Colin Maudry
85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
import polars as pl
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from unidecode import unidecode
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from src.utils.table import add_links
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from src.utils.tracking import track_search
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def search_org(dff: pl.DataFrame, query: str, org_type: str) -> pl.DataFrame:
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"""
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Search in either 'acheteur' or 'titulaire' DataFrame.
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:param dff: Polars DataFrame with acheteur or titulaire columns
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:param query: User search string
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:param org_type: 'acheteur' or 'titulaire'
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:return: Filtered DataFrame with 'matches' column
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"""
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if not query.strip():
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return dff.select(pl.lit(False).alias("matches"))
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# Enregistrement des recherche dans Matomo
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track_search(query, "home_page_search")
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# Normalize query
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normalized_query = unidecode(query.strip()).upper()
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tokens = [" " + t.strip() for t in normalized_query.split() if t.strip()]
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# Define columns based on entity type
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cols = [
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f"{org_type}_id",
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f"{org_type}_nom",
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f"{org_type}_departement_nom",
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f"{org_type}_departement_code",
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f"{org_type}_commune_nom",
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]
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# Concatenate all fields into one string per row
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org_str = pl.concat_str(pl.lit(" "), pl.col(cols), separator=" ").str.replace(
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"-", " "
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)
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# For each token, create a boolean column: True if token is found
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token_matches = []
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for token in tokens:
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token_match = org_str.str.contains(token).alias(f"token_{token}")
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token_matches.append(token_match)
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# Count how many tokens match per row
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match_score = pl.sum_horizontal(token_matches).alias("match_score")
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# For each token, create a boolean column: True if token is found
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token_matches = []
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for token in tokens:
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token_match = org_str.str.contains(token).alias(f"token_{token}")
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token_matches.append(token_match)
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# Sélection des colonnes
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if org_type == "acheteur":
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dff = dff.select(cols + ["Marchés"])
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if org_type == "titulaire":
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dff = dff.select(cols + ["Marchés", "titulaire_typeIdentifiant"])
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# Apply and filter
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dff = (
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dff.with_columns(token_matches + [match_score])
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.filter(pl.col("match_score") == len(tokens))
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.drop([f"token_{token}" for token in tokens])
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)
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# Format result
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dff = add_links(dff)
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dff = dff.with_columns(
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pl.concat_str(
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pl.col(f"{org_type}_departement_nom"),
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pl.lit(" ("),
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pl.col(f"{org_type}_departement_code"),
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pl.lit(")"),
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).alias("Département")
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)
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dff = dff.select(f"{org_type}_id", f"{org_type}_nom", "Département", "Marchés")
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dff = dff.group_by(f"{org_type}_id", f"{org_type}_nom", "Département").sum()
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dff = dff.sort("Marchés", descending=True)
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return dff
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