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skills/python-uv-scripts/patterns/data-processing.md

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+# Data Processing Patterns
+
+> **Status**: 🚧 Placeholder - Content in development
+
+## Overview
+
+Patterns for data analysis, ETL, and processing using Polars, pandas, and other data libraries in UV single-file
+scripts.
+
+## Topics to Cover
+
+- [ ] Polars patterns (recommended for performance)
+- [ ] Pandas alternatives
+- [ ] CSV/Excel processing
+- [ ] JSON data manipulation
+- [ ] Data validation and cleaning
+- [ ] Aggregation and transformation
+- [ ] Memory-efficient processing
+
+## Quick Example
+
+```python
+#!/usr/bin/env -S uv run
+# /// script
+# requires-python = ">=3.11"
+# dependencies = ["polars>=0.20.0"]
+# ///
+import polars as pl
+
+def analyze_csv(file_path: str):
+    df = pl.read_csv(file_path)
+
+    # Basic analysis
+    summary = df.describe()
+    print(summary)
+
+    # Filter and aggregate
+    result = (
+        df.filter(pl.col("value") > 100)
+          .groupby("category")
+          .agg(pl.col("value").mean())
+    )
+    print(result)
+```
+
+## TODO
+
+This file will be expanded to include:
+
+- Complete Polars patterns
+- Performance optimization techniques
+- Large file processing strategies
+- Data validation patterns
+- Export formats and options