diff --git a/duplicate_checker.py b/duplicate_checker.py index a681910a..51ff913c 100644 --- a/duplicate_checker.py +++ b/duplicate_checker.py @@ -1,107 +1,114 @@ -#duplicate_checker.py (v2.0 - mit Blocking-Strategie) +# duplicate_checker.py (v2.0 - mit Blocking-Strategie) + import logging import pandas as pd from thefuzz import fuzz from config import Config from helpers import normalize_company_name, simple_normalize_url from google_sheet_handler import GoogleSheetHandler ---- Konfiguration --- + +# --- Konfiguration --- CRM_SHEET_NAME = "CRM_Accounts" MATCHING_SHEET_NAME = "Matching_Accounts" SCORE_THRESHOLD = 80 + 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 -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 -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) + """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 + 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 + 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) + def main(): -"""Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten.""" -logging.info("Starte den Duplikats-Check (v2.0 mit Blocking)...") -try: - sheet_handler = GoogleSheetHandler() -except Exception as e: - logging.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}") - return - -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 - -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 - -logging.info("Normalisiere Daten für den Vergleich...") -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) - -# --- 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 = "" + """Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten.""" + logging.info("Starte den Duplikats-Check (v2.0 mit Blocking)...") - logging.info(f"Prüfe {index + 1}/{total_matches}: {match_row['CRM Name']}...") + try: + sheet_handler = GoogleSheetHandler() + except Exception as e: + logging.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}") + return - # Finde den Block von Kandidaten - block_key = match_row['block_key'] - candidates = crm_index.get(block_key, []) + 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 + + 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 + + logging.info("Normalisiere Daten für den Vergleich...") + 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) + + # --- 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) - # 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 - best_match_name = crm_row['CRM Name'] + logging.info("Starte Matching-Prozess...") + results = [] + total_matches = len(matching_df) - if best_score >= SCORE_THRESHOLD: - results.append({'Potenzieller Treffer im CRM': best_match_name, 'Ähnlichkeits-Score': best_score}) + for index, match_row in matching_df.iterrows(): + best_score = 0 + best_match_name = "" + + logging.info(f"Prüfe {index + 1}/{total_matches}: {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 + 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}) + + 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) + + 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.") 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}) + logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.") -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) - -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.") -else: - logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.") -if name == "main": -main() \ No newline at end of file +if __name__ == "__main__": + main() \ No newline at end of file