update(pie-basic): letsplot — comprehensive quality review (#4223)

## Summary

Updated **letsplot** implementation for **pie-basic**.

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

## Test Plan

- [x] Preview images uploaded to GCS staging
- [x] Implementation file passes ruff format/check
- [x] Metadata YAML updated with current versions
- [ ] Automated review triggered

---
Generated with [Claude Code](https://claude.com/claude-code) `/update`
command

---------

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

Author: MarkusNeusinger

plots/pie-basic/implementations/letsplot.py

@@ -1,58 +1,62 @@
 """ pyplots.ai
 pie-basic: Basic Pie Chart
-Library: letsplot 4.8.1 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: letsplot 4.8.2 | Python 3.14.0
+Quality: 82/100 | Created: 2025-12-23
 """
 
-import pandas as pd
 from lets_plot import *  # noqa: F403
-from lets_plot.export import ggsave as export_ggsave
+from lets_plot import ggsave
 
 
 LetsPlot.setup_html()  # noqa: F405
 
-# Data - Market share by company
-categories = ["Company A", "Company B", "Company C", "Company D", "Company E"]
-values = [35, 25, 20, 12, 8]
+# Data - Global smartphone market share (2024)
+data = {
+    "company": ["Apple", "Samsung", "Xiaomi", "OPPO", "Others"],
+    "share": [23.1, 19.4, 13.7, 8.8, 35.0],
+    "explode": [0.0, 0.0, 0.0, 0.12, 0.0],
+}
 
-df = pd.DataFrame({"category": categories, "value": values})
+# Colors - Python Blue first, then colorblind-safe palette
+colors = ["#306998", "#FFD43B", "#4CAF50", "#AB47BC", "#90A4AE"]
 
-# Calculate percentages for labels
-total = sum(values)
-df["pct"] = df["value"] / total * 100
-
-# Preserve category order
-df["category"] = pd.Categorical(df["category"], categories=categories, ordered=True)
-
-# Define colors - Python Blue first, then colorblind-safe palette
-colors = ["#306998", "#FFD43B", "#4CAF50", "#FF7043", "#AB47BC"]
-
-# Plot
+# Plot — square canvas fills space evenly for circular pie charts
 plot = (
-    ggplot(df)  # noqa: F405
+    ggplot(data)  # noqa: F405
     + geom_pie(  # noqa: F405
-        aes(slice="value", fill="category"),  # noqa: F405
+        aes(slice="share", fill="company", explode="explode"),  # noqa: F405
         stat="identity",
-        size=20,  # Large size for visibility at 4800x2700
-        hole=0,  # Full pie (not donut)
+        size=75,
+        hole=0,
+        stroke=2,
+        stroke_side="both",
+        color="white",
+        spacer_width=1.5,
+        spacer_color="white",
         labels=layer_labels()  # noqa: F405
-        .line("@pct")
-        .format("pct", "{.1f}%")
-        .size(14),
+        .line("@{share}")
+        .format("share", "{.1f}%")
+        .size(18),
     )
     + scale_fill_manual(values=colors)  # noqa: F405
     + labs(  # noqa: F405
-        title="pie-basic · letsplot · pyplots.ai", fill="Category"
+        title="Global Smartphone Market Share · pie-basic · letsplot · pyplots.ai",
+        subtitle="OPPO's 8.8% slice is the smallest — 'Others' dominate at 35%",
+        fill="Brand",
     )
-    + ggsize(1600, 900)  # noqa: F405
+    + ggsize(1200, 1200)  # noqa: F405
     + theme_void()  # noqa: F405
     + theme(  # noqa: F405
-        plot_title=element_text(size=24, hjust=0.5),  # noqa: F405
-        legend_title=element_text(size=18),  # noqa: F405
-        legend_text=element_text(size=16),  # noqa: F405
+        plot_title=element_text(size=26, hjust=0.5, face="bold"),  # noqa: F405
+        plot_subtitle=element_text(size=16, hjust=0.5, color="#555555"),  # noqa: F405
+        legend_title=element_text(size=20),  # noqa: F405
+        legend_text=element_text(size=18),  # noqa: F405
+        plot_margin=[30, 20, 30, 20],
+        legend_position="bottom",
+        legend_direction="horizontal",
     )
 )
 
 # Save
-export_ggsave(plot, filename="plot.png", path=".", scale=3)
-export_ggsave(plot, filename="plot.html", path=".")
+ggsave(plot, filename="plot.png", path=".", scale=3)
+ggsave(plot, filename="plot.html", path=".")