feat(plotnine): implement bar-basic

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

@@ -1,102 +1,35 @@
 """
 bar-basic: Basic Bar Chart
 Library: plotnine
-
-A fundamental vertical bar chart that visualizes categorical data with numeric values.
 """
 
 import pandas as pd
 from plotnine import aes, geom_bar, ggplot, labs, scale_y_continuous, theme, theme_minimal
-from plotnine.ggplot import ggplot as ggplot_type
-from plotnine.themes.elements import element_blank, element_line, element_text
-
-
-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,
-) -> ggplot_type:
-    """
-    Create a basic vertical bar chart.
-
-    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)
-        color: Bar fill color
-        edgecolor: Bar edge color
-        alpha: Transparency level for bars (0-1)
-        title: Plot title (optional)
-        xlabel: X-axis label (defaults to column name if None)
-        ylabel: Y-axis label (defaults to column name if None)
-        rotation: Rotation angle for x-axis labels
-        **kwargs: Additional parameters passed to geom_bar
-
-    Returns:
-        plotnine ggplot 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, 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}")
-
-    # Set default labels to column names if not provided
-    x_label = xlabel if xlabel is not None else category
-    y_label = ylabel if ylabel is not None else value
-
-    # Create the plot
-    plot = (
-        ggplot(data, aes(x=category, y=value))
-        + geom_bar(stat="identity", fill=color, color=edgecolor, alpha=alpha, **kwargs)
-        + labs(x=x_label, y=y_label, title=title)
-        + scale_y_continuous(expand=(0, 0, 0.05, 0))  # Start y-axis at zero
-        + theme_minimal()
-        + theme(
-            figure_size=figsize,
-            axis_text_x=element_text(rotation=rotation, ha="right" if rotation else "center"),
-            panel_grid_major_x=element_blank(),  # Remove vertical grid lines
-            panel_grid_minor=element_blank(),  # Remove minor grid lines
-            panel_grid_major_y=element_line(alpha=0.3),  # Subtle horizontal grid
-        )
+from plotnine.themes.elements import element_blank, element_line
+
+
+# Data
+data = pd.DataFrame(
+    {"category": ["Product A", "Product B", "Product C", "Product D", "Product E"], "value": [45, 78, 52, 91, 63]}
+)
+
+# Preserve category order
+data["category"] = pd.Categorical(data["category"], categories=data["category"], ordered=True)
+
+# Plot
+plot = (
+    ggplot(data, aes(x="category", y="value"))
+    + geom_bar(stat="identity", fill="#306998", width=0.7)
+    + labs(x="Category", y="Value", title="Basic Bar Chart")
+    + scale_y_continuous(expand=(0, 0, 0.05, 0))
+    + theme_minimal(base_size=16)
+    + theme(
+        figure_size=(16, 9),
+        panel_grid_major_x=element_blank(),
+        panel_grid_minor=element_blank(),
+        panel_grid_major_y=element_line(alpha=0.3),
     )
+)
 
-    return plot
-
-
-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")
-
-    # Save
-    fig.save("plot.png", dpi=300)
-    print("Plot saved to plot.png")
+# Save
+plot.save("plot.png", dpi=300)