feat: implement scatter-basic-007

plots/matplotlib/bar/scatter-basic-007/default.py

@@ -0,0 +1,208 @@
+"""
+scatter-basic-007: Financial Categories Bar Chart
+Implementation for: matplotlib
+Variant: default
+Python: 3.10+
+"""
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+from typing import Optional, Union, List
+from matplotlib.figure import Figure
+
+
+def create_plot(
+    data: pd.DataFrame,
+    categories: str,
+    values: str,
+    color: Optional[Union[str, List[str]]] = None,
+    currency: str = "$",
+    title: Optional[str] = None,
+    xlabel: Optional[str] = None,
+    ylabel: Optional[str] = None,
+    orientation: str = "vertical",
+    show_values: bool = True,
+    **kwargs
+) -> Figure:
+    """
+    Create a bar chart displaying financial categories with their corresponding values.
+
+    This visualization is designed to show categorical financial data such as expense categories,
+    revenue streams, budget allocations, or portfolio distributions. The chart provides clear
+    visual comparison between different financial categories.
+
+    Args:
+        data: Input DataFrame with required columns
+        categories: Column name containing category labels
+        values: Column name containing monetary values
+        color: Bar color or list of colors (default: "steelblue")
+        currency: Currency symbol to display (default: "$")
+        title: Chart title (default: "Financial Categories")
+        xlabel: Custom x-axis label (default: "Categories")
+        ylabel: Custom y-axis label (default: "Amount")
+        orientation: Bar orientation ("vertical" or "horizontal", default: "vertical")
+        show_values: Display values on bars (default: True)
+        **kwargs: Additional parameters passed to bar plotting function
+
+    Returns:
+        Matplotlib Figure object
+
+    Raises:
+        ValueError: If data is empty
+        KeyError: If required columns not found in data
+        ValueError: If orientation is not "vertical" or "horizontal"
+
+    Example:
+        >>> data = pd.DataFrame({
+        ...     'Category': ['Rent', 'Food', 'Transport', 'Entertainment'],
+        ...     'Amount': [1500, 800, 300, 200]
+        ... })
+        >>> fig = create_plot(data, 'Category', 'Amount')
+    """
+    # Input validation
+    if data.empty:
+        raise ValueError("Data cannot be empty")
+
+    # Check required columns
+    required_columns = [categories, values]
+    for col in required_columns:
+        if col not in data.columns:
+            available = ", ".join(data.columns)
+            raise KeyError(f"Column '{col}' not found. Available columns: {available}")
+
+    # Validate orientation
+    if orientation not in ["vertical", "horizontal"]:
+        raise ValueError(f"Orientation must be 'vertical' or 'horizontal', not '{orientation}'")
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=(10, 6))
+
+    # Prepare data
+    x_data = data[categories].astype(str)
+    y_data = data[values]
+
+    # Handle colors
+    if color is None:
+        # Use different colors for positive and negative values
+        colors = ['#ff4444' if v < 0 else '#4682b4' for v in y_data]
+    elif isinstance(color, list):
+        colors = color
+    else:
+        colors = color
+
+    # Plot bars based on orientation
+    if orientation == "vertical":
+        bars = ax.bar(x_data, y_data, color=colors, **kwargs)
+
+        # Rotate x labels if there are many categories
+        if len(x_data) > 10:
+            plt.xticks(rotation=45, ha='right')
+        elif len(x_data) > 7:
+            plt.xticks(rotation=30, ha='right')
+    else:  # horizontal
+        bars = ax.barh(x_data, y_data, color=colors, **kwargs)
+
+    # Add value labels on bars if requested
+    if show_values:
+        for bar, value in zip(bars, y_data):
+            # Format value with currency and appropriate scale
+            if abs(value) >= 1_000_000:
+                label = f"{currency}{value/1_000_000:.1f}M"
+            elif abs(value) >= 1_000:
+                label = f"{currency}{value/1_000:.1f}K"
+            else:
+                label = f"{currency}{value:,.0f}"
+
+            if orientation == "vertical":
+                # Position label above positive bars, below negative bars
+                if value >= 0:
+                    va = 'bottom'
+                    y_pos = value
+                else:
+                    va = 'top'
+                    y_pos = value
+                ax.text(bar.get_x() + bar.get_width()/2, y_pos, label,
+                       ha='center', va=va, fontsize=9, fontweight='bold')
+            else:  # horizontal
+                # Position label to the right of positive bars, left of negative bars
+                if value >= 0:
+                    ha = 'left'
+                    x_pos = value
+                else:
+                    ha = 'right'
+                    x_pos = value
+                ax.text(x_pos, bar.get_y() + bar.get_height()/2, label,
+                       ha=ha, va='center', fontsize=9, fontweight='bold')
+
+    # Apply styling
+    if orientation == "vertical":
+        ax.set_xlabel(xlabel or "Categories", fontsize=11)
+        ax.set_ylabel(ylabel or "Amount", fontsize=11)
+        # Add horizontal grid for vertical bars
+        ax.grid(True, axis='y', alpha=0.3, linestyle='--')
+        ax.set_axisbelow(True)
+
+        # Format y-axis with currency
+        ax.yaxis.set_major_formatter(plt.FuncFormatter(
+            lambda x, p: f'{currency}{x:,.0f}' if abs(x) < 1000 else
+                        f'{currency}{x/1000:.0f}K' if abs(x) < 1000000 else
+                        f'{currency}{x/1000000:.1f}M'))
+    else:  # horizontal
+        ax.set_xlabel(xlabel or "Amount", fontsize=11)
+        ax.set_ylabel(ylabel or "Categories", fontsize=11)
+        # Add vertical grid for horizontal bars
+        ax.grid(True, axis='x', alpha=0.3, linestyle='--')
+        ax.set_axisbelow(True)
+
+        # Format x-axis with currency
+        ax.xaxis.set_major_formatter(plt.FuncFormatter(
+            lambda x, p: f'{currency}{x:,.0f}' if abs(x) < 1000 else
+                        f'{currency}{x/1000:.0f}K' if abs(x) < 1000000 else
+                        f'{currency}{x/1000000:.1f}M'))
+
+    # Add title
+    if title:
+        ax.set_title(title, fontsize=14, fontweight='bold', pad=20)
+    else:
+        ax.set_title("Financial Categories", fontsize=14, fontweight='bold', pad=20)
+
+    # Add a subtle line at y=0 for reference
+    if orientation == "vertical":
+        ax.axhline(y=0, color='black', linewidth=0.5, alpha=0.3)
+    else:
+        ax.axvline(x=0, color='black', linewidth=0.5, alpha=0.3)
+
+    # Adjust layout to prevent label cutoff
+    plt.tight_layout()
+
+    return fig
+
+
+if __name__ == '__main__':
+    # Sample data for testing
+    np.random.seed(42)
+
+    # Create sample financial data
+    categories = ['Rent', 'Food', 'Transport', 'Entertainment', 'Utilities',
+                  'Insurance', 'Savings', 'Healthcare', 'Education', 'Misc']
+    amounts = [1500, 800, 300, 200, 250, 180, 500, 150, 100, 120]
+
+    data = pd.DataFrame({
+        'Category': categories,
+        'Amount': amounts
+    })
+
+    # Create plot
+    fig = create_plot(
+        data,
+        categories='Category',
+        values='Amount',
+        title='Monthly Expense Breakdown',
+        ylabel='Amount (USD)'
+    )
+
+    # Save for inspection
+    plt.savefig('test_output.png', dpi=150, bbox_inches='tight')
+    print("Plot saved to test_output.png")
+    print(f"Total expenses: ${sum(amounts):,.2f}")