Extract structured tabular data from PDF documents using camelot.
npx skills add https://github.com/legout/pi-config --skill table-extractor-legout
Please help me install this Agent Skill. Skill: table-extractor Source: https://github.com/legout/pi-config/tree/main/installed-skills/table-extractor Command: npx skills add https://github.com/legout/pi-config --skill table-extractor-legout
This skill solves the difficulty of extracting tabular data from PDF documents, which often results in messy, unformatted text or broken layouts when using standard copy-paste methods.
Use the table-extractor skill to identify and extract all tables from the provided document named report.pdf.
High-intent search queries and answers about installing and using this skill.
To extract complex tables from PDF documents into structured data, this skill uses the camelot library to identify table layouts and convert them directly into pandas DataFrames. It handles both bordered and borderless structures accurately.
Yes, you can extract merged cells and multi-page tables from a PDF. The extraction process specifically supports complex table layouts including merged cells, multi-page tables, and borderless data structures using advanced detection algorithms.
Yes, you need ghostscript installed to parse PDF tables with camelot. Both camelot-py and ghostscript are required dependencies to perform accurate table detection and successfully convert the extracted data into pandas DataFrames.
The best way to convert borderless PDF tables into pandas DataFrames is using stream mode detection. This approach identifies and parses borderless data structures with high accuracy, automatically formatting the output into clean, machine-readable DataFrames.
Your PDF table extraction results in messy unformatted text when using standard copy-paste methods because they cannot interpret complex layouts. Using a dedicated library like camelot solves this by accurately detecting lattice and stream layouts.
To export extracted PDF tables to Excel or CSV, the skill first converts the identified tabular data into pandas DataFrames. Once the data is structured in a DataFrame, it is ready for immediate export to Excel, CSV, or JSON formats.
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