115 lines
5.1 KiB
Python
115 lines
5.1 KiB
Python
# duplicate_checker.py (v2.1 - mit Match-Basis-Anzeige)
|
|
|
|
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 ---
|
|
CRM_SHEET_NAME = "CRM_Accounts"
|
|
MATCHING_SHEET_NAME = "Matching_Accounts"
|
|
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_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']:
|
|
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']:
|
|
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 (v2.1 mit Match-Basis)...")
|
|
|
|
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: 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: 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()
|
|
df['block_key'] = df['normalized_name'].apply(lambda x: x.split()[0] if x else None)
|
|
|
|
logging.info("Erstelle Index für CRM-Daten zur Beschleunigung...")
|
|
crm_index = crm_df.groupby('block_key').apply(lambda x: x.to_dict('records')).to_dict()
|
|
|
|
logging.info("Starte Matching-Prozess...")
|
|
results = []
|
|
|
|
for index, match_row in matching_df.iterrows():
|
|
best_score_info = {'total': 0, 'details': {'name': 0, 'location': 0, 'domain': 0}}
|
|
best_match_name = ""
|
|
|
|
logging.info(f"Prüfe {index + 1}/{len(matching_df)}: {match_row['CRM Name']}...")
|
|
|
|
block_key = match_row['block_key']
|
|
candidates = crm_index.get(block_key, [])
|
|
|
|
for crm_row in candidates:
|
|
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']
|
|
|
|
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)
|
|
|
|
# KORRIGIERTE LOGIK: Hole die Originaldaten aus dem DataFrame, bevor er normalisiert wurde.
|
|
# `matching_df` enthält hier bereits die normalisierten Spalten, die wir nicht wollen.
|
|
# Wir laden die Originaldaten neu oder verwenden eine Kopie. Der einfachste Weg:
|
|
original_matching_df = sheet_handler.get_sheet_as_dataframe(MATCHING_SHEET_NAME)
|
|
|
|
# Füge die Ergebnisse zu den Originaldaten hinzu
|
|
output_df = pd.concat([original_matching_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)
|
|
if success:
|
|
logging.info(f"Ergebnisse erfolgreich in '{MATCHING_SHEET_NAME}' geschrieben.")
|
|
else:
|
|
logging.error("FEHLER beim Schreiben der Ergebnisse ins Google Sheet.")
|
|
|
|
if __name__ == "__main__":
|
|
main() |