diff --git a/plots/pygal/pie/pie-basic/default.py b/plots/pygal/pie/pie-basic/default.py
index ca44c93104..1e9f933de1 100644
--- a/plots/pygal/pie/pie-basic/default.py
+++ b/plots/pygal/pie/pie-basic/default.py
@@ -3,145 +3,42 @@
 Library: pygal
 """
 
-import pandas as pd
 import pygal
 from pygal.style import Style
 
 
-# PyPlots.ai color palette
-PYPLOTS_COLORS = (
-    "#306998",  # Python Blue (Primary)
-    "#FFD43B",  # Python Yellow
-    "#DC2626",  # Signal Red
-    "#059669",  # Teal Green
-    "#8B5CF6",  # Violet
-    "#F97316",  # Orange
+# Data
+categories = ["Product A", "Product B", "Product C", "Product D", "Other"]
+values = [35, 25, 20, 15, 5]
+
+# Custom style matching default-style-guide.md colors
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333333",
+    foreground_strong="#333333",
+    foreground_subtle="#666666",
+    colors=("#306998", "#FFD43B", "#DC2626", "#059669", "#8B5CF6"),
+    title_font_size=60,
+    legend_font_size=48,
+    value_font_size=48,
+    tooltip_font_size=36,
 )
 
+# Create pie chart
+chart = pygal.Pie(
+    width=4800,
+    height=2700,
+    title="Market Share Distribution",
+    style=custom_style,
+    inner_radius=0,
+    show_legend=True,
+    legend_at_bottom=True,
+)
 
-def create_plot(
-    data: pd.DataFrame,
-    category: str,
-    value: str,
-    figsize: tuple[int, int] = (1600, 900),
-    title: str | None = None,
-    colors: list[str] | None = None,
-    startangle: float = 90,
-    legend: bool = True,
-    legend_loc: str = "right",
-    inner_radius: float = 0,
-    **kwargs,
-) -> pygal.Pie:
-    """
-    Create a basic pie chart for visualizing proportions of categorical data.
-
-    Args:
-        data: Input DataFrame containing the data to plot.
-        category: Column name for category labels.
-        value: Column name for numeric values representing each slice's proportion.
-        figsize: Figure size as (width, height) in pixels.
-        title: Optional plot title.
-        colors: Custom color palette for slices (defaults to PyPlots palette).
-        startangle: Starting angle for first slice in degrees (not used in pygal).
-        legend: Whether to display legend.
-        legend_loc: Legend location ('right', 'bottom', or 'top').
-        inner_radius: Inner radius for donut chart (0-1, 0 for solid pie).
-        **kwargs: Additional parameters passed to pygal.Pie.
-
-    Returns:
-        pygal.Pie chart object.
-
-    Raises:
-        ValueError: If data is empty or contains negative values.
-        KeyError: If required columns are not found in data.
-
-    Example:
-        >>> data = pd.DataFrame({
-        ...     'category': ['Product A', 'Product B', 'Product C'],
-        ...     'value': [35, 25, 40]
-        ... })
-        >>> chart = 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}")
-
-    # Handle missing values
-    clean_data = data[[category, value]].dropna()
-
-    if clean_data.empty:
-        raise ValueError("Data cannot be empty after removing missing values")
-
-    # Validate non-negative values
-    if (clean_data[value] < 0).any():
-        raise ValueError("Pie chart values must be non-negative")
-
-    # Check if all values sum to zero
-    total = clean_data[value].sum()
-    if total == 0:
-        raise ValueError("Values sum to zero; cannot create pie chart")
-
-    # Use provided colors or default PyPlots palette
-    chart_colors = tuple(colors) if colors else PYPLOTS_COLORS
-
-    # Create custom style
-    custom_style = Style(
-        background="white",
-        plot_background="white",
-        foreground="#333333",
-        foreground_strong="#333333",
-        foreground_subtle="#666666",
-        colors=chart_colors,
-        font_family="Inter, DejaVu Sans, Arial, Helvetica, sans-serif",
-        title_font_size=20,
-        legend_font_size=16,
-        value_font_size=14,
-        tooltip_font_size=14,
-    )
-
-    # Determine legend position
-    legend_at_bottom = legend_loc == "bottom"
-    legend_box_size = 16 if legend else 0
-
-    # Create chart
-    chart = pygal.Pie(
-        width=figsize[0],
-        height=figsize[1],
-        title=title,
-        style=custom_style,
-        show_legend=legend,
-        legend_at_bottom=legend_at_bottom,
-        legend_box_size=legend_box_size,
-        inner_radius=inner_radius,
-        print_values=True,
-        value_formatter=lambda x: f"{x:.1f}%",
-        **kwargs,
-    )
-
-    # Add each category as a separate slice with percentage value
-    for _, row in clean_data.iterrows():
-        cat_name = str(row[category])
-        cat_value = float(row[value])
-        percentage = (cat_value / total) * 100
-        chart.add(cat_name, [{"value": percentage, "label": cat_name}])
-
-    return chart
-
-
-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
-    chart = create_plot(sample_data, "category", "value", title="Market Share Distribution")
+# Add data slices
+for category, value in zip(categories, values, strict=True):
+    chart.add(category, value)
 
-    # Save to PNG
-    chart.render_to_png("plot.png")
-    print("Plot saved to plot.png")
+# Save as PNG
+chart.render_to_png("plot.png")