feat(matplotlib): implement bar-basic (#126)

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

**Parent Issue:** #117
**Sub-Issue:** #118
**Base Branch:** `plot/bar-basic`
**Attempt:** 1/3

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

## Features
- Vertical bar chart with customizable colors and transparency
- Axis labels with sensible defaults (uses column names when not
specified)
- Subtle y-axis grid (alpha=0.3) for readability
- Rotatable x-axis labels for long category names
- Y-axis always starts at zero for accurate visual comparison
- Full input validation with descriptive error messages
- Google-style docstrings with type hints

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

Author: claude[bot]

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

@@ -0,0 +1,114 @@
+"""
+bar-basic: Basic Bar Chart
+Library: matplotlib
+"""
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from matplotlib.figure import Figure
+
+
+def create_plot(
+    data: pd.DataFrame,
+    category: str,
+    value: str,
+    figsize: tuple[float, float] = (10, 6),
+    color: str = "steelblue",
+    edgecolor: str = "black",
+    alpha: float = 0.8,
+    title: str | None = None,
+    xlabel: str | None = None,
+    ylabel: str | None = None,
+    rotation: int = 0,
+    **kwargs,
+) -> Figure:
+    """
+    Create a basic vertical bar chart from a DataFrame.
+
+    A fundamental bar chart that visualizes categorical data with numeric values,
+    ideal for comparing quantities across discrete categories.
+
+    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 inches.
+        color: Bar fill color.
+        edgecolor: Bar edge color.
+        alpha: Transparency level for bars (0.0 to 1.0).
+        title: Optional plot title.
+        xlabel: X-axis label. Defaults to category column name if None.
+        ylabel: Y-axis label. Defaults to value column name if None.
+        rotation: Rotation angle for x-axis labels in degrees.
+        **kwargs: Additional keyword arguments passed to ax.bar().
+
+    Returns:
+        Matplotlib Figure object containing the bar chart.
+
+    Raises:
+        ValueError: If data is empty.
+        KeyError: If required columns are not found in the DataFrame.
+
+    Example:
+        >>> data = pd.DataFrame({
+        ...     'category': ['A', 'B', 'C'],
+        ...     'value': [10, 20, 15]
+        ... })
+        >>> fig = 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}")
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=figsize)
+
+    # Extract data
+    categories = data[category]
+    values = data[value]
+
+    # Plot bars
+    ax.bar(categories, values, color=color, edgecolor=edgecolor, alpha=alpha, **kwargs)
+
+    # Set axis labels
+    ax.set_xlabel(xlabel if xlabel is not None else category)
+    ax.set_ylabel(ylabel if ylabel is not None else value)
+
+    # Set title if provided
+    if title is not None:
+        ax.set_title(title)
+
+    # Add subtle grid on y-axis only
+    ax.yaxis.grid(True, alpha=0.3)
+    ax.set_axisbelow(True)
+
+    # Rotate x-axis labels if specified
+    if rotation != 0:
+        plt.xticks(rotation=rotation, ha="right" if rotation > 0 else "left")
+
+    # Ensure y-axis starts at zero
+    ax.set_ylim(bottom=0)
+
+    # Adjust layout
+    plt.tight_layout()
+
+    return fig
+
+
+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
+    fig = create_plot(sample_data, "category", "value", title="Sales by Product", xlabel="Product", ylabel="Sales ($)")
+
+    # Save
+    plt.savefig("plot.png", dpi=300, bbox_inches="tight")
+    print("Plot saved to plot.png")