feat: add pie-basic implementation (9 libraries) (#513)

## Summary
Adds `pie-basic` plot implementation.

### Libraries
- **Merged:** 9 (all libraries)
- **Not Feasible:** 0

### Links
- **Spec:** `specs/pie-basic.md`
- **Parent Issue:** #206

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Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>

Author: 3 people

plots/altair/arc/pie-basic/default.py

@@ -7,152 +7,54 @@
 import pandas as pd
 
 
-# PyPlots.ai default color palette
-PYPLOTS_COLORS = ["#306998", "#FFD43B", "#DC2626", "#059669", "#8B5CF6", "#F97316"]
-
-
-def create_plot(
-    data: pd.DataFrame,
-    category: str,
-    value: str,
-    *,
-    title: str | None = None,
-    colors: list[str] | None = None,
-    startangle: float = 90,
-    show_labels: bool = True,
-    label_format: str = ".1%",
-    legend: bool = True,
-    legend_loc: str = "right",
-    inner_radius: float = 0,
-    outer_radius: float = 150,
-    **kwargs,
-) -> alt.Chart:
-    """
-    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.
-
-    Args:
-        data: Input DataFrame containing the data to plot.
-        category: Column name for category labels (slice names).
-        value: Column name for numeric values (slice sizes).
-        title: Plot title. Defaults to None.
-        colors: Custom color palette for slices. Defaults to PyPlots.ai palette.
-        startangle: Starting angle for first slice in degrees. Defaults to 90.
-        show_labels: Whether to show percentage labels on slices. Defaults to True.
-        label_format: Format string for percentage labels. Defaults to ".1%".
-        legend: Whether to display legend. Defaults to True.
-        legend_loc: Legend location ('right', 'left', 'top', 'bottom'). Defaults to 'right'.
-        inner_radius: Inner radius for donut style (0 for solid pie). Defaults to 0.
-        outer_radius: Outer radius of the pie. Defaults to 150.
-        **kwargs: Additional parameters.
-
-    Returns:
-        Altair Chart 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': ['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}")
-
-    # Validate non-negative values
-    if (data[value] < 0).any():
-        raise ValueError("Pie chart values must be non-negative")
-
-    # Handle case where all values are zero
-    total = data[value].sum()
-    if total == 0:
-        raise ValueError("Sum of values cannot be zero")
-
-    # Use custom colors or default palette
-    color_palette = colors if colors is not None else PYPLOTS_COLORS
+# Data
+data = pd.DataFrame(
+    {"category": ["Product A", "Product B", "Product C", "Product D", "Other"], "value": [35, 25, 20, 15, 5]}
+)
 
-    # Calculate the starting angle in radians (Altair uses radians, offset from 12 o'clock)
-    # Altair's theta starts from 3 o'clock (0 degrees), so we need to adjust
-    # To start from 12 o'clock (90 degrees from 3 o'clock), we use theta2Offset
-    start_offset = (startangle - 90) * 3.14159 / 180
+# Calculate percentages for labels
+total = data["value"].sum()
+data["percentage"] = data["value"] / total
 
-    # Create base chart with arc mark
-    base = alt.Chart(data).encode(
-        theta=alt.Theta(f"{value}:Q", stack=True),
-        color=alt.Color(
-            f"{category}:N",
-            scale=alt.Scale(range=color_palette),
-            legend=alt.Legend(title=category, orient=legend_loc, labelFontSize=16, titleFontSize=16)
-            if legend
-            else None,
-        ),
-        tooltip=[alt.Tooltip(f"{category}:N", title="Category"), alt.Tooltip(f"{value}:Q", title="Value")],
-    )
-
-    # Create the pie/arc chart
-    pie = base.mark_arc(
-        innerRadius=inner_radius,
-        outerRadius=outer_radius,
-        stroke="#ffffff",
-        strokeWidth=2,
-        theta2Offset=start_offset,
-        thetaOffset=start_offset,
-    )
-
-    # Add percentage labels if requested
-    if show_labels:
-        # Calculate percentage for labels
-        data_with_pct = data.copy()
-        data_with_pct["_percentage"] = data_with_pct[value] / total
-
-        # Create text labels positioned at the middle of each arc
-        text = (
-            alt.Chart(data_with_pct)
-            .mark_text(radius=outer_radius * 0.7, fontSize=14, fontWeight="bold", color="#FFFFFF")
-            .encode(theta=alt.Theta(f"{value}:Q", stack=True), text=alt.Text("_percentage:Q", format=label_format))
-            .transform_calculate(theta2Offset=str(start_offset), thetaOffset=str(start_offset))
-        )
-
-        # Layer pie and text
-        chart = alt.layer(pie, text)
-    else:
-        chart = pie
-
-    # Set chart dimensions and title
-    chart = chart.properties(width=400, height=400)
-
-    if title is not None:
-        chart = chart.properties(title=alt.TitleParams(text=title, fontSize=20, anchor="middle", fontWeight=600))
-
-    # Configure chart appearance
-    chart = chart.configure_view(strokeWidth=0).configure_legend(
-        labelFontSize=16, titleFontSize=16, symbolSize=200, padding=10
-    )
-
-    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
-    fig = create_plot(sample_data, "category", "value", title="Market Share Distribution")
+# PyPlots.ai default color palette
+PYPLOTS_COLORS = ["#306998", "#FFD43B", "#DC2626", "#059669", "#8B5CF6", "#F97316"]
 
-    # Save
-    fig.save("plot.png", scale_factor=2.0)
-    print("Plot saved to plot.png")
+# Create pie chart using arc mark
+# Altair uses theta encoding for pie/arc charts
+base = alt.Chart(data).encode(
+    theta=alt.Theta("value:Q", stack=True),
+    color=alt.Color(
+        "category:N",
+        scale=alt.Scale(range=PYPLOTS_COLORS),
+        legend=alt.Legend(title="Category", orient="right", labelFontSize=16, titleFontSize=16),
+    ),
+    tooltip=[
+        alt.Tooltip("category:N", title="Category"),
+        alt.Tooltip("value:Q", title="Value"),
+        alt.Tooltip("percentage:Q", title="Share", format=".1%"),
+    ],
+)
+
+# Create the pie with arc mark
+pie = base.mark_arc(innerRadius=0, outerRadius=300, stroke="#ffffff", strokeWidth=2)
+
+# Add percentage labels on slices
+text = base.mark_text(radius=200, fontSize=20, fontWeight="bold", color="#FFFFFF").encode(
+    text=alt.Text("percentage:Q", format=".1%")
+)
+
+# Combine pie and labels
+chart = alt.layer(pie, text).properties(
+    width=800,
+    height=800,
+    title=alt.TitleParams(text="Market Share Distribution", fontSize=20, anchor="middle", fontWeight="bold"),
+)
+
+# Configure chart appearance
+chart = chart.configure_view(strokeWidth=0).configure_legend(
+    labelFontSize=16, titleFontSize=16, symbolSize=200, padding=20
+)
+
+# Save - scale_factor=3 gives 2400x2400 from 800x800 base
+# For pie charts, square aspect ratio is more appropriate
+chart.save("plot.png", scale_factor=3.0)