arcosparse
Helper to download and subset sparse data that has been Arcoified and are available through STAC and sqlite formated data
Decision gist · record as of 2026-08-14
Yes, if you are working directly with ARCO sparse datasets via STAC and need fine-grained subsetting control. The library is actively maintained, has low install friction, and carries no known vulnerabilities. However, the unclear license status should be resolved before use in production, and the authors explicitly recommend higher-level tools (Copernicus Marine Toolbox, earthkit) for most users—install this only if you have a specific need for low-level STAC subsetting.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a valid STAC metadata URL; authentication token may be needed for ECMWF or other restricted datasets.
- Low install friction with a pure-Python wheel and five common dependencies.
- Active maintenance with recent release (158 days ago).
License · maintenance · safety
(unclear) — License treatment is unclear—no SPDX identifier or raw license text is recorded in the package metadata, so the actual license terms cannot be verified from this fact sheet alone.
last release 2026-03-09 (158 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 123,489 downloads/mo, #11,906 on PyPI
Alternatives
Verify before relying
import arcosparse
df = arcosparse.subset_and_return_dataframe(
url_metadata="https://example.com/metadata.json",
minimum_latitude=10,
maximum_latitude=20,
minimum_longitude=30,
maximum_longitude=40,
variables=["temperature"]
)- What is the actual license of arcosparse (SPDX identifier not recorded)?
- Are there known limitations or performance characteristics for very large subsets?
- Does the package work with all STAC catalogs or only specific implementations?
What it is and what it does
arcosparse is a Python library for querying and downloading subsets of sparse geospatial datasets that have been stored in ARCO (Analysis Ready Cloud Optimized) format and exposed via STAC (SpatioTemporal Asset Catalog) metadata. It wraps SQLite-backed data access with spatial, temporal, and variable filtering, returning results either as pandas DataFrames or as partitioned Parquet files for large extracts. The library handles authentication via bearer tokens for restricted datasets (particularly ECMWF data) and provides metadata introspection functions to explore available entities, variables, and coordinate ranges before subsetting.
The package is built on requests, pandas, pystac, pyarrow, and tqdm, and is explicitly marked as a low-level tool—the authors recommend using higher-level interfaces like the Copernicus Marine Toolbox or earthkit for typical workflows. It is actively maintained and supports Python 3.9 through 3.14.
Use it for
- Extract a spatial and temporal subset of oceanographic data from a Copernicus Marine STAC catalog and load it into a DataFrame for analysis.
- Download a large regional climate dataset as partitioned Parquet files to avoid memory constraints, then read all partitions into a single DataFrame.
- Query available entities and metadata from a remote STAC dataset to understand its structure before requesting a subset.
- Authenticate to ECMWF STAC assets using a bearer token and retrieve a filtered subset of meteorological variables.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working directly with ARCO sparse datasets via STAC and need fine-grained subsetting control.
The library is actively maintained, has low install friction, and carries no known vulnerabilities. However, the unclear license status should be resolved before use in production, and the authors explicitly recommend higher-level tools (Copernicus Marine Toolbox, earthkit) for most users—install this only if you have a specific need for low-level STAC subsetting.
Install
arcosparse on PyPI
Before you install
Low install friction with a pure-Python wheel and five common dependencies. Active maintenance with recent release (158 days ago). Supports Python 3.9 through 3.14.
Requires a valid STAC metadata URL; authentication token may be needed for ECMWF or other restricted datasets.
License in practice
License treatment is unclear—no SPDX identifier or raw license text is recorded in the package metadata, so the actual license terms cannot be verified from this fact sheet alone.
Quickstart
import arcosparse
df = arcosparse.subset_and_return_dataframe(
url_metadata="https://example.com/metadata.json",
minimum_latitude=10,
maximum_latitude=20,
minimum_longitude=30,
maximum_longitude=40,
variables=["temperature"]
)Verify before relying
- What is the actual license of arcosparse (SPDX identifier not recorded)?
- Are there known limitations or performance characteristics for very large subsets?
- Does the package work with all STAC catalogs or only specific implementations?
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesrequestspandaspystacpyarrowtqdm |
| Maintenance | Actively maintained 158 days since the last release |
| First released | |
| Downloads | 123,489 / month, #11,906 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9 |
Evidence: arcosparse-0.5.1-py3-none-any.whl
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See also copernicusmarine · earthkit-data · pypgstac · pystac · odc-stac · pystac-client · pyreadstat · rio-stac · stac-fastapi-types · kedro-datasets