feat: implement box-basic plot for all 8 libraries

- Created spec file specs/box-basic.md defining requirements for basic box plots
- Implemented matplotlib version with customizable colors and outlier display
- Implemented seaborn version with statistical annotations
- Implemented plotly version with interactive hover information
- Implemented bokeh version with HTML output and customizable whiskers
- Implemented altair version with declarative Vega-Lite approach
- Implemented plotnine version with ggplot2-style grammar of graphics
- Implemented pygal version with SVG output
- Implemented highcharts version with interactive web visualization

All implementations:
- Show quartiles (Q1, median, Q3) as boxes
- Display whiskers extending to 1.5 * IQR
- Mark outliers as individual points
- Include sample size annotations
- Support multiple groups for comparison
- Use deterministic sample data with fixed seed

Closes #41

Author: github-actions[bot]

plots/altair/box/box-basic/default.py

@@ -0,0 +1,200 @@
+"""
+box-basic: Basic Box Plot
+Implementation for: altair
+Variant: default
+Python: 3.10+
+"""
+
+import altair as alt
+import pandas as pd
+import numpy as np
+from typing import TYPE_CHECKING, Optional
+
+if TYPE_CHECKING:
+    from altair import Chart
+
+
+def create_plot(
+    data: pd.DataFrame,
+    values: str,
+    groups: str,
+    title: Optional[str] = None,
+    xlabel: Optional[str] = None,
+    ylabel: Optional[str] = None,
+    color_scheme: str = 'set2',
+    width: int = 600,
+    height: int = 400,
+    **kwargs
+) -> Chart:
+    """
+    Create a basic box plot showing statistical distribution of multiple groups using altair.
+
+    Args:
+        data: Input DataFrame with required columns
+        values: Column name containing numeric values
+        groups: Column name containing group categories
+        title: Plot title (optional)
+        xlabel: Custom x-axis label (optional, defaults to groups column name)
+        ylabel: Custom y-axis label (optional, defaults to values column name)
+        color_scheme: Color scheme for boxes (default: 'set2')
+        width: Figure width in pixels (default: 600)
+        height: Figure height in pixels (default: 400)
+        **kwargs: Additional parameters for altair chart configuration
+
+    Returns:
+        Altair Chart object
+
+    Raises:
+        ValueError: If data is empty
+        KeyError: If required columns not found
+
+    Example:
+        >>> data = pd.DataFrame({
+        ...     'Group': ['A', 'A', 'B', 'B', 'C', 'C'],
+        ...     'Value': [1, 2, 2, 3, 3, 4]
+        ... })
+        >>> chart = create_plot(data, values='Value', groups='Group')
+    """
+    # Input validation
+    if data.empty:
+        raise ValueError("Data cannot be empty")
+
+    # Check required columns
+    for col in [values, groups]:
+        if col not in data.columns:
+            available = ", ".join(data.columns)
+            raise KeyError(f"Column '{col}' not found. Available columns: {available}")
+
+    # Create the box plot using Altair's mark_boxplot
+    base = alt.Chart(data).mark_boxplot(
+        extent=1.5,  # 1.5 * IQR for whiskers
+        outliers=True,
+        size=40,
+        opacity=0.7
+    ).encode(
+        x=alt.X(
+            f'{groups}:N',
+            title=xlabel or groups,
+            axis=alt.Axis(
+                labelAngle=0 if data[groups].nunique() <= 5 else -45,
+                labelLimit=200
+            )
+        ),
+        y=alt.Y(
+            f'{values}:Q',
+            title=ylabel or values,
+            scale=alt.Scale(zero=False)
+        ),
+        color=alt.Color(
+            f'{groups}:N',
+            scale=alt.Scale(scheme=color_scheme),
+            legend=None  # Hide legend as it's redundant with x-axis
+        ),
+        tooltip=[
+            alt.Tooltip(f'{groups}:N', title='Group'),
+            alt.Tooltip(f'count({values}):Q', title='Count'),
+            alt.Tooltip(f'min({values}):Q', title='Min', format='.2f'),
+            alt.Tooltip(f'q1({values}):Q', title='Q1', format='.2f'),
+            alt.Tooltip(f'median({values}):Q', title='Median', format='.2f'),
+            alt.Tooltip(f'q3({values}):Q', title='Q3', format='.2f'),
+            alt.Tooltip(f'max({values}):Q', title='Max', format='.2f')
+        ]
+    )
+
+    # Add sample size annotations
+    text = alt.Chart(data).mark_text(
+        align='center',
+        baseline='top',
+        dy=10,
+        fontSize=10,
+        opacity=0.7
+    ).encode(
+        x=alt.X(f'{groups}:N'),
+        y=alt.Y(f'min({values}):Q'),
+        text=alt.Text('count():Q', format='d')
+    ).transform_aggregate(
+        count='count()',
+        groupby=[groups]
+    )
+
+    # Combine box plot with annotations
+    chart = (base + text).properties(
+        width=width,
+        height=height,
+        title=alt.TitleParams(
+            text=title or 'Box Plot Distribution',
+            fontSize=16,
+            anchor='middle'
+        )
+    ).configure_view(
+        strokeWidth=0
+    ).configure_axis(
+        grid=True,
+        gridOpacity=0.3,
+        gridDash=[3, 3],
+        domainWidth=1,
+        tickWidth=1
+    ).configure_boxplot(
+        median=dict(color='red', strokeWidth=2),
+        box=dict(strokeWidth=1.5),
+        outliers=dict(fill='red', fillOpacity=0.5, size=50)
+    )
+
+    return chart
+
+
+if __name__ == '__main__':
+    # Sample data for testing with different distributions per group
+    np.random.seed(42)  # For reproducibility
+
+    # Generate sample data with 4 groups
+    data_dict = {
+        'Group': [],
+        'Value': []
+    }
+
+    # Group A: Normal distribution, mean=50, std=10
+    group_a_data = np.random.normal(50, 10, 40)
+    # Add some outliers
+    group_a_data = np.append(group_a_data, [80, 85, 15])
+
+    # Group B: Normal distribution, mean=60, std=15
+    group_b_data = np.random.normal(60, 15, 35)
+    # Add outliers
+    group_b_data = np.append(group_b_data, [100, 10])
+
+    # Group C: Normal distribution, mean=45, std=8
+    group_c_data = np.random.normal(45, 8, 45)
+
+    # Group D: Skewed distribution
+    group_d_data = np.random.gamma(2, 2, 30) + 40
+    # Add outliers
+    group_d_data = np.append(group_d_data, [75, 78, 20])
+
+    # Combine all data
+    for group, values in zip(
+        ['Group A', 'Group B', 'Group C', 'Group D'],
+        [group_a_data, group_b_data, group_c_data, group_d_data]
+    ):
+        data_dict['Group'].extend([group] * len(values))
+        data_dict['Value'].extend(values)
+
+    data = pd.DataFrame(data_dict)
+
+    # Create plot
+    chart = create_plot(
+        data,
+        values='Value',
+        groups='Group',
+        title='Statistical Distribution Comparison Across Groups',
+        ylabel='Measurement Value',
+        xlabel='Categories'
+    )
+
+    # Save for inspection
+    chart.save('plot.html')
+    print("Interactive plot saved to plot.html")
+
+    # Also save as PNG
+    chart.save('plot.png', scale_factor=2.0)
+    print("Static plot saved to plot.png")