From 94a2dc882d98417fcdd5f55a1bd7609641d134fc Mon Sep 17 00:00:00 2001 From: Floke Date: Fri, 1 Aug 2025 11:20:10 +0000 Subject: [PATCH] duplicate_checker.py aktualisiert --- duplicate_checker.py | 84 +++++++++++++++++++++----------------------- 1 file changed, 40 insertions(+), 44 deletions(-) diff --git a/duplicate_checker.py b/duplicate_checker.py index 51ff913c..c2e15fd0 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -1,4 +1,4 @@ -# duplicate_checker.py (v2.0 - mit Blocking-Strategie) +# duplicate_checker.py (v2.1 - mit Match-Basis-Anzeige) import logging import pandas as pd @@ -10,26 +10,35 @@ from google_sheet_handler import GoogleSheetHandler # --- Konfiguration --- CRM_SHEET_NAME = "CRM_Accounts" MATCHING_SHEET_NAME = "Matching_Accounts" -SCORE_THRESHOLD = 80 +SCORE_THRESHOLD = 80 # Wird jetzt nur noch zur Hervorhebung genutzt, angezeigt werden alle logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') -def calculate_similarity(record1, record2): - """Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen.""" - total_score = 0 - if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']: - total_score += 100 +def calculate_similarity_details(record1, record2): + """ + Berechnet einen gewichteten Ähnlichkeits-Score und gibt die Details zurück. + """ + scores = {'name': 0, 'location': 0, 'domain': 0} + + # 1. Website-Domain (stärkstes Signal) + if record1['normalized_domain'] and record1['normalized_domain'] != 'k.a.' and record1['normalized_domain'] == record2['normalized_domain']: + scores['domain'] = 100 + + # 2. Firmenname (Fuzzy-Signal) if record1['normalized_name'] and record2['normalized_name']: - name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) - total_score += name_similarity * 0.7 + scores['name'] = round(fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name']) * 0.7) + + # 3. Standort (Bestätigungs-Signal) if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']: if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']: - total_score += 20 - return round(total_score) + scores['location'] = 20 + + total_score = sum(scores.values()) + return {'total': total_score, 'details': scores} def main(): """Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten.""" - logging.info("Starte den Duplikats-Check (v2.0 mit Blocking)...") + logging.info("Starte den Duplikats-Check (v2.1 mit Match-Basis)...") try: sheet_handler = GoogleSheetHandler() @@ -39,15 +48,11 @@ def main(): logging.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...") crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME) - if crm_df is None or crm_df.empty: - logging.critical(f"Konnte keine Daten aus '{CRM_SHEET_NAME}' laden. Breche ab.") - return + if crm_df is None or crm_df.empty: return logging.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...") matching_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME) - if matching_df is None or matching_df.empty: - logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.") - return + if matching_df is None or matching_df.empty: return logging.info("Normalisiere Daten für den Vergleich...") for df in [crm_df, matching_df]: @@ -55,58 +60,49 @@ def main(): df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url) df['CRM Ort'] = df['CRM Ort'].astype(str).str.lower().str.strip() df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip() - # Blocking Key: Das erste Wort des normalisierten Namens df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None) - # --- NEUE, SCHNELLE BLOCKING-STRATEGIE --- logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...") - crm_index = {} - for index, row in crm_df.iterrows(): - key = row['block_key'] - if key: - if key not in crm_index: - crm_index[key] = [] - crm_index[key].append(row) + crm_index = crm_df.groupby('block_key').apply(lambda x: x.to_dict('records')).to_dict() logging.info("Starte Matching-Prozess...") results = [] - total_matches = len(matching_df) for index, match_row in matching_df.iterrows(): - best_score = 0 + best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}} best_match_name = "" - logging.info(f"Prüfe {index + 1}/{total_matches}: {match_row['CRM Name']}...") + logging.info(f"Prüfe {index + 1}/{len(matching_df)}: {match_row['CRM Name']}...") - # Finde den Block von Kandidaten block_key = match_row['block_key'] candidates = crm_index.get(block_key, []) - # Führe den teuren Vergleich nur für die Kandidaten in diesem Block durch for crm_row in candidates: - score = calculate_similarity(match_row, crm_row) - if score > best_score: - best_score = score + score_info = calculate_similarity_details(match_row, crm_row) + if score_info['total'] > best_score_info['total']: + best_score_info = score_info best_match_name = crm_row['CRM Name'] - if best_score >= SCORE_THRESHOLD: - results.append({'Potenzieller Treffer im CRM': best_match_name, 'Ähnlichkeits-Score': best_score}) - else: - # Wenn nichts im Block gefunden wurde, trotzdem den besten Treffer (kann 0 sein) anzeigen - results.append({'Potenzieller Treffer im CRM': '' if not best_match_name else best_match_name, 'Ähnlichkeits-Score': best_score}) + results.append({ + 'Potenzieller Treffer im CRM': best_match_name if best_score_info['total'] >= SCORE_THRESHOLD else "", + 'Score (Gesamt)': best_score_info['total'], + 'Score (Name)': best_score_info['details']['name'], + 'Bonus (Standort)': best_score_info['details']['location'], + 'Bonus (Domain)': best_score_info['details']['domain'] + }) logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...") result_df = pd.DataFrame(results) - # Die ursprünglichen Spalten aus matching_df für die Ausgabe nehmen - output_df = matching_df[['CRM Name', 'CRM Website', 'CRM Ort', 'CRM Land']].copy() - output_df = pd.concat([output_df.reset_index(drop=True), result_df], axis=1) + # Originale Spalten aus matching_df für die Ausgabe nehmen + original_cols = [col for col in ['CRM Name', 'CRM Website', 'CRM Ort', 'CRM Land'] if col in matching_df.columns] + output_df = pd.concat([matching_df[original_cols].reset_index(drop=True), result_df], axis=1) data_to_write = [output_df.columns.values.tolist()] + output_df.values.tolist() success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write) if success: - logging.info(f"Ergebnisse erfolgreich in das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.") + logging.info(f"Ergebnisse erfolgreich in '{MATCHING_SHEET_NAME}' geschrieben.") else: logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")