Files
fn_registry/python/functions/datascience/summary_stats.md
egutierrez dabc945eda feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)
Extrae al registry funciones del proyecto interno footprint_aurgi:
- core (6): slugify_ascii, normalize_for_join, cp_provincia_es, infer_provincia_from_cp, safe_read_csv_fallback, csv_to_parquet_duckdb
- geo puras (7): haversine_km, point_in_ring, point_in_polygon, point_in_polygons_bbox, polygon_bbox, extent_with_padding, distance_bucket
- geo I/O (4): load_geojson_polygons, load_boundary_gdf, add_basemap_osm, add_basemap_with_timeout
- valhalla client (4): valhalla_route, valhalla_isochrone, valhalla_isochrones_async, valhalla_matrix_1_to_n
- datascience stats (7): trimmed_mean, geometric_mean, detect_distribution_type, best_central_tendency, summary_stats, kde_density_levels, alpha_shape_concave_hull
- datascience fuzzy (3): fuzzy_merge_adaptive (rapidfuzz), words_to_dataset, remove_words_from_column
- datascience viz (2): plot_kde_2d, plot_heatmap_log
- infra (4): compress_pdf_ghostscript, render_table_page_pdfpages, add_header_logo, osm2pgsql_ingest
- pipelines (4): setup_geo_stack_docker, compute_centers_reachability, generate_isochrones_by_zone, count_points_per_zone
- types geo (4): LonLat, BBox, IsochroneRequest, Centro

Incluye:
- apps/footprint_geo_stack/ (PostGIS + Martin + Valhalla via docker-compose)
- 131/132 tests pasan (1 skip esperado: osm2pgsql en PATH)
- Issue tracker dev/issues/0052-footprint-aurgi-extraction.md
- Atribucion uniforme: source_repo internal:footprint_aurgi, source_license internal-aurgi
- Build con 9 agentes en paralelo (8 wave 1 + 1 wave 2 pipelines)

Tambien commitea trabajo previo no commiteado: aggregate_extraction_results, chunk_with_overlap, clean_pdf_text, merge_entity_aliases, extract_graph_gliner2, extract_relations_mrebel, extract_triples_spacy_es, gliner2/mrebel/marianmt/rebel/spacy_es load_model, parse_rebel_output, translate_es_to_en, issue 0050/0051.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-04 23:35:22 +02:00

1.3 KiB

id, name, kind, lang, domain, version, purity, signature, description, tags, uses_functions, uses_types, returns, returns_optional, error_type, imports, example, tested, tests, test_file_path, file_path, params, output, source_repo, source_license, source_file
id name kind lang domain version purity signature description tags uses_functions uses_types returns returns_optional error_type imports example tested tests test_file_path file_path params output source_repo source_license source_file
summary_stats_py_datascience summary_stats function py datascience 1.0.0 pure def summary_stats(values: list[float]) -> dict Returns basic descriptive statistics (n, mean, median, p25, p75) for a list of floats. Empty input returns n=0 and nan for all numeric fields.
statistics
descriptive
eda
summary
percentile
false
math
numpy
from summary_stats import summary_stats result = summary_stats([1, 2, 3, 4, 5]) true
test_summary_stats_basic
test_summary_stats_empty
test_summary_stats_single
test_summary_stats_keys
python/functions/datascience/tests/test_summary_stats.py python/functions/datascience/summary_stats.py
name desc
values List of numeric values to summarize.
Dict with n (int), mean, median, p25, p75 (floats). All floats are math.nan when values is empty. internal:footprint_aurgi internal-aurgi ponderacion_isochronas/example/models/eda/utils.py:60

Funcion pura minimal para EDA rapido. No incluye std, min, max ni otros percentiles — mantener la interfaz pequena.