118 lines
4.2 KiB
Python
118 lines
4.2 KiB
Python
import re
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import pandas as pd
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import recordlinkage
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from rapidfuzz import fuzz
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from google_sheet_handler import GoogleSheetHandler
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# --- Konfiguration ---
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CRM_SHEET_NAME = "CRM_Accounts"
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MATCHING_SHEET_NAME = "Matching_Accounts"
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SCORE_THRESHOLD = 0.8
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WEIGHTS = {
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'domain': 0.5,
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'name': 0.4,
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'city': 0.1,
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}
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# --- Hilfsfunktionen ---
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def normalize_company_name(name: str) -> str:
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"""
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Vereinfachte Normalisierung von Firmennamen:
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- Unicode‑safe Kleinschreibung
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- Umlaute in ae/oe/ue, ß in ss
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- Entfernen von Rechtsformen und Stop-Wörtern
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"""
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s = str(name).casefold()
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for src, dst in [('ä','ae'), ('ö','oe'), ('ü','ue'), ('ß','ss')]:
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s = s.replace(src, dst)
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# Nur alphanumerisch und Leerzeichen
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s = re.sub(r'[^a-z0-9\s]', ' ', s)
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stops = ['gmbh','ag','kg','ug','ohg','holding','group','international']
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tokens = [t for t in s.split() if t and t not in stops]
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return ' '.join(tokens)
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def normalize_domain(url: str) -> str:
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"""Root-Domain extrahieren, Protokoll und www entfernen"""
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s = str(url).casefold().strip()
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s = re.sub(r'^https?://', '', s)
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s = s.split('/')[0]
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return s.removeprefix('www.')
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def main():
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# Google Sheets laden
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sheet_handler = GoogleSheetHandler()
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crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
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match_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
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if crm_df is None or crm_df.empty or match_df is None or match_df.empty:
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print("Fehler: Leere Daten in einem der Tabs. Abbruch.")
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return
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# Normalisierung
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for df in (crm_df, match_df):
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df['norm_name'] = df['CRM Name'].fillna('').apply(normalize_company_name)
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df['norm_domain'] = df['CRM Website'].fillna('').apply(normalize_domain)
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df['city'] = df['CRM Ort'].fillna('').apply(lambda x: str(x).casefold().strip())
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# Blocking per Domain
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indexer = recordlinkage.Index()
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indexer.block('norm_domain')
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candidate_pairs = indexer.index(crm_df, match_df)
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# Vergleichsregeln definieren
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compare = recordlinkage.Compare()
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compare.exact('norm_domain', 'norm_domain', label='domain')
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compare.string('norm_name', 'norm_name', method='jarowinkler', label='name_sim')
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compare.exact('city', 'city', label='city')
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features = compare.compute(candidate_pairs, crm_df, match_df)
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# Gewichte und Score
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features['score'] = (
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WEIGHTS['domain'] * features['domain'] +
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WEIGHTS['name'] * features['name_sim'] +
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WEIGHTS['city'] * features['city']
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)
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# Bestes Match pro neuer Zeile
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matches = features.reset_index()
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best = matches.sort_values(['level_1','score'], ascending=[True, False]) \
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.drop_duplicates('level_1')
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best = best[best['score'] >= SCORE_THRESHOLD] \
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.rename(columns={'level_0':'crm_idx','level_1':'match_idx'})
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# Merges
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crm_df = crm_df.reset_index()
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match_df = match_df.reset_index()
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merged = (best
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.merge(crm_df, left_on='crm_idx', right_on='index')
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.merge(match_df, left_on='match_idx', right_on='index', suffixes=('_CRM','_NEW'))
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)
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# Ausgabe aufbauen
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output = match_df[['CRM Name','CRM Website','CRM Ort','CRM Land']].copy()
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output['Matched CRM Name'] = ''
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output['Matched CRM Website'] = ''
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output['Matched CRM Ort'] = ''
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output['Matched CRM Land'] = ''
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output['Score'] = 0.0
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for _, row in merged.iterrows():
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i = int(row['match_idx'])
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output.at[i, 'Matched CRM Name'] = row['CRM Name_CRM']
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output.at[i, 'Matched CRM Website'] = row['CRM Website_CRM']
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output.at[i, 'Matched CRM Ort'] = row['CRM Ort_CRM']
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output.at[i, 'Matched CRM Land'] = row['CRM Land_CRM']
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output.at[i, 'Score'] = row['score']
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# Zurückschreiben ins Google Sheet
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data = [output.columns.tolist()] + output.values.tolist()
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success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data)
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if success:
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print(f"Erfolgreich: {len(best)} Matches mit Score ≥ {SCORE_THRESHOLD}")
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else:
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print("Fehler beim Schreiben ins Google Sheet.")
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if __name__ == '__main__':
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main()
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