feat(matplotlib): implement bar-basic

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

@@ -5,110 +5,25 @@
 
 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.
+# Data
+data = pd.DataFrame(
+    {"category": ["Product A", "Product B", "Product C", "Product D", "Product E"], "value": [45, 78, 52, 91, 63]}
+)
 
-    A fundamental bar chart that visualizes categorical data with numeric values,
-    ideal for comparing quantities across discrete categories.
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+ax.bar(data["category"], data["value"], color="#306998", edgecolor="white", linewidth=1)
 
-    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().
+# Labels and styling
+ax.set_xlabel("Category", fontsize=20)
+ax.set_ylabel("Value", fontsize=20)
+ax.set_title("Basic Bar Chart", fontsize=20)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(axis="y", alpha=0.3)
+ax.set_axisbelow(True)
+ax.set_ylim(bottom=0)
 
-    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")
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")