
 ]
 
-# Create figure
-fig, ax = plt.subplots(figsize=(16, 9))
-sns.set_style("whitegrid")
-
-# Qualitative range colors (grayscale as per specification - dark to light for poor to good)
-range_colors = ["#e0e0e0", "#bdbdbd", "#8c8c8c"]
-
-# Bar dimensions
-range_height = 0.7
-actual_height = 0.35
-n_metrics = len(metrics)
-y_positions = np.arange(n_metrics)
-
-# Draw qualitative ranges as background bands for each metric
-for y_pos, ranges in zip(y_positions, ranges_list, strict=True):
+# Build a long-form DataFrame for qualitative range bands
+range_labels = ["Good", "Satisfactory", "Poor"]
+range_records = []
+for metric, ranges in zip(metrics, ranges_list, strict=True):
     prev = 0
-    for end, color in zip(ranges, range_colors, strict=True):
-        width = end - prev
-        ax.barh(y_pos, width, left=prev, height=range_height, color=color, edgecolor="none", zorder=1)
+    for end, label in zip(ranges, range_labels[::-1], strict=True):
+        range_records.append({"Metric": metric, "Range": label, "Start": prev, "Width": end - prev})
         prev = end
+df_ranges = pd.DataFrame(range_records)
+
+# Seaborn grayscale palette for qualitative bands (dark=poor, medium=satisfactory, light=good)
+band_palette = dict(zip(range_labels, sns.light_palette("#555555", n_colors=4, reverse=True)[1:], strict=True))
+
+# Create figure with seaborn theme
+sns.set_theme(style="whitegrid", rc={"axes.spines.top": False, "axes.spines.right": False, "axes.spines.left": False})
+fig, ax = plt.subplots(figsize=(16, 9))
 
-# Create DataFrame for seaborn barplot
-df = pd.DataFrame({"Metric": metrics, "Actual": actuals})
+# Draw qualitative range bands using seaborn barplot layering
+range_height = 0.7
+for label in range_labels:
+    subset = df_ranges[df_ranges["Range"] == label]
+    sns.barplot(
+        data=subset,
+        x="Width",
+        y="Metric",
+        color=band_palette[label],
+        left=subset["Start"].values,
+        width=range_height,
+        edgecolor="none",
+        ax=ax,
+        zorder=1,
+    )
 
-# Draw actual value bars using seaborn
+# Actual value bars using seaborn barplot
+df_actual = pd.DataFrame({"Metric": metrics, "Actual": actuals})
 sns.barplot(
-    data=df,
+    data=df_actual,
     x="Actual",
     y="Metric",
-    color="#306998",  # Python Blue
-    width=actual_height,
-    ax=ax,
-    zorder=3,
+    color="#306998",
+    width=0.35,
     edgecolor="#1e4d6b",
     linewidth=1.5,
+    ax=ax,
+    zorder=3,
 )
 
 # Draw target markers as vertical lines
@@ -92,18 +99,13 @@
 
 # Create legend
 legend_elements = [
-    mpatches.Patch(facecolor=range_colors[0], label="Poor"),
-    mpatches.Patch(facecolor=range_colors[1], label="Satisfactory"),
-    mpatches.Patch(facecolor=range_colors[2], label="Good"),
+    mpatches.Patch(facecolor=band_palette["Poor"], label="Poor"),
+    mpatches.Patch(facecolor=band_palette["Satisfactory"], label="Satisfactory"),
+    mpatches.Patch(facecolor=band_palette["Good"], label="Good"),
     mpatches.Patch(facecolor="#306998", edgecolor="#1e4d6b", linewidth=1.5, label="Actual"),
     plt.Line2D([0], [0], color="#1a1a1a", linewidth=5, label="Target"),
 ]
-ax.legend(handles=legend_elements, loc="upper right", fontsize=14, framealpha=0.95)
-
-# Remove spines for cleaner look
-ax.spines["top"].set_visible(False)
-ax.spines["right"].set_visible(False)
-ax.spines["left"].set_visible(False)
+ax.legend(handles=legend_elements, loc="upper right", fontsize=13, framealpha=0.95)
 
 plt.tight_layout()
 plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/bullet-basic/metadata/seaborn.yaml

@@ -1,16 +1,16 @@
 library: seaborn
 specification_id: bullet-basic
 created: '2025-12-23T09:20:31Z'
-updated: '2025-12-23T09:24:02Z'
-generated_by: claude-opus-4-5-20251101
+updated: '2026-02-22T12:00:00+00:00'
+generated_by: claude-opus-4-6
 workflow_run: 20456603775
 issue: 0
-python_version: 3.13.11
+python_version: 3.14.3
 library_version: 0.13.2
 preview_url: https://storage.googleapis.com/pyplots-images/plots/bullet-basic/seaborn/plot.png
 preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bullet-basic/seaborn/plot_thumb.png
 preview_html: null
-quality_score: 92
+quality_score: null
 impl_tags:
   dependencies: []
   techniques: