feat(bokeh): implement bar-basic

Simplified implementation following KISS principles:
- Sequential script structure without functions/classes
- 4800x2700px output with proper typography sizing
- Uses ColumnDataSource and vbar glyph method
- Python Blue (#306998) color from style guide

Author: github-actions[bot]

plots/bokeh/vbar/bar-basic/default.py

@@ -3,150 +3,42 @@
 Library: bokeh
 """
 
-from typing import TYPE_CHECKING
-
 import pandas as pd
 from bokeh.io import export_png
 from bokeh.models import ColumnDataSource
 from bokeh.plotting import figure
 
 
-if TYPE_CHECKING:
-    from bokeh.plotting import figure as Figure
-
-
-def create_plot(
-    data: pd.DataFrame,
-    category: str,
-    value: str,
-    figsize: tuple[int, int] = (1600, 900),
-    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.
-
-    A fundamental bar chart that visualizes categorical data with numeric values,
-    ideal for comparing quantities across discrete categories.
-
-    Args:
-        data: Input DataFrame with categorical and numeric columns
-        category: Column name for category labels (x-axis)
-        value: Column name for numeric values (bar heights)
-        figsize: Figure size as (width, height) in pixels. Defaults to (1600, 900).
-        color: Bar fill color. Defaults to "steelblue".
-        edgecolor: Bar edge color. Defaults to "black".
-        alpha: Transparency level for bars (0-1). Defaults to 0.8.
-        title: Plot title. Defaults to None.
-        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 in degrees. Defaults to 0.
-        **kwargs: Additional parameters passed to figure.
-
-    Returns:
-        Bokeh figure object with the bar chart.
-
-    Raises:
-        ValueError: If data is empty.
-        KeyError: If required columns are not found in data.
-
-    Example:
-        >>> data = pd.DataFrame({
-        ...     'category': ['A', 'B', 'C', 'D'],
-        ...     'value': [10, 25, 15, 30]
-        ... })
-        >>> fig = create_plot(data, 'category', 'value', title='Sample Bar 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.tolist())
-            raise KeyError(f"Column '{col}' not found. Available: {available}")
-
-    # Prepare data - drop NaN values
-    plot_data = data[[category, value]].dropna()
-
-    # Get categories as list for x_range
-    categories = plot_data[category].astype(str).tolist()
-
-    # Create ColumnDataSource
-    source = ColumnDataSource(data={"categories": categories, "values": plot_data[value].tolist()})
-
-    # Set labels
-    x_label = xlabel if xlabel is not None else category
-    y_label = ylabel if ylabel is not None else value
-
-    # Create figure with categorical x-axis
-    p = figure(
-        width=figsize[0],
-        height=figsize[1],
-        x_range=categories,
-        title=title,
-        x_axis_label=x_label,
-        y_axis_label=y_label,
-        **kwargs,
-    )
-
-    # Calculate bar width (0.8 of available space)
-    bar_width = 0.8
-
-    # Add bars
-    p.vbar(
-        x="categories",
-        top="values",
-        width=bar_width,
-        source=source,
-        fill_color=color,
-        fill_alpha=alpha,
-        line_color=edgecolor,
-        line_width=1,
-    )
-
-    # Ensure y-axis starts at zero
-    p.y_range.start = 0
-
-    # Style grid - subtle y-grid only
-    p.xgrid.grid_line_color = None
-    p.ygrid.grid_line_alpha = 0.3
-
-    # Apply x-axis label rotation if specified
-    if rotation != 0:
-        from math import pi
-
-        p.xaxis.major_label_orientation = rotation * pi / 180
-
-    # Style axis labels
-    p.xaxis.axis_label_text_font_size = "12pt"
-    p.yaxis.axis_label_text_font_size = "12pt"
-    p.xaxis.major_label_text_font_size = "10pt"
-    p.yaxis.major_label_text_font_size = "10pt"
-
-    # Style title if present
-    if title:
-        p.title.text_font_size = "14pt"
-        p.title.align = "center"
-
-    return p
-
-
-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
-    export_png(fig, filename="plot.png")
-    print("Plot saved to plot.png")
+# Data
+data = pd.DataFrame(
+    {"category": ["Product A", "Product B", "Product C", "Product D", "Product E"], "value": [45, 78, 52, 91, 63]}
+)
+
+# Create ColumnDataSource
+source = ColumnDataSource(data)
+
+# Create figure with categorical x-axis
+p = figure(
+    width=4800,
+    height=2700,
+    x_range=data["category"].tolist(),
+    title="Basic Bar Chart",
+    x_axis_label="Category",
+    y_axis_label="Value",
+)
+
+# Plot vertical bars
+p.vbar(x="category", top="value", source=source, width=0.7, color="#306998", alpha=0.8)
+
+# Styling
+p.title.text_font_size = "20pt"
+p.xaxis.axis_label_text_font_size = "20pt"
+p.yaxis.axis_label_text_font_size = "20pt"
+p.xaxis.major_label_text_font_size = "16pt"
+p.yaxis.major_label_text_font_size = "16pt"
+p.xgrid.grid_line_color = None
+p.ygrid.grid_line_alpha = 0.3
+p.y_range.start = 0
+
+# Save
+export_png(p, filename="plot.png")