feat(letsplot): implement histogram-density (#2487)

## Implementation: `histogram-density` - letsplot

Implements the **letsplot** version of `histogram-density`.

**File:** `plots/histogram-density/implementations/letsplot.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20584334205)*

---------

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

Author: github-actions[bot]

plots/histogram-density/implementations/letsplot.py

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+""" pyplots.ai
+histogram-density: Density Histogram
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-29
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *
+from scipy import stats
+
+
+LetsPlot.setup_html()
+
+# Data - Test scores with normal-like distribution
+np.random.seed(42)
+scores = np.concatenate(
+    [
+        np.random.normal(72, 8, 300),  # Main group
+        np.random.normal(88, 5, 100),  # High performers
+    ]
+)
+# Clip to realistic test score range
+scores = np.clip(scores, 0, 100)
+
+# Create DataFrame
+df = pd.DataFrame({"score": scores})
+
+# Create theoretical normal distribution for overlay
+x_range = np.linspace(scores.min() - 5, scores.max() + 5, 200)
+# Fit normal distribution to data
+mu, sigma = stats.norm.fit(scores)
+y_pdf = stats.norm.pdf(x_range, mu, sigma)
+df_pdf = pd.DataFrame({"x": x_range, "y": y_pdf})
+
+# Create density histogram with KDE overlay
+plot = (
+    ggplot()
+    + geom_histogram(
+        aes(x="score", y="..density.."), data=df, bins=25, fill="#306998", color="white", alpha=0.7, size=0.5
+    )
+    + geom_line(aes(x="x", y="y"), data=df_pdf, color="#FFD43B", size=2.5)
+    + labs(x="Test Score", y="Density", title="histogram-density · letsplot · pyplots.ai")
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        panel_grid_major=element_line(color="#CCCCCC", size=0.3),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save as PNG (scale 3x = 4800 x 2700 px)
+ggsave(plot, "plot.png", path=".", scale=3)
+
+# Save as HTML for interactivity
+ggsave(plot, "plot.html", path=".")

plots/histogram-density/metadata/letsplot.yaml

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+library: letsplot
+specification_id: histogram-density
+created: '2025-12-29T22:53:39Z'
+updated: '2025-12-29T23:01:40Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20584334205
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/histogram-density/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/histogram-density/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/histogram-density/letsplot/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of density histogram with proper ..density.. stat transformation
+  - Clean addition of fitted normal distribution PDF overlay using scipy.stats
+  - 'Appropriate color scheme (#306998 blue bars, #FFD43B yellow line) with good contrast'
+  - Proper use of lets-plot ggplot2-like grammar with theme customization
+  - Bimodal data distribution effectively demonstrates density normalization concept
+  - Correct figure sizing (1600x900 base with scale=3) and text sizing per library
+    guidelines
+  weaknesses:
+  - Axis labels lack units (could specify Test Score points or Density probability
+    per unit)
+  - A legend explaining the yellow line as Fitted Normal Distribution would add clarity
+  - Could leverage more distinctive lets-plot features like scale_fill_* or tooltip
+    configuration