feat(matplotlib): implement scatter-color-groups

plots/matplotlib/scatter/scatter-color-groups/default.py

@@ -1,135 +1,59 @@
 """
 scatter-color-groups: Scatter Plot with Color Groups
-Implementation for: matplotlib
-Variant: default
-Python: 3.10+
+Library: matplotlib
 """
 
-from typing import TYPE_CHECKING
-
 import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
 
 
-if TYPE_CHECKING:
-    from matplotlib.figure import Figure
-
-
-def create_plot(
-    data: pd.DataFrame,
-    x: str,
-    y: str,
-    group: str,
-    figsize: tuple[float, float] = (16, 9),
-    alpha: float = 0.7,
-    size: float = 50,
-    title: str | None = None,
-    xlabel: str | None = None,
-    ylabel: str | None = None,
-    palette: str = "Set1",
-    **kwargs,
-) -> "Figure":
-    """
-    Create a scatter plot with points colored by categorical groups.
-
-    Visualizes data points in a 2D x-y space with distinct colors for each
-    categorical group, showing separate "color clouds" for different categories.
-
-    Args:
-        data: Input DataFrame with required columns
-        x: Column name for x-axis values
-        y: Column name for y-axis values
-        group: Column name for categorical grouping and coloring
-        figsize: Figure size as (width, height) tuple (default: (16, 9))
-        alpha: Transparency level for points (default: 0.7)
-        size: Point size (default: 50)
-        title: Plot title (default: None)
-        xlabel: Custom x-axis label (default: uses column name)
-        ylabel: Custom y-axis label (default: uses column name)
-        palette: Matplotlib colormap or seaborn palette name (default: "Set1")
-        **kwargs: Additional parameters passed to scatter plot
-
-    Returns:
-        Matplotlib Figure object
-
-    Raises:
-        ValueError: If data is empty
-        KeyError: If required columns not found in data
-
-    Example:
-        >>> import pandas as pd
-        >>> data = pd.DataFrame({
-        ...     'x': [1, 2, 3, 4, 5, 6],
-        ...     'y': [2, 4, 3, 5, 6, 4],
-        ...     'group': ['A', 'A', 'B', 'B', 'C', 'C']
-        ... })
-        >>> fig = create_plot(data, 'x', 'y', 'group')
-        >>> plt.savefig('plot.png')
-    """
-    # Input validation
-    if data.empty:
-        raise ValueError("Data cannot be empty")
-
-    # Check required columns
-    required_cols = [x, y, group]
-    for col in required_cols:
-        if col not in data.columns:
-            available = ", ".join(data.columns)
-            raise KeyError(f"Column '{col}' not found in data. Available columns: {available}")
-
-    # Create figure and axis
-    fig, ax = plt.subplots(figsize=figsize)
-
-    # Get unique groups and create color mapping
-    groups = data[group].unique()
-
-    # Get colors from palette
-    try:
-        cmap = plt.get_cmap(palette)
-        colors = [cmap(i / max(len(groups) - 1, 1)) for i in range(len(groups))]
-    except (ValueError, AttributeError):
-        # Fallback to tab10 if palette not found
-        cmap = plt.get_cmap("tab10")
-        colors = [cmap(i % 10) for i in range(len(groups))]
-
-    # Plot each group with a different color
-    for idx, group_val in enumerate(groups):
-        group_data = data[data[group] == group_val]
-        ax.scatter(group_data[x], group_data[y], label=str(group_val), alpha=alpha, s=size, color=colors[idx], **kwargs)
-
-    # Set labels
-    ax.set_xlabel(xlabel or x, fontsize=11)
-    ax.set_ylabel(ylabel or y, fontsize=11)
-
-    # Add title if provided
-    if title:
-        ax.set_title(title, fontsize=12, fontweight="bold", pad=15)
-
-    # Add legend
-    ax.legend(title=group, loc="best", framealpha=0.9)
-
-    # Add subtle grid
-    ax.grid(True, alpha=0.3, linestyle="--")
-
-    # Layout
-    plt.tight_layout()
-
-    return fig
-
-
-if __name__ == "__main__":
-    # Sample data for testing
-    data = pd.DataFrame(
-        {
-            "x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5],
-            "y": [2, 4, 3, 5, 6, 4, 7, 8, 9, 10, 3, 5, 4, 6, 7, 5],
-            "group": ["A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "C", "C", "C", "C", "C", "C"],
-        }
+# Data - Iris-like dataset
+np.random.seed(42)
+n_per_group = 50
+
+data = pd.DataFrame({
+    "sepal_length": np.concatenate([
+        np.random.normal(5.0, 0.35, n_per_group),
+        np.random.normal(5.9, 0.50, n_per_group),
+        np.random.normal(6.6, 0.60, n_per_group),
+    ]),
+    "sepal_width": np.concatenate([
+        np.random.normal(3.4, 0.38, n_per_group),
+        np.random.normal(2.8, 0.30, n_per_group),
+        np.random.normal(3.0, 0.30, n_per_group),
+    ]),
+    "species": ["setosa"] * n_per_group + ["versicolor"] * n_per_group + ["virginica"] * n_per_group,
+})
+
+# Color palette (colorblind safe from style guide)
+colors = ["#306998", "#FFD43B", "#DC2626"]
+species = data["species"].unique()
+color_map = {sp: colors[i] for i, sp in enumerate(species)}
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+for species_name in species:
+    subset = data[data["species"] == species_name]
+    ax.scatter(
+        subset["sepal_length"],
+        subset["sepal_width"],
+        c=color_map[species_name],
+        label=species_name.capitalize(),
+        alpha=0.7,
+        s=80,
+        edgecolors="white",
+        linewidths=0.5,
     )
 
-    # Create plot
-    fig = create_plot(data, "x", "y", "group", title="Scatter Plot with Color Groups")
+# Labels and styling
+ax.set_xlabel("Sepal Length (cm)", fontsize=20)
+ax.set_ylabel("Sepal Width (cm)", fontsize=20)
+ax.set_title("Iris Species by Sepal Dimensions", fontsize=20)
+ax.tick_params(axis="both", labelsize=16)
+ax.legend(title="Species", fontsize=16, title_fontsize=16)
+ax.grid(True, alpha=0.3)
 
-    # Save for inspection
-    plt.savefig("plot.png", dpi=300, bbox_inches="tight")
-    print("Plot saved to plot.png")
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")