feat(seaborn): implement box-basic

Simplify implementation to follow KISS principles:
- Remove function wrapper, use sequential script style
- Use style guide color palette
- Follow seaborn 0.14+ API (hue with palette, legend=False)
- Use spec-defined sample data

Author: github-actions[bot]

plots/seaborn/boxplot/box-basic/default.py

@@ -1,189 +1,53 @@
 """
 box-basic: Basic Box Plot
-Implementation for: seaborn
-Variant: default
-Python: 3.10+
+Library: seaborn
 """
 
-from typing import TYPE_CHECKING, Optional
-
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import seaborn as sns
 
 
-if TYPE_CHECKING:
-    from matplotlib.figure import Figure
-
-
-def create_plot(
-    data: pd.DataFrame,
-    values: str,
-    groups: str,
-    title: Optional[str] = None,
-    xlabel: Optional[str] = None,
-    ylabel: Optional[str] = None,
-    palette: Optional[str] = "Set2",
-    figsize: tuple[float, float] = (16, 9),
-    showfliers: bool = True,
-    **kwargs,
-) -> Figure:
-    """
-    Create a basic box plot showing statistical distribution of multiple groups using seaborn.
-
-    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)
-        palette: Color palette name for boxes (default: 'Set2')
-        figsize: Figure size as (width, height) in inches (default: (16, 9))
-        showfliers: Whether to show outliers (default: True)
-        **kwargs: Additional parameters passed to seaborn boxplot function
-
-    Returns:
-        Matplotlib Figure 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]
-        ... })
-        >>> fig = 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 figure
-    fig, ax = plt.subplots(figsize=figsize)
-
-    # Create boxplot with seaborn
-    sns.boxplot(
-        data=data,
-        x=groups,
-        y=values,
-        hue=groups,
-        palette=palette,
-        ax=ax,
-        showfliers=showfliers,
-        width=0.7,
-        linewidth=1.5,
-        fliersize=6,
-        legend=False,
-        **kwargs,
-    )
-
-    # Customize the appearance
-    # Set median line color to be more visible
-    for patch in ax.artists:
-        # Get the current face color
-        r, g, b, a = patch.get_facecolor()
-        # Set the box face color with some transparency
-        patch.set_facecolor((r, g, b, 0.7))
-        # Set edge color
-        patch.set_edgecolor("black")
-        patch.set_linewidth(1.2)
-
-    # Style the median lines
-    for line in ax.lines:
-        # Median lines are the ones inside the boxes
-        if line.get_linestyle() == "-" and line.get_marker() == "None":
-            line.set_color("red")
-            line.set_linewidth(2)
-
-    # Labels and title
-    ax.set_xlabel(xlabel or groups)
-    ax.set_ylabel(ylabel or values)
-
-    if title:
-        ax.set_title(title, fontsize=14, fontweight="bold", pad=20)
-
-    # Grid for better readability
-    ax.grid(True, axis="y", alpha=0.3, linestyle="--")
-    ax.set_axisbelow(True)
-
-    # Rotate x-axis labels if there are many groups
-    unique_groups = data[groups].nunique()
-    if unique_groups > 5:
-        plt.xticks(rotation=45, ha="right")
-
-    # Add some statistical annotations
-    # Calculate and display the number of data points per group
-    group_counts = data.groupby(groups)[values].count()
-    y_bottom = ax.get_ylim()[0]
-    for i, (_group_name, count) in enumerate(group_counts.items()):
-        ax.text(i, y_bottom, f"n={count}", ha="center", va="top", fontsize=9, alpha=0.7)
-
-    # Apply seaborn style for better aesthetics
-    sns.despine(ax=ax)
-
-    # Layout
-    plt.tight_layout()
-
-    return fig
-
-
-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],
-        strict=False,
-    ):
-        data_dict["Group"].extend([group] * len(values))
-        data_dict["Value"].extend(values)
-
-    data = pd.DataFrame(data_dict)
-
-    # Create plot
-    fig = create_plot(
-        data,
-        values="Value",
-        groups="Group",
-        title="Statistical Distribution Comparison Across Groups",
-        ylabel="Measurement Value",
-        xlabel="Categories",
-        palette="Set2",
-    )
-
-    # Save for inspection
-    plt.savefig("plot.png", dpi=300, bbox_inches="tight")
-    print("Plot saved to plot.png")
+# Data
+np.random.seed(42)
+data = pd.DataFrame(
+    {
+        "group": ["A"] * 50 + ["B"] * 50 + ["C"] * 50 + ["D"] * 50,
+        "value": np.concatenate(
+            [
+                np.random.normal(50, 10, 50),
+                np.random.normal(60, 15, 50),
+                np.random.normal(45, 8, 50),
+                np.random.normal(70, 20, 50),
+            ]
+        ),
+    }
+)
+
+# Custom color palette using style guide colors
+colors = ["#306998", "#FFD43B", "#DC2626", "#059669"]
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+sns.boxplot(
+    data=data,
+    x="group",
+    y="value",
+    hue="group",
+    palette=colors,
+    legend=False,
+    ax=ax,
+    linewidth=2,
+    flierprops={"marker": "o", "markersize": 8, "alpha": 0.7},
+)
+
+# Labels and styling
+ax.set_xlabel("Group", fontsize=20)
+ax.set_ylabel("Value", fontsize=20)
+ax.set_title("Basic Box Plot", fontsize=20)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, axis="y")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")