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skills/uv/assets/script_examples/data_analysis.py

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+#!/usr/bin/env -S uv --quiet run --active --script
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#   "pandas>=2.0.0",
+#   "matplotlib>=3.7.0",
+#   "rich>=13.0.0",
+# ]
+# [tool.uv]
+# exclude-newer = "2025-10-24T00:00:00Z"  # Time-based reproducibility
+# ///
+
+"""Data Analysis Script Example
+
+This script demonstrates PEP 723 inline script metadata with uv.
+It analyzes CSV data and creates visualizations.
+
+Usage:
+    # Make executable and run
+    chmod +x data_analysis.py
+    ./data_analysis.py data.csv
+
+    # Or run directly with uv
+    uv run data_analysis.py data.csv
+
+    # With additional dependencies
+    uv run --with seaborn data_analysis.py data.csv
+"""
+
+import sys
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from rich.console import Console
+from rich.table import Table
+
+console = Console()
+
+
+def analyze_data(csv_path: str) -> None:
+    """Analyze CSV data and display summary statistics."""
+    # Load data
+    df = pd.read_csv(csv_path)
+
+    console.print(f"[bold green]Analyzing {csv_path}[/bold green]")
+    console.print(f"Shape: {df.shape[0]} rows x {df.shape[1]} columns\n")
+
+    # Display summary statistics in rich table
+    table = Table(title="Summary Statistics")
+    table.add_column("Column", style="cyan")
+    table.add_column("Mean", style="magenta")
+    table.add_column("Std", style="green")
+    table.add_column("Min", style="yellow")
+    table.add_column("Max", style="red")
+
+    for col in df.select_dtypes(include=["number"]).columns:
+        table.add_row(
+            col, f"{df[col].mean():.2f}", f"{df[col].std():.2f}", f"{df[col].min():.2f}", f"{df[col].max():.2f}"
+        )
+
+    console.print(table)
+
+    # Create visualization
+    _fig, axes = plt.subplots(1, 2, figsize=(12, 5))
+
+    # Plot 1: Distribution of first numeric column
+    numeric_cols = df.select_dtypes(include=["number"]).columns
+    if len(numeric_cols) > 0:
+        df[numeric_cols[0]].hist(ax=axes[0], bins=30)
+        axes[0].set_title(f"Distribution of {numeric_cols[0]}")
+        axes[0].set_xlabel(numeric_cols[0])
+        axes[0].set_ylabel("Frequency")
+
+    # Plot 2: Correlation heatmap
+    if len(numeric_cols) > 1:
+        corr = df.loc[:, numeric_cols].corr()
+        im = axes[1].imshow(corr, cmap="coolwarm", aspect="auto")
+        axes[1].set_xticks(range(len(numeric_cols)))
+        axes[1].set_yticks(range(len(numeric_cols)))
+        axes[1].set_xticklabels(numeric_cols, rotation=45)
+        axes[1].set_yticklabels(numeric_cols)
+        axes[1].set_title("Correlation Matrix")
+        plt.colorbar(im, ax=axes[1])
+
+    plt.tight_layout()
+    output_path = Path(csv_path).stem + "_analysis.png"
+    plt.savefig(output_path, dpi=150)
+    console.print(f"\n[bold green]✓[/bold green] Saved visualization to {output_path}")
+
+
+def main() -> None:
+    """Main entry point."""
+    if len(sys.argv) < 2:
+        console.print("[bold red]Error:[/bold red] Please provide CSV file path")
+        console.print("\nUsage: uv run data_analysis.py <csv_file>")
+        sys.exit(1)
+
+    csv_path = sys.argv[1]
+    if not Path(csv_path).exists():
+        console.print(f"[bold red]Error:[/bold red] File '{csv_path}' not found")
+        sys.exit(1)
+
+    try:
+        analyze_data(csv_path)
+    except Exception as e:
+        console.print(f"[bold red]Error:[/bold red] {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()