geoparquet-io 0.9.0

Fast I/O and transformation tools for GeoParquet files

geoparquet-io

Fast I/O and transformation tools for GeoParquet files using PyArrow and DuckDB.

📚 Full Documentation | Quick Start Tutorial

Features

• Fast: Built on PyArrow and DuckDB for high-performance operations
• Pipeable: Chain commands with Unix pipes using Arrow IPC streaming - no intermediate files
• Comprehensive: Sort, extract, partition, enhance, validate, and upload GeoParquet files
• Cloud-Native: Read from and write to S3, GCS, Azure, and HTTPS sources
• Spatial Indexing: Add bbox, H3 hexagonal cells, KD-tree partitions, and admin divisions
• Best Practices: Automatic optimization following GeoParquet 1.1 and 2.0 specs
• Parquet Geo Types support: Read and write Parquet geometry and geography types.
• Flexible: CLI and Python API for any workflow
• Tested: Extensive test suite across Python 3.10-3.13 and all platforms

Installation

pip install geoparquet-io

See the Installation Guide for other options (uv, from source) and requirements.

Quick Start

# Inspect file structure and metadata
gpio inspect myfile.parquet

# Check file quality and best practices
gpio check all myfile.parquet

# Add bounding box column for faster queries
gpio add bbox input.parquet output.parquet

# Sort using Hilbert curve for spatial locality
gpio sort hilbert input.parquet output_sorted.parquet

# Partition by admin boundaries
gpio partition admin buildings.parquet output_dir/ --dataset gaul --levels continent,country

# Remote-to-remote processing (S3, GCS, Azure, HTTPS)
gpio add bbox s3://bucket/input.parquet s3://bucket/output.parquet --profile my-aws
gpio partition h3 gs://bucket/data.parquet gs://bucket/partitions/ --resolution 9
gpio sort hilbert https://example.com/data.parquet s3://bucket/sorted.parquet

# Chain commands with Unix pipes - no intermediate files needed
gpio extract --bbox "-122.5,37.5,-122.0,38.0" input.parquet | gpio add bbox - | gpio sort hilbert - output.parquet

For more examples and detailed usage, see the Quick Start Tutorial and User Guide.

Python API

Use gpio programmatically for the best performance:

import geoparquet_io as gpio

# Read, transform, and write in a fluent chain
gpio.read('input.parquet') \
    .add_bbox() \
    .sort_hilbert() \
    .write('output.parquet')

# Convert from other formats (Shapefile, GeoJSON, GeoPackage, CSV)
gpio.convert('data.gpkg') \
    .add_h3(resolution=9) \
    .partition_by_h3('output/', resolution=5)

# Upload to cloud storage
gpio.read('data.parquet') \
    .extract(bbox=(-122.5, 37.5, -122.0, 38.0)) \
    .add_bbox() \
    .upload('s3://bucket/filtered.parquet')

The Python API keeps data in memory as Arrow tables, providing up to 5x better performance than CLI operations. See the Python API documentation for full details.

Claude Code Integration

Use gpio with Claude Code for AI-assisted spatial data workflows.

Install the skill from skills/geoparquet/ or download it from:

https://github.com/geoparquet/geoparquet-io/tree/main/skills/geoparquet

The skill teaches Claude how to help you convert spatial data to optimized GeoParquet, validate files, recommend partitioning strategies, and publish to cloud storage.

Contributing

Contributions are welcome! See CONTRIBUTING.md for development setup, coding standards, and how to submit changes.

Links

• Documentation: https://geoparquet.io/
• PyPI: https://pypi.org/project/geoparquet-io/
• Issues: https://github.com/geoparquet/geoparquet-io/issues

License

Apache 2.0 - See LICENSE for details.