egutierrez
faac610745
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>
2.5 KiB
2.5 KiB
name, kind, lang, domain, version, purity, signature, description, tags, params, output, uses_functions, uses_types, returns, returns_optional, error_type, imports, tested, tests, test_file_path, file_path, source_repo, source_license, source_file
| name | kind | lang | domain | version | purity | signature | description | tags | params | output | uses_functions | uses_types | returns | returns_optional | error_type | imports | tested | tests | test_file_path | file_path | source_repo | source_license | source_file | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fuzzy_merge_adaptive | function | py | datascience | 1.0.0 | pure | def fuzzy_merge_adaptive(left: list[dict], right: list[dict], left_key: str, right_key: str, thresholds: list[int] | None = None, how: str = 'left') -> list[dict] | Fuzzy join adaptativo entre dos listas de dicts usando rapidfuzz.token_sort_ratio. Prueba thresholds de mayor a menor y asigna el mayor cumplido. Soporta how='left' (todos los de left) e how='inner' (solo con match). Campos colisionantes reciben sufijos _left/_right. |
|
|
Lista de dicts mergeados con campos de left + right (sufijos _left/_right si colisionan) + fuzzy_match (str|None), match_score (int), threshold_used (int|None). | false |
|
true |
|
python/functions/datascience/tests/test_fuzzy_merge_adaptive.py | python/functions/datascience/fuzzy_merge_adaptive.py | internal:footprint_aurgi | internal-aurgi | fuzzy_joins/fuzzy_en_batches.py |
Ejemplo
from fuzzy_merge_adaptive import fuzzy_merge_adaptive
left = [{"name": "Madrid"}, {"name": "Barclona"}]
right = [{"name": "Madrid", "cp": "28"}, {"name": "Barcelona", "cp": "08"}]
result = fuzzy_merge_adaptive(left, right, left_key="name", right_key="name")
# result[1]["fuzzy_match"] == "Barcelona", result[1]["match_score"] >= 80
Notas
Migrado de thefuzz a rapidfuzz (API compatible, mayor velocidad). Sin pandas: el merge se implementa manualmente via dict lookup por right_key. Los thresholds se prueban de mayor a menor; el primero cumplido se asigna a threshold_used.