{"categories":[{"label":"Text Processing","url":"https://skillfed.io/packages/category/text-processing/2"}],"enrichment":{"capability":"Extracts tables from PDFs into pandas DataFrames using multiple parsing strategies (lattice, stream, network, hybrid, and optional neural) and exports them to CSV, JSON, Excel, HTML, Markdown, or SQLite.","skillfed_tags":["pdf-extraction","data-pipeline","table-parsing"],"use_cases":["Extract financial tables from annual reports or regulatory filings into DataFrames for analysis.","Batch-convert tabular data from scanned PDFs or image-only documents using the optional OCR backend.","Automatically recover table structure from borderless PDFs using the neural ml backend when heuristics fail.","Build ETL pipelines that read PDF tables, filter by quality metrics, and export to CSV or databases.","Parse mixed PDF documents with both ruled and borderless tables using flavor='auto' for automatic parser selection.","Stitch multi-page tables across PDF boundaries with stack_contiguous() for continuous data extraction."],"what_it_does":"This library extracts tables from PDF files into structured data using five parsing strategies: lattice (for ruled tables with visible grid lines), stream and network (for borderless tables using whitespace heuristics), hybrid (combining text alignment), and an optional neural backend using a Table Transformer model for hard borderless cases. It also supports scanned PDFs via optional OCR integration. Every extracted table becomes a pandas DataFrame, ready for analysis or export to multiple formats (CSV, JSON, Excel, HTML, Markdown, SQLite).\n\nThe package handles both text-based and image-only PDFs, provides quality metrics (accuracy, whitespace, confidence scores) to filter noise, supports multi-page table continuations, and includes a command-line interface. Default installation requires no system dependencies\u2014the pdfium backend is bundled as a wheel. Optional extras add neural parsing and OCR capabilities. Configuration is extensive, covering table areas, column separators, and text processing.","worth_installing":"Yes. Actively maintained, no known vulnerabilities, installs with low friction (no system dependencies by default), and well-suited for any workflow requiring PDF table extraction into pandas. The multiple parsing strategies and optional neural backend make it flexible for ruled, borderless, and scanned tables. MIT license poses no restrictions."},"id":"camelot-py","links":{"html":"https://skillfed.io/packages/camelot-py","md":"https://skillfed.io/packages/camelot-py.md","pypi":"https://pypi.org/project/camelot-py/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-04","license_spdx":null,"license_treatment":"permissive","name":"camelot-py","python_support":"supports_current","summary":"PDF Table Extraction for Humans."},"popularity":{"monthly_downloads":844528,"position":4922,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.0.0"}