From e7c8a66f76429eac25907b2733170e5201dda027 Mon Sep 17 00:00:00 2001 From: Floke Date: Fri, 1 Aug 2025 11:10:42 +0000 Subject: [PATCH] duplicate_checker.py aktualisiert --- duplicate_checker.py | 72 +++++++++++++++++++++++--------------------- 1 file changed, 37 insertions(+), 35 deletions(-) diff --git a/duplicate_checker.py b/duplicate_checker.py index 57a64a84..51ff913c 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -1,4 +1,4 @@ -# duplicate_checker.py +# duplicate_checker.py (v2.0 - mit Blocking-Strategie) import logging import pandas as pd @@ -10,42 +10,33 @@ from google_sheet_handler import GoogleSheetHandler # --- Konfiguration --- CRM_SHEET_NAME = "CRM_Accounts" MATCHING_SHEET_NAME = "Matching_Accounts" -SCORE_THRESHOLD = 80 # Mindest-Score, um als "wahrscheinlicher Treffer" zu gelten +SCORE_THRESHOLD = 80 -# --- Logging einrichten --- 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 - - # 1. Website-Domain (stärkstes Signal) if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']: total_score += 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 # Gewichtung: 70% - - # 3. Standort (Bestätigungs-Signal) + total_score += name_similarity * 0.7 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 # Bonus für vollen Standort-Match - + total_score += 20 return round(total_score) def main(): """Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten.""" - logging.info("Starte den Duplikats-Check...") + logging.info("Starte den Duplikats-Check (v2.0 mit Blocking)...") try: sheet_handler = GoogleSheetHandler() except Exception as e: - logging.critical(f"FEHLER bei der Initialisierung des GoogleSheetHandler: {e}") + logging.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}") return - # 1. Daten laden 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: @@ -58,29 +49,41 @@ def main(): logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.") return - # 2. Daten normalisieren logging.info("Normalisiere Daten für den Vergleich...") - crm_df['normalized_name'] = crm_df['CRM Name'].astype(str).apply(normalize_company_name) - crm_df['normalized_domain'] = crm_df['CRM Website'].astype(str).apply(simple_normalize_url) - crm_df['CRM Ort'] = crm_df['CRM Ort'].astype(str).str.lower().str.strip() - crm_df['CRM Land'] = crm_df['CRM Land'].astype(str).str.lower().str.strip() + for df in [crm_df, matching_df]: + df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name) + 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) - matching_df['normalized_name'] = matching_df['CRM Name'].astype(str).apply(normalize_company_name) - matching_df['normalized_domain'] = matching_df['CRM Website'].astype(str).apply(simple_normalize_url) - matching_df['CRM Ort'] = matching_df['CRM Ort'].astype(str).str.lower().str.strip() - matching_df['CRM Land'] = matching_df['CRM Land'].astype(str).str.lower().str.strip() - - # 3. Matching-Prozess - logging.info("Starte Matching-Prozess... Dies kann einige Zeit dauern.") + # --- 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) + + logging.info("Starte Matching-Prozess...") results = [] + total_matches = len(matching_df) for index, match_row in matching_df.iterrows(): best_score = 0 best_match_name = "" - logging.info(f"Prüfe: {match_row['CRM Name']}...") + logging.info(f"Prüfe {index + 1}/{total_matches}: {match_row['CRM Name']}...") - for _, crm_row in crm_df.iterrows(): + # 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 @@ -89,17 +92,16 @@ def main(): if best_score >= SCORE_THRESHOLD: results.append({'Potenzieller Treffer im CRM': best_match_name, 'Ähnlichkeits-Score': best_score}) else: - results.append({'Potenzieller Treffer im CRM': '', 'Ähnlichkeits-Score': best_score}) + # 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}) - # 4. Ergebnisse zusammenführen und schreiben logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...") result_df = pd.DataFrame(results) - output_df = pd.concat([matching_df.reset_index(drop=True), result_df], axis=1) - # Entferne die temporären normalisierten Spalten für eine saubere Ausgabe - output_df = output_df.drop(columns=['normalized_name', 'normalized_domain']) + # 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) - # Konvertiere DataFrame in Liste von Listen für den Upload 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)