107 lines
4.5 KiB
Python
107 lines
4.5 KiB
Python
duplicate_checker.py (v2.0 - mit Blocking-Strategie)
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import logging
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import pandas as pd
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from thefuzz import fuzz
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from config import Config
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from helpers import normalize_company_name, simple_normalize_url
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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 = 80
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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def calculate_similarity(record1, record2):
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"""Berechnet einen gewichteten Ähnlichkeits-Score zwischen zwei Datensätzen."""
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total_score = 0
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if record1['normalized_domain'] and record1['normalized_domain'] == record2['normalized_domain']:
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total_score += 100
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if record1['normalized_name'] and record2['normalized_name']:
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name_similarity = fuzz.token_set_ratio(record1['normalized_name'], record2['normalized_name'])
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total_score += name_similarity * 0.7
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if record1['CRM Ort'] and record1['CRM Ort'] == record2['CRM Ort']:
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if record1['CRM Land'] and record1['CRM Land'] == record2['CRM Land']:
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total_score += 20
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return round(total_score)
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def main():
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"""Hauptfunktion zum Laden, Vergleichen und Schreiben der Daten."""
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logging.info("Starte den Duplikats-Check (v2.0 mit Blocking)...")
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try:
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sheet_handler = GoogleSheetHandler()
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except Exception as e:
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logging.critical(f"FEHLER bei Initialisierung des GoogleSheetHandler: {e}")
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return
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logging.info(f"Lade Master-Daten aus '{CRM_SHEET_NAME}'...")
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crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
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if crm_df is None or crm_df.empty:
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logging.critical(f"Konnte keine Daten aus '{CRM_SHEET_NAME}' laden. Breche ab.")
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return
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logging.info(f"Lade zu prüfende Daten aus '{MATCHING_SHEET_NAME}'...")
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matching_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
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if matching_df is None or matching_df.empty:
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logging.critical(f"Konnte keine Daten aus '{MATCHING_SHEET_NAME}' laden. Breche ab.")
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return
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logging.info("Normalisiere Daten für den Vergleich...")
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for df in [crm_df, matching_df]:
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df['normalized_name'] = df['CRM Name'].astype(str).apply(normalize_company_name)
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df['normalized_domain'] = df['CRM Website'].astype(str).apply(simple_normalize_url)
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df['CRM Ort'] = df['CRM Ort'].astype(str).str.lower().str.strip()
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df['CRM Land'] = df['CRM Land'].astype(str).str.lower().str.strip()
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# Blocking Key: Das erste Wort des normalisierten Namens
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df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
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# --- NEUE, SCHNELLE BLOCKING-STRATEGIE ---
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logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
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crm_index = {}
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for index, row in crm_df.iterrows():
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key = row['block_key']
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if key:
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if key not in crm_index:
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crm_index[key] = []
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crm_index[key].append(row)
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logging.info("Starte Matching-Prozess...")
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results = []
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total_matches = len(matching_df)
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for index, match_row in matching_df.iterrows():
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best_score = 0
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best_match_name = ""
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logging.info(f"Prüfe {index + 1}/{total_matches}: {match_row['CRM Name']}...")
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# Finde den Block von Kandidaten
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block_key = match_row['block_key']
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candidates = crm_index.get(block_key, [])
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# Führe den teuren Vergleich nur für die Kandidaten in diesem Block durch
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for crm_row in candidates:
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score = calculate_similarity(match_row, crm_row)
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if score > best_score:
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best_score = score
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best_match_name = crm_row['CRM Name']
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if best_score >= SCORE_THRESHOLD:
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results.append({'Potenzieller Treffer im CRM': best_match_name, 'Ähnlichkeits-Score': best_score})
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else:
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# Wenn nichts im Block gefunden wurde, trotzdem den besten Treffer (kann 0 sein) anzeigen
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results.append({'Potenzieller Treffer im CRM': '' if not best_match_name else best_match_name, 'Ähnlichkeits-Score': best_score})
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logging.info("Matching abgeschlossen. Schreibe Ergebnisse zurück ins Sheet...")
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result_df = pd.DataFrame(results)
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# Die ursprünglichen Spalten aus matching_df für die Ausgabe nehmen
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output_df = matching_df[['CRM Name', 'CRM Website', 'CRM Ort', 'CRM Land']].copy()
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output_df = pd.concat([output_df.reset_index(drop=True), result_df], axis=1)
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data_to_write = [output_df.columns.values.tolist()] + output_df.values.tolist()
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success = sheet_handler.clear_and_write_data(MATCHING_SHEET_NAME, data_to_write)
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if success:
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logging.info(f"Ergebnisse erfolgreich in das Tabellenblatt '{MATCHING_SHEET_NAME}' geschrieben.")
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else:
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logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")
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if name == "main":
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main() |