{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/9"}],"enrichment":{"capability":"earthkit-data provides a unified Python interface to read, transform, and export geospatial data in multiple formats (GRIB, NetCDF, and others) for meteorology and climate applications.","skillfed_tags":["geospatial-data","meteorology","climate-science"],"use_cases":["Load GRIB or NetCDF meteorological data from ECMWF sources and convert to xarray Datasets for analysis.","Batch process multiple climate model outputs in different formats into a unified pandas DataFrame.","Export geospatial data between formats (GRIB to NetCDF, or to NumPy) for downstream modeling or visualization.","Access remote weather data through earthkit-data's source abstraction without writing format-specific parsers.","Integrate geospatial data pipelines into dask-based workflows for out-of-core processing."],"what_it_does":"earthkit-data is a Python library that abstracts away format differences in geospatial data, letting you load meteorological and climate datasets from multiple sources and export them to common scientific formats. It sits at the data-handling layer of the broader earthkit ecosystem, providing a consistent API whether you're reading GRIB files (via cfgrib and eccodes), NetCDF (via netcdf4), or other geospatial formats. The library is marked as Graduated under ECMWF's maturity guidelines, indicating production-ready status.\n\nThe package is designed for scientists and developers working with weather and climate data. It handles the complexity of format conversion and data access, letting you work with xarray Datasets, pandas DataFrames, or NumPy arrays depending on your workflow. With 20 runtime dependencies\u2014including dask for lazy evaluation, xarray for labeled arrays, and ECMWF's own eccodes for GRIB handling\u2014it brings together the scientific Python ecosystem's best tools for geospatial work.","worth_installing":"Yes. earthkit-data is actively maintained, production-stable, has no known vulnerabilities, and solves a real problem for meteorology and climate workflows. The Apache-2.0 license is permissive. Install friction is low, though the 20 runtime dependencies (especially eccodes) may require time to resolve; conda installation is recommended. Worth installing if you work with GRIB, NetCDF, or other geospatial formats in scientific Python."},"id":"earthkit-data","links":{"html":"https://skillfed.io/packages/earthkit-data","md":"https://skillfed.io/packages/earthkit-data.md","pypi":"https://pypi.org/project/earthkit-data/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-20","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"earthkit-data","python_support":"supports_current","summary":"A format-agnostic Python interface for geospatial data"},"popularity":{"monthly_downloads":82061,"position":14187,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.0"}