Bank Statement Parser

Bank Statement Parser is a Python library designed to parse and normalize transaction data from various bank statement formats ( CSV, Excel, etc.) into a consistent and easy-to-use Pandas DataFrame. It supports multiple banks and file formats, making it a versatile tool for financial data analysis.

Features

• Multi-Format Support: Parse bank statements from CSV, Excel, and more.
• Config-Driven: All parsing behaviour is described by a config dict — no per-bank classes to maintain.
• Resilient: Tolerant of header spacing/punctuation changes and messy number/date formatting.
• Consistent Output: Normalized transaction data with standardized columns (bank, created_date, remarks, amount, hash).
• Easy Integration: Simple API for quick integration into your Python projects.
• Extensible: Add support for a new bank by writing config, not code.

Installation

You can install the library via pip:

pip install statement_parser

Usage

Using a bundled preset

from statement_parser import GenericBank, list_banks

print(list_banks())  # ['HDFC-CREDIT', 'HSBC-CREDIT', ...]

parser = GenericBank.from_builtin("HSBC-CREDIT")
df = parser.getDataFrame("path/to/statement.csv")
print(df.head())

Bring your own config

No subclassing required — define a config dict and hand it to GenericBank:

from statement_parser import GenericBank

config = {
    "file": {
        "delimiter": ",",
        "header": {
            "mode": "detect",                       # detect | fixed | none
            "match": ["date", "description", "amount"],
            "min_matches": 2,
        },
    },
    "columns": {                                    # logical -> candidate names
        "date": ["Txn Date", "Date"],
        "details": ["Description", "Narration"],
        "amount": ["Amount"],
    },
    "date_field": "date",
    "remarks": [{"field": "details"}],
    "amount": {"mode": "direct", "field": "amount"},
}

parser = GenericBank(config, bank_id="MY-BANK")
df = parser.getDataFrame("path/to/statement.csv")
print(df.head())

Amount modes: direct (single amount column), signed (amount column whose sign is decided by a CR/DR column), or deposit_minus_withdrawal (separate deposit and withdrawal columns).