feat(seaborn): implement area-basic

Adds a basic area chart implementation for seaborn library.

- Creates spec file at specs/area-basic.md
- Implements fill_between-based area chart with seaborn styling
- Supports customizable fill transparency, colors, and labels
- Includes proper input validation and type hints

Note: Seaborn does not have a native area chart function,
so this uses matplotlib's fill_between with seaborn's styling.

Author: github-actions[bot]

plots/seaborn/fill_between/area-basic/default.py

@@ -0,0 +1,123 @@
+"""
+area-basic: Basic Area Chart
+Implementation for: seaborn
+Variant: default
+Python: 3.10+
+
+Note: Seaborn does not have a native area chart function. This implementation
+uses matplotlib's fill_between with seaborn's styling for a consistent look.
+"""
+
+from typing import TYPE_CHECKING, Optional
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+
+if TYPE_CHECKING:
+    from matplotlib.figure import Figure
+
+
+def create_plot(
+    data: pd.DataFrame,
+    x: str,
+    y: str,
+    color: str = "steelblue",
+    alpha: float = 0.4,
+    line_alpha: float = 1.0,
+    title: Optional[str] = None,
+    xlabel: Optional[str] = None,
+    ylabel: Optional[str] = None,
+    figsize: tuple[float, float] = (16, 9),
+    **kwargs,
+) -> "Figure":
+    """
+    Create a basic filled area chart using seaborn styling with matplotlib.
+
+    Args:
+        data: Input DataFrame with required columns
+        x: Column name for x-axis values (sequential: datetime, numeric, or categorical)
+        y: Column name for y-axis values (numeric)
+        color: Fill and line color (default: "steelblue")
+        alpha: Transparency level for area fill 0.0-1.0 (default: 0.4)
+        line_alpha: Transparency level for edge line 0.0-1.0 (default: 1.0)
+        title: Plot title (default: None)
+        xlabel: Custom x-axis label (default: column name)
+        ylabel: Custom y-axis label (default: column name)
+        figsize: Figure size as (width, height) (default: (16, 9))
+        **kwargs: Additional parameters passed to fill_between()
+
+    Returns:
+        Matplotlib Figure object
+
+    Raises:
+        ValueError: If data is empty
+        KeyError: If required columns not found
+
+    Example:
+        >>> data = pd.DataFrame({'Month': range(1, 13), 'Revenue': [10, 15, 13, 17, 20, 25, 22, 26, 24, 28, 30, 35]})
+        >>> fig = create_plot(data, x='Month', y='Revenue')
+    """
+    # 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: {available}")
+
+    # Set seaborn style for consistent appearance
+    sns.set_theme(style="whitegrid")
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=figsize)
+
+    # Get x and y values
+    x_vals = data[x]
+    y_vals = data[y]
+
+    # Plot the edge line first
+    ax.plot(x_vals, y_vals, color=color, alpha=line_alpha, linewidth=2)
+
+    # Fill the area between the line and baseline (y=0)
+    ax.fill_between(x_vals, y_vals, alpha=alpha, color=color, **kwargs)
+
+    # Apply styling
+    ax.set_xlabel(xlabel or x, fontsize=12)
+    ax.set_ylabel(ylabel or y, fontsize=12)
+    ax.grid(True, alpha=0.3, linestyle="--")
+
+    # Set y-axis to start from 0 for area charts (standard practice)
+    y_min = min(0, y_vals.min())
+    y_max = y_vals.max()
+    y_padding = (y_max - y_min) * 0.05
+    ax.set_ylim(y_min, y_max + y_padding)
+
+    # Title
+    if title:
+        ax.set_title(title, fontsize=14, fontweight="bold")
+
+    # Tight layout to avoid label clipping
+    plt.tight_layout()
+
+    return fig
+
+
+if __name__ == "__main__":
+    # Sample data for testing - monthly revenue over a year
+    data = pd.DataFrame(
+        {
+            "Month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"],
+            "Revenue": [12000, 15000, 13500, 17000, 20000, 25000, 22000, 26000, 24000, 28000, 30000, 35000],
+        }
+    )
+
+    # Create plot
+    fig = create_plot(data, x="Month", y="Revenue", title="Monthly Revenue Growth", ylabel="Revenue ($)")
+
+    # Save for inspection
+    plt.savefig("plot.png", dpi=300, bbox_inches="tight")
+    print("Plot saved to plot.png")

