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>
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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 | |||||||||||||||||||||||||||||
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| words_to_dataset | function | py | datascience | 1.0.0 | pure | def words_to_dataset(texts: Iterable[str | None], min_ocurrencias: int = 1, eliminar_stopwords: bool = False) -> list[dict] | Extrae palabras y sus ocurrencias de un iterable de textos. Tokeniza con \b\w+\b, convierte a mayusculas, cuenta con Counter, filtra por minimo de ocurrencias y opcionalmente elimina stopwords en espanol. Sin pandas. |
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Lista de dicts {'palabra': str, 'ocurrencias': int} ordenada por ocurrencias descendente. | false | true |
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python/functions/datascience/tests/test_words_to_dataset.py | python/functions/datascience/words_to_dataset.py | internal:footprint_aurgi | internal-aurgi | fuzzy_joins/arreglo_fuzzy.py |
Ejemplo
from words_to_dataset import words_to_dataset
texts = ["calle mayor", "calle del sol", "avenida principal"]
result = words_to_dataset(texts)
# [{"palabra": "CALLE", "ocurrencias": 2}, {"palabra": "MAYOR", "ocurrencias": 1}, ...]
result_clean = words_to_dataset(texts, eliminar_stopwords=True)
# "DEL" no aparece
Notas
Stopwords embebidas (frozenset de ~40 palabras ES). Funcion pura: solo stdlib (re, collections.Counter). Tokens en mayusculas para unificar "Calle" y "CALLE".