# 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