specs/area-basic.md

@@ -10,21 +10,20 @@ Last Updated: 2025-12-01
 
 ## Description
 
-Create a simple area chart showing a filled region between the x-axis and a line.
-Perfect for visualizing quantities over continuous intervals, trends over time, or cumulative values.
-Works with any dataset containing a continuous x variable and numeric y values.
+Create a simple filled area chart showing a single data series over time or sequential x-values.
+Perfect for visualizing cumulative values, trends with emphasis on magnitude, and comparing values to a baseline.
+Works with any dataset containing sequential x-values and numeric y-values.
 
 ## Data Requirements
 
-- **x**: Values for the x-axis (numeric or datetime: continuous sequence)
-- **y**: Numeric values defining the top of the filled area (numeric: continuous values)
+- **x**: Sequential values for x-axis (datetime, numeric, or categorical)
+- **y**: Numeric values representing the area height at each x point
 
 ## Optional Parameters
 
 - `color`: Fill color for the area (type: string, default: "steelblue")
-- `alpha`: Transparency level for the filled area (type: float 0.0-1.0, default: 0.4)
-- `line_color`: Color of the top edge line (type: string, default: same as fill color)
-- `line_width`: Width of the top edge line (type: float, default: 2.0)
+- `alpha`: Transparency level for fill (type: float 0.0-1.0, default: 0.4)
+- `line_alpha`: Transparency level for edge line (type: float 0.0-1.0, default: 1.0)
 - `title`: Plot title (type: string, default: None)
 - `xlabel`: Custom x-axis label (type: string, default: column name)
 - `ylabel`: Custom y-axis label (type: string, default: column name)
@@ -34,30 +33,30 @@ Works with any dataset containing a continuous x variable and numeric y values.
 
 - [ ] X and Y axes are labeled with column names (or custom labels if provided)
 - [ ] Grid is visible but subtle with alpha=0.3
-- [ ] Filled area is clearly visible with appropriate transparency
-- [ ] Top edge line is visible and distinguishes the data boundary
+- [ ] Area fill is clearly visible with appropriate transparency (alpha ~0.4)
+- [ ] Edge line is visible to define the data boundary
 - [ ] No overlapping axis labels or tick marks
-- [ ] Colorblind-safe colors (use accessible defaults)
-- [ ] Appropriate figure size (16x9 default) for readability
-- [ ] Title is centered and clearly readable if provided (fontsize 14, bold)
+- [ ] Colorblind-safe colors used
+- [ ] Appropriate figure size (16x9 aspect ratio) for readability
+- [ ] Title is centered and clearly readable if provided
 
 ## Expected Output
 
-A clean area chart with a filled region extending from the x-axis up to the data values.
-The filled area should have moderate transparency to show grid lines underneath while remaining clearly visible.
-A solid line along the top edge should clearly define where the data values lie.
+A clean area chart with a filled region between the data line and the x-axis baseline.
+The fill should be semi-transparent to allow grid lines to show through while still emphasizing magnitude.
+A solid edge line should clearly define the top boundary of the data.
 The plot should be immediately understandable without additional explanation.
-All text elements (labels, title, and tick labels) should be legible at standard display sizes.
+All text elements should be legible at standard display sizes.
 
 ## Tags
 
-trend, continuous, basic, area, time-series, statistical, exploratory
+area, trend, time-series, basic, magnitude, cumulative, exploratory
 
 ## Use Cases
 
-- Stock price visualization showing value over time (e.g., daily closing prices)
-- Website traffic metrics over time (e.g., daily page views)
-- Temperature variations throughout a day or season
-- Revenue or sales trends over months or years
-- Resource utilization over time (e.g., CPU usage, memory consumption)
-- Population growth over time periods
+- Visualizing cumulative values over time (e.g., total revenue growth)
+- Showing trends with emphasis on magnitude (e.g., stock prices)
+- Comparing values to a baseline (e.g., temperature above/below average)
+- Website traffic visualization over time
+- Resource utilization monitoring (CPU, memory usage)
+- Population or growth trends