diff --git a/plots/matplotlib/scatter/scatter-basic/default.py b/plots/matplotlib/scatter/scatter-basic/default.py
index 6be51a12f3..5efb7e95ae 100644
--- a/plots/matplotlib/scatter/scatter-basic/default.py
+++ b/plots/matplotlib/scatter/scatter-basic/default.py
@@ -1,119 +1,27 @@
 """
 scatter-basic: Basic Scatter Plot
-Implementation for: matplotlib
-Variant: default
-Python: 3.10+
+Library: matplotlib
 """
 
-from typing import TYPE_CHECKING, Optional
-
 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,
-    figsize: tuple[float, float] = (16, 9),
-    alpha: float = 0.6,
-    size: float = 30,
-    color: str = "steelblue",
-    title: Optional[str] = None,
-    xlabel: Optional[str] = None,
-    ylabel: Optional[str] = None,
-    edgecolors: Optional[str] = None,
-    linewidth: float = 0,
-    **kwargs,
-) -> "Figure":
-    """
-    Create a basic scatter plot visualizing the relationship between two continuous variables.
-
-    Args:
-        data: Input DataFrame with required columns
-        x: Column name for x-axis values
-        y: Column name for y-axis values
-        figsize: Figure size as (width, height) tuple (default: (16, 9))
-        alpha: Transparency level for points (default: 0.6 for better visibility with many points)
-        size: Point size (default: 30)
-        color: Point color (default: "steelblue")
-        title: Plot title (default: None)
-        xlabel: X-axis label (default: uses column name)
-        ylabel: Y-axis label (default: uses column name)
-        edgecolors: Edge color for points (default: None)
-        linewidth: Width of edge lines (default: 0)
-        **kwargs: Additional parameters passed to scatter function
-
-    Returns:
-        Matplotlib Figure object
-
-    Raises:
-        ValueError: If data is empty
-        KeyError: If required columns not found
-        TypeError: If x or y columns contain non-numeric data
-
-    Example:
-        >>> data = pd.DataFrame({'x': [1, 2, 3], 'y': [2, 4, 3]})
-        >>> fig = create_plot(data, 'x', 'y')
-    """
-    # Input validation
-    if data.empty:
-        raise ValueError("Data cannot be empty")
-
-    # Check required columns
-    for col in [x, y]:
-        if col not in data.columns:
-            available = ", ".join(data.columns)
-            raise KeyError(f"Column '{col}' not found. Available columns: {available}")
-
-    # Check if columns are numeric
-    if not pd.api.types.is_numeric_dtype(data[x]):
-        raise TypeError(f"Column '{x}' must contain numeric data")
-    if not pd.api.types.is_numeric_dtype(data[y]):
-        raise TypeError(f"Column '{y}' must contain numeric data")
-
-    # Create figure
-    fig, ax = plt.subplots(figsize=figsize)
-
-    # Plot data
-    ax.scatter(data[x], data[y], s=size, alpha=alpha, c=color, edgecolors=edgecolors, linewidth=linewidth, **kwargs)
-
-    # Labels and title
-    ax.set_xlabel(xlabel or x)
-    ax.set_ylabel(ylabel or y)
-
-    if title:
-        ax.set_title(title)
-
-    # Apply styling
-    ax.grid(True, alpha=0.3)
-
-    # Layout
-    plt.tight_layout()
-
-    return fig
 
 
-if __name__ == "__main__":
-    # Sample data for testing - many points to demonstrate basic scatter
-    np.random.seed(42)
-    n_points = 500
+# Data
+np.random.seed(42)
+x = np.random.randn(100) * 2 + 5
+y = x * 0.8 + np.random.randn(100) * 1.5
 
-    data = pd.DataFrame(
-        {
-            "x": np.random.randn(n_points) * 2 + 10,
-            "y": np.random.randn(n_points) * 3 + 15 + np.random.randn(n_points) * 0.5,
-        }
-    )
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+ax.scatter(x, y, alpha=0.7, s=50, color="#306998")
 
-    # Create plot
-    fig = create_plot(data, "x", "y", title="Basic Scatter Plot Example", xlabel="X Value", ylabel="Y Value")
+# Labels and styling
+ax.set_xlabel("X Value", fontsize=20)
+ax.set_ylabel("Y Value", fontsize=20)
+ax.set_title("Basic Scatter Plot", fontsize=20)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3)
 
-    # Save for inspection - ALWAYS use 'plot.png' as filename
-    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")