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

Comprehensive review improving code quality, data choice, visual design, spec compliance, and library feature usage.

Author: MarkusNeusinger

plots/dendrogram-basic/implementations/seaborn.py

@@ -1,81 +1,85 @@
-""" pyplots.ai
+"""pyplots.ai
 dendrogram-basic: Basic Dendrogram
-Library: seaborn 0.13.2 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: seaborn 0.13.2 | Python 3.14.3
+Quality: /100 | Updated: 2026-04-05
 """
 
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
-from scipy.cluster.hierarchy import dendrogram, linkage
-from sklearn.datasets import load_iris
+from scipy.cluster.hierarchy import dendrogram, linkage, set_link_color_palette
 
 
-# Set seaborn style for better aesthetics
-sns.set_theme(style="whitegrid")
+# Style
+sns.set_theme(style="white", rc={"axes.linewidth": 0.8})
 sns.set_context("talk", font_scale=1.2)
+palette = sns.color_palette("colorblind", n_colors=3)
 
-# Load iris dataset - use subset for readability (spec recommends 10-50 items)
+# Data - use seaborn's iris dataset (30 samples for readable dendrogram)
 np.random.seed(42)
-iris = load_iris()
-species_names = ["Setosa", "Versicolor", "Virginica"]
+iris = sns.load_dataset("iris")
+species_names = ["setosa", "versicolor", "virginica"]
+display_names = ["Setosa", "Versicolor", "Virginica"]
+species_color = dict(zip(display_names, palette, strict=True))
 
-# Select 10 samples from each species (30 total) for clearer visualization
-indices = np.concatenate([np.random.choice(np.where(iris.target == i)[0], 10, replace=False) for i in range(3)])
+samples = (
+    iris.groupby("species").apply(lambda g: g.sample(10, random_state=42), include_groups=False).reset_index(level=0)
+)
 
-X = iris.data[indices]
+features = samples[["sepal_length", "sepal_width", "petal_length", "petal_width"]].values
 
-# Create clear labels: Species-Number format using vectorized approach
-species_ids = iris.target[indices]
-labels = [f"{species_names[sid]}-{np.sum(species_ids[: i + 1] == sid)}" for i, sid in enumerate(species_ids)]
+# Build labels: Species-Number
+counters = dict.fromkeys(species_names, 0)
+labels = []
+for species in samples["species"]:
+    counters[species] += 1
+    labels.append(f"{species.title()}-{counters[species]}")
 
-# Compute linkage matrix using Ward's method
-linkage_matrix = linkage(X, method="ward")
+# Compute linkage
+linkage_matrix = linkage(features, method="ward")
 
-# Create figure
-fig, ax = plt.subplots(figsize=(16, 9))
+# Map dendrogram branch colors to species palette
+hex_colors = ["#{:02x}{:02x}{:02x}".format(int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)) for c in palette]
+set_link_color_palette(hex_colors)
 
-# Define custom colors using seaborn colorblind palette for species
-palette = sns.color_palette("colorblind", n_colors=3)
-species_color_map = dict(zip(species_names, palette, strict=True))
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
 
-# Create dendrogram
 dendrogram(
     linkage_matrix,
     labels=labels,
     leaf_rotation=45,
     leaf_font_size=14,
     ax=ax,
-    above_threshold_color="#888888",
+    above_threshold_color="#aaaaaa",
     color_threshold=0.7 * max(linkage_matrix[:, 2]),
 )
 
-# Color the x-axis labels by species using exact palette colors
+set_link_color_palette(None)
+
+# Color x-axis labels by species
 for lbl in ax.get_xticklabels():
-    text = lbl.get_text()
-    species = text.rsplit("-", 1)[0]
-    if species in species_color_map:
-        lbl.set_color(species_color_map[species])
+    species = lbl.get_text().rsplit("-", 1)[0]
+    if species in species_color:
+        lbl.set_color(species_color[species])
         lbl.set_fontweight("bold")
 
-# Style the plot with seaborn-compatible settings
+# Axes and title
 ax.set_xlabel("Iris Samples (by Species)", fontsize=20)
 ax.set_ylabel("Distance (Ward Linkage)", fontsize=20)
-ax.set_title("dendrogram-basic · seaborn · pyplots.ai", fontsize=24)
+ax.set_title("dendrogram-basic \u00b7 seaborn \u00b7 pyplots.ai", fontsize=24, fontweight="medium", pad=16)
 ax.tick_params(axis="y", labelsize=16)
 ax.tick_params(axis="x", labelsize=14)
 
-# Make grid subtle
-ax.grid(True, alpha=0.3, linestyle="--", axis="y")
+# Grid and spines
+ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color="#cccccc")
 ax.set_axisbelow(True)
-
-# Add legend using scatter plot handles for exact color matching
-for i, species in enumerate(species_names):
-    ax.scatter([], [], c=[palette[i]], s=150, label=species, marker="s")
-ax.legend(title="Species", loc="upper right", fontsize=14, title_fontsize=16, framealpha=0.9)
-
-# Remove top and right spines for cleaner look
 sns.despine(ax=ax)
 
+# Legend
+for name, color in species_color.items():
+    ax.scatter([], [], c=[color], s=150, label=name, marker="s")
+ax.legend(title="Species", loc="upper right", fontsize=14, title_fontsize=16, framealpha=0.9, edgecolor="#dddddd")
+
 plt.tight_layout()
 plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/dendrogram-basic/metadata/seaborn.yaml

@@ -1,15 +1,15 @@
 library: seaborn
 specification_id: dendrogram-basic
 created: '2025-12-23T10:01:46Z'
-updated: '2025-12-23T10:24:16Z'
-generated_by: claude-opus-4-5-20251101
+updated: '2026-04-05T20:00:00+00:00'
+generated_by: claude-opus-4-6
 workflow_run: 20457530242
 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/dendrogram-basic/seaborn/plot.png
 preview_html: null
-quality_score: 91
+quality_score: null
 impl_tags:
   dependencies:
   - scipy