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2026-08-16 04:07:48 +02:00
parent 799a1c305a
commit ad2b93d80c
5 changed files with 2724 additions and 0 deletions
@@ -0,0 +1,117 @@
+import camelot
+import pandas as pd
+import re
+
+def extract_bank_data(file_path):
+    print(f"Reading {file_path}... enforcing strict Debit/Credit alignment.")
+    
+    # We use 'stream' flavor for the NLB PDF layout
+    tables = camelot.read_pdf(file_path, pages='1-end', flavor='stream')
+    
+    final_transactions = []
+    current_tx = None
+    
+    # Precise Regex Patterns
+    iban_pattern = r'SI56\s?\d{4}\s?\d{4}\s?\d{4}\s?\d{3}'
+    amount_pattern = r'=(\d+[\.,]\d{2})'
+    date_pattern = r'(\d{2}\.\d{2}\.\d{2})'
+    bic_pattern = r'\b[A-Z]{8,11}\b' # Standard BIC pattern
+
+    for table in tables:
+        df = table.df
+        for _, row in df.iterrows():
+            cells = [str(c).strip() for c in row]
+            row_text = " ".join(cells)
+            
+            # TRIGGER: A new transaction starts with an amount (=xx,xx) and a date
+            amount_match = re.search(amount_pattern, row_text)
+            date_match = re.search(date_pattern, row_text)
+            
+            if amount_match and date_match:
+                # Save previous transaction before starting new one
+                if current_tx:
+                    final_transactions.append(current_tx)
+                
+                amt_val = amount_match.group(1)
+                
+                # --- STRICT DEBIT/CREDIT LOGIC ---
+                # Based on the NLB layout, columns are indexed 0 to N.
+                # If the '=' sign is found in index 1, it is DEBIT.
+                # If the '=' sign is found in index 2 or higher, it is CREDIT.
+                is_credit = False
+                for i, cell_content in enumerate(cells):
+                    if '=' in cell_content:
+                        if i >= 2: 
+                            is_credit = True
+                        break
+                
+                # Take only the first line of the cell as the Name (to exclude address)
+                name_raw = cells[0].split('\n')[0].strip()
+
+                current_tx = {
+                    "value date": date_match.group(1),
+                    "Creditor / Debtor": name_raw,
+                    "account": "",
+                    "payment details": "",
+                    "debit": "" if is_credit else amt_val,
+                    "credit": amt_val if is_credit else "",
+                    "collecting": True
+                }
+                
+                # Check if IBAN is on this same row
+                ib = re.search(iban_pattern, cells[0])
+                if ib:
+                    current_tx["account"] = ib.group(0)
+
+            elif current_tx:
+                text_line = cells[0]
+                if not text_line or text_line.lower() in ["address", "account"]:
+                    continue
+
+                # 1. Look for IBAN
+                iban_search = re.search(iban_pattern, text_line)
+                if iban_search:
+                    current_tx["account"] = iban_search.group(0)
+                    # Anything after IBAN on the same line might be details
+                    rem = text_line.replace(iban_search.group(0), "").strip()
+                    text_line = rem
+                
+                # 2. Filter out Address and Bank Noise
+                is_address = any(k in text_line.upper() for k in ["CESTA", "ULICA", "TRG", " LJUBLJANA", " LOGATEC"]) or re.search(r'\d{4}', text_line)
+                
+                if not is_address and len(text_line) > 1:
+                    # Clean up BIC/Routing codes (e.g. LJBASI2X)
+                    clean_text = re.sub(bic_pattern, '', text_line).strip()
+                    # Clean up internal bank references (CR.../DR...)
+                    clean_text = re.sub(r'\b[CD]R\d+\b|\bNRC\b', '', clean_text).strip()
+                    
+                    if clean_text:
+                        if not current_tx["payment details"]:
+                            current_tx["payment details"] = clean_text
+                        else:
+                            # Avoid repeating the name in details if it's already there
+                            if clean_text not in current_tx["Creditor / Debtor"]:
+                                current_tx["payment details"] += " " + clean_text
+
+    # Append last transaction
+    if current_tx:
+        final_transactions.append(current_tx)
+
+    # Convert to DataFrame
+    df_final = pd.DataFrame(final_transactions)
+    
+    # Final Cleanup: ensure columns match your requested list exactly
+    cols = ["value date", "Creditor / Debtor", "account", "payment details", "debit", "credit"]
+    df_final = df_final[cols]
+    
+    # Clean whitespace and handle empty values
+    for col in df_final.columns:
+        df_final[col] = df_final[col].astype(str).str.replace(r'\s+', ' ', regex=True).str.strip()
+        df_final[col] = df_final[col].replace('nan', '')
+
+    output_file = "bank_export_final_fixed.csv"
+    df_final.to_csv(output_file, sep=';', index=False, encoding='utf-8-sig')
+    print(f"Success! {len(df_final)} transactions exported to {output_file}")
+
+if __name__ == "__main__":
+    extract_bank_data("MSDataReport (1).pdf")