feat(seaborn): implement pie-basic

Simplify pie chart implementation following KISS principles:
- Remove function-based structure
- Use sequential script approach like gallery examples
- Apply 16:9 figsize for 4800x2700px output at 300 DPI
- Follow PyPlots.ai style guide font sizes
- Keep seaborn styling context for consistent aesthetics

Note: Seaborn lacks native pie chart support, so this uses
matplotlib's pie with seaborn styling.

Author: github-actions[bot]

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

@@ -6,179 +6,75 @@
 matplotlib's pie chart with seaborn's styling context for consistent aesthetics.
 """
 
-from typing import TYPE_CHECKING
-
 import matplotlib.pyplot as plt
 import pandas as pd
 import seaborn as sns
 
 
-if TYPE_CHECKING:
-    from matplotlib.figure import Figure
-
-
 # PyPlots.ai color palette
 PYPLOTS_COLORS = [
-    "#306998",  # Python Blue (Primary)
+    "#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 visualizing proportions of categorical data.
-
-    A fundamental pie chart where each slice represents a category's share of the
-    whole, ideal for showing composition and distribution across a small number
-    of categories.
-
-    Note: Seaborn does not have a native pie chart function. This implementation
-    uses matplotlib's pie chart with seaborn's styling context.
-
-    Args:
-        data: Input DataFrame containing category and value columns
-        category: Column name for category labels
-        value: Column name for numeric values (proportions)
-        figsize: Figure size as (width, height) in inches
-        title: Plot title (optional)
-        colors: Custom color palette for slices (uses PyPlots.ai palette if None)
-        startangle: Starting angle for first slice in degrees from positive x-axis
-        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 if None)
-        legend: Whether to display legend
-        legend_loc: Legend location
-        **kwargs: Additional parameters passed to ax.pie()
-
-    Returns:
-        Matplotlib Figure object
-
-    Raises:
-        ValueError: If data is empty or contains negative values
-        KeyError: If required columns not found in data
-
-    Example:
-        >>> data = pd.DataFrame({
-        ...     'category': ['Product A', 'Product B', 'Product C'],
-        ...     'value': [35, 40, 25]
-        ... })
-        >>> fig = create_plot(data, 'category', 'value', title='Market Share')
-    """
-    # 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}")
-
-    # Extract values and validate
-    values = data[value].values
-    categories = data[category].values
-
-    if (values < 0).any():
-        raise ValueError("Pie chart values cannot be negative")
-
-    if values.sum() == 0:
-        raise ValueError("Sum of values cannot be zero")
-
-    # Set seaborn style context for consistent aesthetics
-    sns.set_theme(style="white")
-
-    # Create figure with equal aspect ratio to prevent elliptical distortion
-    fig, ax = plt.subplots(figsize=figsize)
-    ax.set_aspect("equal")
-
-    # Determine colors
-    if colors is None:
-        # Extend palette if needed for more categories
-        n_categories = len(categories)
-        if n_categories <= len(PYPLOTS_COLORS):
-            pie_colors = PYPLOTS_COLORS[:n_categories]
-        else:
-            # Use seaborn color palette for many categories
-            pie_colors = sns.color_palette("husl", n_categories)
-    else:
-        pie_colors = colors
-
-    # Determine labels
-    pie_labels = labels if labels is not None else categories
-
-    # Create pie chart
-    wedges, texts, autotexts = ax.pie(
-        values,
-        labels=pie_labels if not legend else None,
-        autopct=autopct,
-        startangle=startangle,
-        explode=explode,
-        shadow=shadow,
-        colors=pie_colors,
-        wedgeprops={"edgecolor": "white", "linewidth": 1.5},
-        textprops={"fontsize": 12},
-        pctdistance=0.75,
-        **kwargs,
-    )
-
-    # Style percentage labels
-    for autotext in autotexts:
-        autotext.set_fontsize(11)
-        autotext.set_fontweight("bold")
-        autotext.set_color("white")
-
-    # Add legend if requested
-    if legend:
-        ax.legend(
-            wedges,
-            pie_labels,
-            title=category,
-            loc=legend_loc,
-            bbox_to_anchor=(1, 0, 0.5, 1),
-            frameon=True,
-            facecolor="white",
-            edgecolor="gray",
-            fontsize=11,
-        )
-
-    # Set title if provided
-    if title is not None:
-        ax.set_title(title, fontsize=16, fontweight="semibold", pad=20)
-
-    # Layout adjustment
-    plt.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 - ALWAYS use 'plot.png'!
-    plt.savefig("plot.png", dpi=300, bbox_inches="tight")
-    print("Plot saved to plot.png")
+# Data from spec
+data = pd.DataFrame(
+    {"category": ["Product A", "Product B", "Product C", "Product D", "Other"], "value": [35, 25, 20, 15, 5]}
+)
+
+# Set seaborn style for consistent aesthetics
+sns.set_theme(style="white")
+
+# Create figure (16:9 aspect ratio for 4800x2700 at 300 DPI)
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Extract data
+categories = data["category"].tolist()
+values = data["value"].tolist()
+
+# Use PyPlots colors
+colors = PYPLOTS_COLORS[: len(categories)]
+
+# Create pie chart
+wedges, texts, autotexts = ax.pie(
+    values,
+    autopct="%1.1f%%",
+    startangle=90,
+    colors=colors,
+    wedgeprops={"edgecolor": "white", "linewidth": 2},
+    textprops={"fontsize": 16},
+    pctdistance=0.7,
+)
+
+# Style percentage labels
+for autotext in autotexts:
+    autotext.set_fontsize(16)
+    autotext.set_fontweight("bold")
+    autotext.set_color("white")
+
+# Ensure circular shape
+ax.set_aspect("equal")
+
+# Add legend
+ax.legend(
+    wedges,
+    categories,
+    title="Category",
+    loc="center left",
+    bbox_to_anchor=(1.0, 0.5),
+    fontsize=16,
+    title_fontsize=16,
+    frameon=True,
+    facecolor="white",
+    edgecolor="gray",
+)
+
+# Title
+ax.set_title("Market Share Distribution", fontsize=20, fontweight="semibold", pad=20)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor="white")