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skills/python-uv-scripts/assets/templates/data-processor.py

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+#!/usr/bin/env -S uv run --script
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#   "polars>=0.20.0",
+#   "rich>=13.0.0",
+# ]
+# ///
+"""
+Data processing template using Polars.
+
+Demonstrates reading, transforming, and analyzing data with
+modern data processing libraries.
+
+Usage:
+    python data-processor.py input.csv
+    python data-processor.py data/*.csv --output results/
+"""
+
+import sys
+from pathlib import Path
+import polars as pl
+from rich.console import Console
+from rich.table import Table
+
+console = Console()
+
+
+def process_csv(file_path: Path) -> pl.DataFrame:
+    """Read and process a CSV file."""
+    try:
+        df = pl.read_csv(file_path)
+        console.print(f"[cyan]Loaded {len(df)} rows from {file_path.name}[/cyan]")
+
+        # Example transformations
+        # df = df.filter(pl.col("status") == "active")
+        # df = df.with_columns(pl.col("amount").cast(pl.Float64))
+
+        return df
+
+    except Exception as e:
+        console.print(f"[red]Error processing {file_path}: {e}[/red]")
+        sys.exit(1)
+
+
+def display_summary(df: pl.DataFrame):
+    """Display data summary using Rich tables."""
+    table = Table(title="Data Summary")
+    table.add_column("Metric", style="cyan")
+    table.add_column("Value", style="green")
+
+    table.add_row("Total Rows", str(len(df)))
+    table.add_row("Columns", str(len(df.columns)))
+
+    # Example statistics
+    # if "amount" in df.columns:
+    #     table.add_row("Total Amount", f"${df['amount'].sum():,.2f}")
+
+    console.print(table)
+
+
+def main():
+    """Main entry point."""
+    if len(sys.argv) < 2:
+        console.print("[red]Usage: python data-processor.py <input.csv>[/red]")
+        sys.exit(1)
+
+    input_path = Path(sys.argv[1])
+
+    if not input_path.exists():
+        console.print(f"[red]Error: {input_path} not found[/red]")
+        sys.exit(1)
+
+    df = process_csv(input_path)
+    display_summary(df)
+
+    # Optional: Save results
+    # output_path = Path("output.csv")
+    # df.write_csv(output_path)
+    # console.print(f"[green]✓ Saved to {output_path}[/green]")
+
+
+if __name__ == "__main__":
+    main()