Extracting Context from Every Spreadsheet

Spreadsheets, despite their simple appearance, often contain complex structures that challenge programmatic processing, especially when dealing with large or messy files. Ragie has been effective at extracting data from typical table-like spreadsheets but faced difficulties with those that visually resemble documents with multiple sections. To address this, Ragie developed a method to identify "islands" of data, or sections separated by empty cells, within spreadsheets. This approach is informed by an understanding of how spreadsheets store data—specifically, that only non-empty cells are stored, making the files conceptually sparse. By using Python libraries like openpyxl and pandas, Ragie leverages vectorized operations to efficiently handle large datasets, reducing runtime complexity. The process involves detecting large tables using heuristics and processing remaining sections to form structured regions, enabling Ragie to better extract and understand spreadsheet content at scale. This methodology helps transform disorganized spreadsheets into organized regions, facilitating improved data retrieval and generation.