feat(pygal): implement bar-basic (#128)

## Summary
Implements `bar-basic` for **pygal** library.

- Creates a basic vertical bar chart for categorical data comparison
- Uses custom styling with subtle grid and proper colors
- Supports configurable figure size, colors, alpha, title, and axis
labels
- Handles missing values gracefully
- Y-axis starts at zero for accurate visual comparison

**Parent Issue:** #117
**Sub-Issue:** #124
**Base Branch:** `plot/bar-basic`
**Attempt:** 1/3

## Implementation
- `plots/pygal/bar/bar-basic/default.py`

## Quality Checklist
- [x] X-axis displays category labels clearly
- [x] Y-axis shows numeric scale with appropriate tick marks
- [x] Both axes are labeled
- [x] Grid lines are visible but subtle on y-axis only
- [x] Bars have consistent width and spacing
- [x] Bar colors are distinguishable from background
- [x] Title is present when specified
- [x] Y-axis starts at zero

Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>

Author: claude[bot]

plots/pygal/bar/bar-basic/default.py

@@ -0,0 +1,117 @@
+"""
+bar-basic: Basic Bar Chart
+Library: pygal
+"""
+
+import pandas as pd
+import pygal
+from pygal.style import Style
+
+
+def create_plot(
+    data: pd.DataFrame,
+    category: str,
+    value: str,
+    figsize: tuple[int, int] = (1600, 900),
+    color: str = "#3498db",
+    alpha: float = 0.8,
+    title: str | None = None,
+    xlabel: str | None = None,
+    ylabel: str | None = None,
+    rotation: int = 0,
+    **kwargs,
+) -> pygal.Bar:
+    """
+    Create a basic vertical bar chart for categorical data comparison.
+
+    Args:
+        data: Input DataFrame containing the data to plot.
+        category: Column name for category labels (x-axis).
+        value: Column name for numeric values (bar heights).
+        figsize: Figure size as (width, height) in pixels.
+        color: Bar fill color.
+        alpha: Transparency level for bars (0.0 to 1.0).
+        title: Optional plot title.
+        xlabel: X-axis label (defaults to category column name).
+        ylabel: Y-axis label (defaults to value column name).
+        rotation: Rotation angle for x-axis labels.
+        **kwargs: Additional parameters passed to pygal.Bar.
+
+    Returns:
+        pygal.Bar chart object.
+
+    Raises:
+        ValueError: If data is empty.
+        KeyError: If required columns are not found in data.
+
+    Example:
+        >>> data = pd.DataFrame({
+        ...     'category': ['A', 'B', 'C'],
+        ...     'value': [10, 20, 30]
+        ... })
+        >>> chart = create_plot(data, 'category', 'value', title='Sample Chart')
+    """
+    # Input validation
+    if data.empty:
+        raise ValueError("Data cannot be empty")
+
+    for col in [category, value]:
+        if col not in data.columns:
+            available = ", ".join(data.columns)
+            raise KeyError(f"Column '{col}' not found. Available: {available}")
+
+    # Handle missing values
+    clean_data = data[[category, value]].dropna()
+
+    # Create custom style with subtle grid and proper colors
+    custom_style = Style(
+        background="white",
+        plot_background="white",
+        foreground="#333333",
+        foreground_strong="#333333",
+        foreground_subtle="#666666",
+        opacity=str(alpha),
+        opacity_hover=str(min(alpha + 0.1, 1.0)),
+        colors=(color,),
+        guide_stroke_color="#cccccc",
+        major_guide_stroke_color="#cccccc",
+    )
+
+    # Create chart
+    chart = pygal.Bar(
+        width=figsize[0],
+        height=figsize[1],
+        title=title,
+        x_title=xlabel if xlabel else category,
+        y_title=ylabel if ylabel else value,
+        style=custom_style,
+        show_legend=False,
+        show_y_guides=True,
+        show_x_guides=False,
+        x_label_rotation=rotation,
+        print_values=False,
+        range=(0, None),
+        **kwargs,
+    )
+
+    # Set x-axis labels
+    chart.x_labels = [str(cat) for cat in clean_data[category].tolist()]
+
+    # Add data series
+    chart.add("", clean_data[value].tolist())
+
+    return chart
+
+
+if __name__ == "__main__":
+    # Sample data for testing
+    sample_data = pd.DataFrame(
+        {"category": ["Product A", "Product B", "Product C", "Product D", "Product E"], "value": [45, 78, 52, 91, 63]}
+    )
+
+    # Create plot
+    chart = create_plot(sample_data, "category", "value", title="Sales by Product")
+
+    # Save to PNG
+    chart.render_to_png("plot.png")
+    print("Plot saved to plot.png")