Show HN: LLM-powered data extraction from messy spreadsheets (github.com)

A new tool has been introduced that leverages large language models (LLMs) for automated data extraction from disorganized Excel and CSV files. The library, named "tablereader," simplifies the process of retrieving clean, structured data by detecting table boundaries while intelligently skipping irrelevant elements like headers and totals. Users can easily deploy it by installing via pip, setting their OpenAI API key, and utilizing a straightforward Python function to read data from various file formats. This development is significant for the AI/ML community as it enhances data accessibility and usability, particularly in fields where messy datasets are common. Key features include the ability to recognize different data types such as numbers and dates, manage formatting inconsistencies like currency symbols, and efficiently process large files without overwhelming system resources. By streamlining data extraction, "tablereader" not only saves time but also helps researchers and analysts focus more on data analysis rather than data cleaning, effectively democratizing the use of LLMs in practical applications.