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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 +1,105 @@
+import camelot
+import pandas as pd
+import re
+
+def extract_bank_data(file_path):
+    print(f"Reading {file_path}... processing all pages.")
+    
+    # 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})'
+    
+    for table in tables:
+        df = table.df
+        for _, row in df.iterrows():
+            cells = [str(c).strip() for c in row]
+            row_text = " ".join(cells)
+            
+            # 1. TRIGGER: A new transaction starts with an amount (=xx,xx)
+            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
+                if current_tx:
+                    final_transactions.append(current_tx)
+                
+                # Assign Debit/Credit based on the column index of the '=' sign
+                amt_val = amount_match.group(1)
+                is_credit = any('=' in cells[i] for i in range(len(cells)) if i >= 2)
+                
+                current_tx = {
+                    "value date": date_match.group(1),
+                    "Creditor / Debtor": cells[0].split('\n')[0], # Only take the first line of text
+                    "account": "",
+                    "payment details": "",
+                    "debit": "" if is_credit else amt_val,
+                    "credit": amt_val if is_credit else "",
+                    "is_collecting_details": False
+                }
+                
+                # Check if IBAN is in this row
+                iban_search = re.search(iban_pattern, cells[0])
+                if iban_search:
+                    current_tx["account"] = iban_search.group(0)
+            
+            elif current_tx:
+                # 2. CONTINUATION: Extract info from rows following the amount row
+                text_line = cells[0]
+                if not text_line:
+                    continue
+                
+                # Check for IBAN
+                iban_search = re.search(iban_pattern, text_line)
+                if iban_search:
+                    current_tx["account"] = iban_search.group(0)
+                    # Once we hit the IBAN, everything following it is usually 'Payment Details'
+                    current_tx["is_collecting_details"] = True
+                    remaining = re.sub(iban_pattern, '', text_line).strip()
+                    # Filter out BIC/Internal Routing codes
+                    remaining = re.sub(r'[A-Z]{8,11}', '', remaining).strip()
+                    if remaining:
+                        current_tx["payment details"] += " " + remaining
+                    continue
+                
+                # Identify if this is a Payment Detail row (Reference numbers or purpose text)
+                # We filter out common address keywords (Cesta, Ulica, Trg, Postcodes)
+                is_address = any(kw in text_line.upper() for kw in ["CESTA", "ULICA", "TRG", " LJUBLJANA", " LOGATEC"])
+                
+                if current_tx["is_collecting_details"] or ("SI00" in text_line or "SI12" in text_line):
+                    # Filter out routing codes like BACXSI...
+                    clean_text = re.sub(r'[A-Z]{8,11}', '', text_line).strip()
+                    if clean_text:
+                        current_tx["payment details"] = (current_tx["payment details"] + " " + clean_text).strip()
+                elif not is_address and not current_tx["account"]:
+                    # If it's not an address and we don't have an IBAN yet, it might be Name continuation
+                    if len(current_tx["Creditor / Debtor"]) < 20: # Only append if the name is very short
+                        current_tx["Creditor / Debtor"] += " " + text_line
+
+    # Append the last transaction
+    if current_tx:
+        final_transactions.append(current_tx)
+
+    # 3. Clean up and Export
+    df_final = pd.DataFrame(final_transactions)
+    if "is_collecting_details" in df_final.columns:
+        df_final = df_final.drop(columns=["is_collecting_details"])
+
+    # Final text cleaning (remove double spaces, routing codes)
+    df_final["payment details"] = df_final["payment details"].str.replace(r'[A-Z]{8,11}', '', regex=True)
+    df_final["payment details"] = df_final["payment details"].str.replace(r'\s+', ' ', regex=True).str.strip()
+    
+    # Export
+    output_file = "bank_export_fixed_v3.csv"
+    df_final.to_csv(output_file, sep=';', index=False, encoding='utf-8-sig')
+    print(f"Success! Exported {len(df_final)} transactions to {output_file}")
+
+if __name__ == "__main__":
+    extract_bank_data("MSDataReport (1).pdf")