geoarrow-types
Decision gist · record as of 2026-08-14
Yes, if you are working with GeoArrow data in PyArrow or building tools that need to handle multiple geometry encodings and coordinate dimensions. No, if you are only using high-level libraries directly—those packages handle type management internally. The aging maintenance status is a minor concern but not a blocker, as the package is stable and has no known vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction install with no runtime dependencies.
- Maintenance status is aging—last release was 444 days ago, though the repository remains active with a recent commit on 2025-09-30.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions.
last release 2025-05-27 (444 days) · last repo commit 2025-09-30 · 95 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 315,135 downloads/mo, #7,690 on PyPI
Alternatives
Verify before relying
import geoarrow.types as gt
gt.TypeSpec.common(
gt.Encoding.GEOARROW,
gt.GeometryType.POINT,
gt.GeometryType.MULTIPOINT,
gt.Dimensions.XYM,
gt.Dimensions.XYZ,
).to_pyarrow()- Whether geoarrow-types is intended for end-user applications or primarily as a developer-facing utility
- Performance characteristics when handling large numbers of type permutations
- Stability guarantees or semantic versioning policy given the aging maintenance status
What it is and what it does
geoarrow-types is a pure Python library that manages the complexity of GeoArrow type specifications. GeoArrow is a standardized way to represent geographic data in Apache Arrow format, but the specification allows many permutations: different geometry types, coordinate dimensions (X/Y, X/Y/Z, X/Y/M, X/Y/Z/M), and serialized encodings (native GeoArrow, well-known text, well-known binary). This package provides utilities to construct, query, and transform between these type combinations without manually tracking all the variations.
The package is designed primarily as a developer tool within the geoarrow ecosystem—it powers higher-level bindings that integrate with Arrow-friendly formats. If you are building tools that work with GeoArrow data or need to reason about geometry type compatibility across different encodings and dimensions, this package handles the type algebra for you.
Use it for
- Determine the common GeoArrow type that can represent multiple input geometry types with different dimensions
- Convert between WKT, WKB, and native GeoArrow encodings while preserving coordinate dimensionality
- Construct PyArrow extension types for GeoArrow geometries with specific coordinate layouts and dimensions
- Validate and reason about geometry type compatibility when merging or transforming spatial datasets
- Build tooling that needs to inspect or manipulate GeoArrow type metadata and specifications
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working with GeoArrow data in PyArrow or building tools that need to handle multiple geometry encodings and coordinate dimensions.
No, if you are only using high-level libraries directly—those packages handle type management internally. The aging maintenance status is a minor concern but not a blocker, as the package is stable and has no known vulnerabilities.
Install
geoarrow-types on PyPI
Before you install
Low friction install with no runtime dependencies. Maintenance status is aging—last release was 444 days ago, though the repository remains active with a recent commit on 2025-09-30.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions.
Quickstart
import geoarrow.types as gt
gt.TypeSpec.common(
gt.Encoding.GEOARROW,
gt.GeometryType.POINT,
gt.GeometryType.MULTIPOINT,
gt.Dimensions.XYM,
gt.Dimensions.XYZ,
).to_pyarrow()Verify before relying
- Whether geoarrow-types is intended for end-user applications or primarily as a developer-facing utility
- Performance characteristics when handling large numbers of type permutations
- Stability guarantees or semantic versioning policy given the aging maintenance status
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Aging 444 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 315,135 / month, #7,690 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: geoarrow_types-0.3.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “geoarrow type specification”
- geoarrow-typesProvides pure Python utilities to manage, compute on, and specify…
- geoarrow-pyarrowProvides Python bindings for the GeoArrow specification, enabling…
- geoarrow-pandasIntegrates GeoArrow geometry data with pandas and pyarrow, enabling…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also geoarrow-pyarrow · geoarrow-pandas · geoarrow-c · plpygis · geopandas · geomet · pygeoif · pyGeoTile · pymap3d · utm