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CSV / Excel parser for bank statement uploads.
Supports flexible column name mapping.
"""
import pandas as pd
import io
from datetime import datetime
from typing import Optional
from loguru import logger
COLUMN_ALIASES = {
"date": ["date", "txn date", "transaction date", "value date", "trans date",
"booking date", "posting date", "transaction_date", "txn_date", "date_time"],
"description": ["description", "narration", "particulars", "remarks", "details",
"transaction details", "trans details", "merchant", "raw_description",
"recipient_name", "recipient", "note", "transaction_remarks"],
"amount": ["amount", "transaction amount", "debit/credit amount", "debit amount",
"credit amount", "txn amount"],
"debit": ["debit", "withdrawal", "dr", "debit amount"],
"credit": ["credit", "deposit", "cr", "credit amount"],
"balance": ["balance", "closing balance", "available balance", "running balance"],
"method": ["method", "mode", "payment method", "type", "transaction type",
"payment mode", "channel"],
}
def _find_column(df_cols: list, aliases: list) -> Optional[str]:
"""Find the first matching column from aliases (case-insensitive)."""
lower_cols = {c.lower().strip(): c for c in df_cols}
for alias in aliases:
if alias.lower() in lower_cols:
return lower_cols[alias.lower()]
return None
def _detect_direction(row, debit_col: Optional[str], credit_col: Optional[str],
amount_col: Optional[str], desc: str) -> tuple:
"""Determine if a transaction is debit or credit and return amount."""
amount = 0.0
direction = "debit"
if debit_col and credit_col:
# Separate debit/credit columns
debit_val = row.get(debit_col, None)
credit_val = row.get(credit_col, None)
try:
debit_amt = float(str(debit_val).replace(',', '').strip()) if pd.notna(debit_val) and str(debit_val).strip() not in ('', '-', 'nan') else 0
except (ValueError, TypeError):
debit_amt = 0
try:
credit_amt = float(str(credit_val).replace(',', '').strip()) if pd.notna(credit_val) and str(credit_val).strip() not in ('', '-', 'nan') else 0
except (ValueError, TypeError):
credit_amt = 0
if debit_amt > 0:
amount = debit_amt
direction = "debit"
elif credit_amt > 0:
amount = credit_amt
direction = "credit"
elif amount_col:
raw = str(row.get(amount_col, 0)).replace(',', '').strip()
try:
amount = abs(float(raw))
except (ValueError, TypeError):
amount = 0
# Infer direction from sign or description keywords
try:
if float(raw) < 0:
direction = "debit"
else:
direction = "credit"
except (ValueError, TypeError):
desc_lower = desc.lower()
direction = "credit" if any(w in desc_lower for w in ['salary', 'credit', 'received', 'refund']) else "debit"
return amount, direction
def _detect_payment_method(desc: str, method_val: Optional[str]) -> str:
if method_val and str(method_val).strip().upper() not in ('NAN', ''):
mv = str(method_val).strip().upper()
for m in ['UPI', 'IMPS', 'NEFT', 'ATM']:
if m in mv:
return m
desc_upper = desc.upper()
for m in ['UPI', 'IMPS', 'NEFT', 'ATM']:
if m in desc_upper:
return m
return 'OTHER'
def parse_bank_statement(file_bytes: bytes, filename: str) -> list:
"""
Parse a CSV or Excel bank statement and return a list of normalized transaction dicts.
"""
try:
if filename.lower().endswith(('.xlsx', '.xls')):
df = pd.read_excel(io.BytesIO(file_bytes), engine='openpyxl')
else:
# Try multiple encodings
for enc in ['utf-8', 'latin-1', 'cp1252']:
try:
df = pd.read_csv(io.BytesIO(file_bytes), encoding=enc, skip_blank_lines=True)
break
except UnicodeDecodeError:
continue
else:
raise ValueError("Could not decode CSV file")
except Exception as e:
raise ValueError(f"Failed to read file: {e}")
# Strip whitespace from column names
df.columns = [str(c).strip() for c in df.columns]
# Drop rows that are completely empty
df = df.dropna(how='all')
cols = df.columns.tolist()
date_col = _find_column(cols, COLUMN_ALIASES["date"])
desc_col = _find_column(cols, COLUMN_ALIASES["description"])
amount_col = _find_column(cols, COLUMN_ALIASES["amount"])
debit_col = _find_column(cols, COLUMN_ALIASES["debit"])
credit_col = _find_column(cols, COLUMN_ALIASES["credit"])
balance_col = _find_column(cols, COLUMN_ALIASES["balance"])
method_col = _find_column(cols, COLUMN_ALIASES["method"])
if not date_col or not desc_col:
raise ValueError(
f"Could not auto-detect required columns (date, description). "
f"Found: {cols}. "
f"Please ensure your CSV has columns like: date, description, amount."
)
if not amount_col and not (debit_col and credit_col):
raise ValueError(
"Could not find amount column(s). "
"Need either 'amount' or separate 'debit'/'credit' columns."
)
transactions = []
skipped = 0
for _, row in df.iterrows():
try:
# Parse date
raw_date = row[date_col]
if pd.isna(raw_date):
skipped += 1
continue
if isinstance(raw_date, datetime):
txn_date = raw_date
else:
raw_date_str = str(raw_date).strip()
for fmt in [
'%d-%m-%Y', '%d/%m/%Y', '%Y-%m-%d', '%m/%d/%Y',
'%d %b %Y', '%d %B %Y', '%b %d, %Y', '%d-%b-%Y',
'%d/%m/%y', '%m/%d/%y',
]:
try:
txn_date = datetime.strptime(raw_date_str, fmt)
break
except ValueError:
continue
else:
try:
txn_date = pd.to_datetime(raw_date_str, dayfirst=True).to_pydatetime()
except Exception:
skipped += 1
continue
# Build a rich description if multiple columns exist
all_desc_parts = []
main_desc = str(row[desc_col]).strip()
if main_desc and main_desc.lower() != 'nan':
all_desc_parts.append(main_desc)
# Check for extra info in common columns like UPI_ID or Note
for extra_col_alias in ["upi_id", "note", "remarks", "category_hint"]:
found_col = _find_column(cols, [extra_col_alias])
if found_col and found_col != desc_col:
val = str(row[found_col]).strip()
if val and val.lower() != 'nan' and val not in all_desc_parts:
all_desc_parts.append(val)
desc = " | ".join(all_desc_parts)
if not desc:
skipped += 1
continue
amount, direction = _detect_direction(row, debit_col, credit_col, amount_col, desc)
if amount <= 0:
skipped += 1
continue
# Balance
balance = None
if balance_col:
try:
bal_raw = str(row[balance_col]).replace(',', '').strip()
balance = float(bal_raw) if bal_raw not in ('nan', '', '-') else None
except (ValueError, TypeError):
balance = None
method = _detect_payment_method(desc, row.get(method_col) if method_col else None)
transactions.append({
"raw_description": desc,
"amount": round(amount, 2),
"direction": direction,
"payment_method": method,
"transaction_date": txn_date,
"balance": balance,
})
except Exception as e:
logger.warning(f"Skipped row: {e}")
skipped += 1
logger.info(f"Parsed {len(transactions)} transactions, skipped {skipped} rows from '{filename}'")
return transactions
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