DataPars

DataPars is a data processing and parsing framework designed for extracting structured information from unstructured or semi‑structured sources such as logs, web pages, and scientific datasets. The tool is written in Python and provides a modular architecture that allows developers to compose reusable parsers using declarative grammar specifications or programmatic extraction rules. DataPars supports common formats including CSV, JSON, XML, HTML, and custom binary protocols, and can be extended with plug‑in modules to handle proprietary data streams.

The project was initially released in 2015 by a group of open‑source contributors who identified a need for lightweight, extensible parsing utilities in the data science community. Since its first public release, DataPars has evolved to include features such as pattern matching with regular expressions, schema inference, and progress‑monitoring callbacks. The framework also offers integration with popular libraries like pandas for downstream data manipulation and analysis.

Typical use cases for DataPars involve extracting key metrics from server logs, cleaning questionnaire responses, converting raw sensor data into time‑series formats, and scraping tables from HTML documents. Its API allows both low‑level byte‑stream manipulation and high‑level semantic extraction, making it suitable for both researcher prototyping and production data pipelines.

DataPars is maintained under an MIT license, with source code hosted on a public Git hosting service. The community is active on issue trackers, discussion forums, and a quarterly mailing list that reports new releases, bug fixes, and user‑contributed parser templates. The project also documents best practices for error handling, testing, and performance optimization, helping users to adopt robust parsing workflows.