statementsparser 0.1.0

Parse Indian bank statements (SBI, HDFC, ICICI, Axis) into clean, unified pandas DataFrames

🏦 India Bank Parse

Parse Indian bank statements (SBI, HDFC, ICICI, Axis) into clean, unified pandas DataFrames.

No more manual data entry. No more messy CSVs. Just clean, structured data from your bank PDFs.

---

✨ Features

• 🏦 Multi-bank support — SBI, HDFC, ICICI, Axis Bank
• 🔐 Password-protected PDFs — handles encrypted statements (DOB-based passwords)
• 📊 Unified DataFrame output — same columns regardless of bank
• 💳 UPI metadata extraction — parse UPI-ZOMATO-gpay-user@ybl into structured fields
• 🏷️ Auto-categorization — Food, Salary, EMI, Rent, Investment, etc.
• ✅ Balance verification — confirms opening + credits - debits = closing
• 📤 Export to CSV/Excel/JSON — Tally-ready Excel with summary sheet
• 🖥️ CLI tool — bankparse statement.pdf from your terminal
• 🔍 Auto bank detection — detects which bank from PDF content

📦 Installation

pip install statementparser

🚀 Quick Start

Python API

from statementparser import parse

# Parse a statement (auto-detects bank)
stmt = parse("sbi_statement.pdf", password="01011990")

# Get a clean DataFrame
df = stmt.to_dataframe()
print(df[['date', 'amount', 'category', 'upi_merchant', 'upi_app']].head())

# Access structured data
for txn in stmt.transactions:
    print(f"{txn.date} | ₹{txn.amount} | {txn.category.value}")
    if txn.upi:
        print(f"  → {txn.upi.merchant} via {txn.upi.app}")

# Check balance verification
if stmt.balance_verification:
    print(f"Balance verified: {stmt.balance_verification.is_valid}")

CLI

# Pretty table output
bankparse statement.pdf --password 01011990

# Export to CSV
bankparse statement.pdf -p 01011990 -f csv -o transactions.csv

# Export to Excel (with summary sheet)
bankparse statement.pdf -p 01011990 -f excel

# Parse all PDFs in a folder
bankparse ./statements/ -p 01011990 -f excel

# Force a specific bank parser
bankparse statement.pdf -b sbi -f json

🏦 Supported Banks

Bank	Status	Formats
State Bank of India (SBI)	✅	PDF
HDFC Bank	✅	PDF
ICICI Bank	✅	PDF
Axis Bank	✅	PDF

💳 UPI Parsing

The library parses UPI narration strings into structured data:

Input:  "UPI-Hari Enterprises-gpay-11244530509@okbizaxis-UTIB0000553-121864632957"

Output: {
    "merchant": "Hari Enterprises",
    "app": "Google Pay",           # from "gpay" + @okbizaxis
    "vpa": "11244530509@okbizaxis",
    "counterparty_bank": "Axis Bank",  # from IFSC UTIB
    "upi_ref": "121864632957"
}

📊 Auto-Categorization

Transactions are automatically tagged:

Category	Keywords
Food	Zomato, Swiggy, Restaurant, Hotel
Shopping	Amazon, Flipkart, Myntra
Investment	Zerodha, Groww, Prudent, SIP
Salary	Salary, Payroll
EMI	EMI, Loan, Bajaj Finance
Transport	Uber, Ola, IRCTC
... and 15+ more

🛠️ Development

# Clone and install
git clone https://github.com/iharshlalakiya/statementparser.git
cd statementparser
uv sync --extra dev

# Run tests
uv run pytest

# Lint & format
uv run ruff check src/ tests/
uv run ruff format src/ tests/

# Build
uv build

📄 License

MIT License — see LICENSE for details.

🤝 Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

Want to add support for your bank? Check the parser guide — it's easier than you think!