{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"}],"enrichment":{"capability":"Downloads and subsets sparse geospatial datasets stored in ARCO format via STAC metadata and SQLite, returning results as pandas DataFrames or partitioned Parquet files.","skillfed_tags":["geospatial-data","stac-catalog","sparse-datasets"],"use_cases":["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."],"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.\n\nThe package is built on requests, pandas, pystac, pyarrow, and tqdm, and is explicitly marked as a low-level tool\u2014the 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.","worth_installing":"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\u2014install this only if you have a specific need for low-level STAC subsetting."},"id":"arcosparse","links":{"html":"https://skillfed.io/packages/arcosparse","md":"https://skillfed.io/packages/arcosparse.md","pypi":"https://pypi.org/project/arcosparse/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-09","license_spdx":null,"license_treatment":"unclear","name":"arcosparse","python_support":"supports_current","summary":"Helper to download and subset sparse data that has been Arcoified and are available through STAC and sqlite formated data"},"popularity":{"monthly_downloads":123489,"position":11906,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.1"}