The Transtractor

PyPI version Development Status Tests codecov License

Universal PDF Bank Statement Parsing

The Transaction Extractor, or 'Transtractor', aspires to be a universal library for extracting transaction data from PDF bank statements. Key features:

  • Written in Rust (fast)
  • Python API (user friendly)
  • AI-free (lightweight)
  • Rules-based extraction (100% predictable and accurate)

Installation

Install from PyPI

Transtractor is available on PyPI and can be installed with pip:

pip install transtractor

Compile from Source

  1. Install Rust: Download and install Rust from rustup.rs

  2. Install uv: Follow instructions from Astral

  3. Sync Python environment and compile: Clone the repository and build

    git clone https://github.com/transtractor/transtractor.git
cd transtractor-lib
uv sync --locked --group dev

  4. Test the package: Run Rust and Python unit tests

    cargo test
uv run pytest

Basic Usage

  1. Import and initialise the parser

    from transtractor import Parser

parser = Parser()

  2. Convert PDF to CSV: All CSV files are written in a standard format

    parser.parse('statement.pdf').to_csv('statement.csv')

  3. Convert PDF to DataFrame: Load into a DataFrame for analysis

    import pandas as pd

data = parser.parse('statement.pdf').to_pandas_dict()
df = pd.DataFrame(data)

Advanced Usage

See the documentation maintained on Read the Docs.

Supported Statements

See the documentation for a current list of supported statements. You may also create your own parsing configuration files by following these instructions and loading it by:

from transtractor import Parser

parser = Parser()
parser.load('my_config.json')
parser.parse('statement.pdf').to_csv('statement.csv')

Contributions

New and well-tested configuration files are especially welcome. Please submit a pull request with them add to the python/transtractor/configs directory, or email to gravytoast@pm.me.

The following pages provide further information about how this package is built and developed:

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.14.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for weberdak from gravatar.com weberdak

Credits

Author: Daniel Weber

License

MIT License

Requires

Python >=3.10

Tags

pdf bank statement parser transaction extraction csv

Classifiers

Report project as malware

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0.14.0

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0.13.0

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0.12.0

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0.11.0

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0.10.0

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0.9.3 This release

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0.9.2

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