feat(plotnine): implement pie-basic (#370)

## Summary
Implements `pie-basic` for **plotnine** library.

**Parent Issue:** #206
**Sub-Issue:** #270
**Base Branch:** `plot/pie-basic`
**Attempt:** 1/3

## Implementation
- `plots/plotnine/pie/pie-basic/default.py`

## Details
- Simplified to KISS pattern (no functions, sequential script)
- Uses matplotlib directly since plotnine lacks `coord_polar()` support
- Applies PyPlots.ai color palette
- Uses 16:9 aspect ratio (figsize 16x9 at 300 dpi)
- Legend positioned outside pie for clarity
- Percentage labels displayed on each slice

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

Author: claude[bot]

plots/plotnine/pie/pie-basic/default.py

@@ -2,194 +2,71 @@
 pie-basic: Basic Pie Chart
 Library: plotnine
 
-A fundamental pie chart that visualizes proportions and percentages of categorical data
-as slices of a circular chart.
-
-Note: plotnine (ggplot2 for Python) does not support coord_polar() as of version 0.15.x,
-which is required for true pie charts in the grammar of graphics. This implementation
-uses matplotlib directly (plotnine's underlying engine) to create the pie chart while
-maintaining a compatible interface and following PyPlots.ai style guidelines.
+Note: plotnine does not support coord_polar() required for pie charts.
+This implementation uses matplotlib directly while following PyPlots.ai style.
 """
 
-from typing import TYPE_CHECKING
-
 import matplotlib.pyplot as plt
 import pandas as pd
 
 
-if TYPE_CHECKING:
-    from matplotlib.figure import Figure
+# Data
+data = pd.DataFrame(
+    {"category": ["Product A", "Product B", "Product C", "Product D", "Other"], "value": [35, 25, 20, 15, 5]}
+)
 
 # PyPlots.ai color palette
-PYPLOTS_COLORS = [
-    "#306998",  # Python Blue (Primary)
+colors = [
+    "#306998",  # Python Blue
     "#FFD43B",  # Python Yellow
     "#DC2626",  # Signal Red
     "#059669",  # Teal Green
     "#8B5CF6",  # Violet
     "#F97316",  # Orange
 ]
 
