Data Export

CoreLens provides a unified core_lens.export module for saving Result objects to various file formats. Standard tabular formats use Polars for high-speed writes, while geospatial formats leverage DuckDB’s spatial extensions for robust geometry encoding.

Standard Formats

Export non-spatial results to standard formats. Note that these formats cannot encode geometry columns; if your Result has geometry, these functions will raise a TypeError.

from core_lens.export import parquet, csv, json

res = aoi.mws.annual

# Export to Parquet with ZSTD compression
parquet(res, "output.parquet", compression="zstd")

# Export to CSV
csv(res, "output.csv", separator=",")

# Export to JSON
json(res, "output.json", pretty=True)

Geospatial Formats

Export spatial data to GeoParquet and GeoJSON. The result must have geometry attached (e.g., by calling .with_geometry()).

from core_lens.export import geoparquet, geojson

# Make sure geometry is attached!
spatial_res = aoi.mws.annual.with_geometry()

# Export to GeoParquet with DuckDB COPY options (e.g., partitioning)
geoparquet(spatial_res, "output.geoparquet", compression="ZSTD", partition_by="year")

# Export to GeoJSON
geojson(spatial_res, "output.json")

# Export to newline-delimited GeoJSON (GeoJSONSeq)
geojson(spatial_res, "output.ndjson", driver="GeoJSONSeq")

Note

Exporting to GeoParquet uses DuckDB to cast WKB geometries to proper spatial types via the spatial extension. CoreLens handles the installation and loading of the extension seamlessly under the hood.

Exporting to GeoJSON is fully natively streamed directly from Polars and Shapely without invoking GDAL or DuckDB, making it extremely fast for large datasets. Driver-specific kwargs for GeoJSON are ignored.