1.1 KiB
1.1 KiB
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
#!/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