-
-def create_plot(
-    data: pd.DataFrame,
-    category: str,
-    value: str,
-    figsize: tuple[float, float] = (10, 8),
-    title: str | None = None,
-    colors: list[str] | None = None,
-    startangle: float = 90,
-    autopct: str = "%1.1f%%",
-    explode: list[float] | None = None,
-    shadow: bool = False,
-    labels: list[str] | None = None,
-    legend: bool = True,
-    legend_loc: str = "best",
-    **kwargs,
-) -> "Figure":
-    """
-    Create a basic pie chart.
-
-    Note: plotnine does not support polar coordinates (coord_polar), so this
-    implementation uses matplotlib directly while maintaining a compatible interface.
-
-    Args:
-        data: Input DataFrame containing category and value columns
-        category: Column name for category names (slice labels)
-        value: Column name for numeric values (slice proportions)
-        figsize: Figure size as (width, height)
-        title: Plot title (optional)
-        colors: Custom color palette for slices (defaults to PyPlots palette)
-        startangle: Starting angle for first slice in degrees (default 90 = top)
-        autopct: Format string for percentage labels
-        explode: Offset distances for each slice (0-0.1 typical)
-        shadow: Add shadow effect for 3D appearance
-        labels: Custom labels (defaults to category names)
-        legend: Display legend
-        legend_loc: Legend location ('best', 'upper right', 'lower left', etc.)
-        **kwargs: Additional parameters passed to matplotlib pie()
-
-    Returns:
-        matplotlib Figure object
-
-    Raises:
-        ValueError: If data is empty or values contain negative numbers
-        KeyError: If required columns are not found in data
-
-    Example:
-        >>> data = pd.DataFrame({
-        ...     'category': ['A', 'B', 'C', 'D'],
-        ...     'value': [35, 25, 20, 20]
-        ... })
-        >>> fig = create_plot(data, 'category', 'value', title='Distribution')
-    """
-    # Input validation
-    if data.empty:
-        raise ValueError("Data cannot be empty")
-
-    for col in [category, value]:
-        if col not in data.columns:
-            available = ", ".join(data.columns)
-            raise KeyError(f"Column '{col}' not found. Available: {available}")
-
-    # Validate non-negative values
-    if (data[value] < 0).any():
-        raise ValueError("Pie chart values must be non-negative")
-
-    # Handle case where all values sum to zero
-    total = data[value].sum()
-    if total == 0:
-        raise ValueError("Total of values cannot be zero")
-
-    # Prepare data
-    values = data[value].tolist()
-    category_labels = labels if labels is not None else data[category].astype(str).tolist()
-
-    # Validate labels length if custom labels provided
-    if labels is not None and len(labels) != len(data):
-        raise ValueError(f"Labels length ({len(labels)}) must match data length ({len(data)})")
-
-    # Set up colors
-    n_categories = len(data)
-    if colors is None:
-        # Extend palette if more categories than colors
-        plot_colors = (PYPLOTS_COLORS * ((n_categories // len(PYPLOTS_COLORS)) + 1))[:n_categories]
-    else:
-        if len(colors) < n_categories:
-            plot_colors = (colors * ((n_categories // len(colors)) + 1))[:n_categories]
-        else:
-            plot_colors = colors[:n_categories]
-
-    # Create figure with white background
-    fig, ax = plt.subplots(figsize=figsize, facecolor="white")
-    ax.set_facecolor("white")
-
-    # Configure explode
-    pie_explode = explode if explode is not None else None
-    if pie_explode is not None and len(pie_explode) != n_categories:
-        raise ValueError(f"Explode length ({len(pie_explode)}) must match data length ({n_categories})")
-
-    # Configure text properties for percentage labels
-    textprops = {"fontsize": 14, "fontweight": "bold", "color": "white"}
-
-    # Create pie chart - hide labels on the pie itself since we'll use legend
-    wedges, texts, autotexts = ax.pie(
-        values,
-        labels=None if legend else category_labels,  # Labels on pie only if no legend
-        autopct=autopct if autopct else None,
-        startangle=startangle,
-        colors=plot_colors,
-        explode=pie_explode,
-        shadow=shadow,
-        textprops=textprops,
-        wedgeprops={"linewidth": 1, "edgecolor": "white"},
-        **kwargs,
-    )
-
-    # Style the percentage labels with better contrast
-    for autotext in autotexts:
-        autotext.set_fontsize(14)
-        autotext.set_fontweight("bold")
-        # Use white for darker colors, dark for lighter colors
-        autotext.set_color("white")
-
-    # Add legend if requested
-    if legend:
-        ax.legend(
-            wedges,
-            category_labels,
-            title=category,
-            loc=legend_loc,
-            fontsize=14,
-            title_fontsize=16,
-            frameon=True,
-            facecolor="white",
-            edgecolor="gray",
-            framealpha=1.0,
-        )
-
-    # Add title if provided
-    if title:
-        ax.set_title(title, fontsize=20, fontweight="semibold", pad=20)
-
-    # Ensure equal aspect ratio for circular pie
-    ax.set_aspect("equal")
-
-    # Adjust layout
-    fig.tight_layout()
-
-    return fig
-
-
-if __name__ == "__main__":
-    # Sample data for testing
-    sample_data = pd.DataFrame(
-        {"category": ["Product A", "Product B", "Product C", "Product D", "Other"], "value": [35, 25, 20, 15, 5]}
-    )
-
-    # Create plot
-    fig = create_plot(sample_data, "category", "value", title="Market Share Distribution")
-
-    # Save
-    fig.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor="white")
-    print("Plot saved to plot.png")
+# Create figure (16:9 aspect ratio for 4800x2700 at 300 dpi)
+fig, ax = plt.subplots(figsize=(16, 9), facecolor="white")
+ax.set_facecolor("white")
+
+# Create pie chart
+wedges, texts, autotexts = ax.pie(
+    data["value"],
+    autopct="%1.1f%%",
+    startangle=90,
+    colors=colors[: len(data)],
+    textprops={"fontsize": 16, "fontweight": "bold", "color": "white"},
+    wedgeprops={"linewidth": 2, "edgecolor": "white"},
+)
+
+# Style percentage labels
+for autotext in autotexts:
+    autotext.set_fontsize(16)
+    autotext.set_fontweight("bold")
+    autotext.set_color("white")
+
+# Add legend
+ax.legend(
+    wedges,
+    data["category"],
+    title="Category",
+    loc="center left",
+    bbox_to_anchor=(1.0, 0.5),
+    fontsize=16,
+    title_fontsize=20,
+    frameon=True,
+    facecolor="white",
+    edgecolor="gray",
+)
+
+# Title
+ax.set_title("Basic Pie Chart", fontsize=20, fontweight="semibold", pad=20)
+
+# Ensure circular shape
+ax.set_aspect("equal")
+
+# Adjust layout
+plt.tight_layout()
+
+# Save
+plt.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor="white")