How to Extract Data from PDFs and Documents with Python

Not all valuable data lives on web pages. Reports, invoices, research papers, and government filings often come as PDFs and documents. Python has excellent libraries for extracting structured data from these formats. In this guide, I'll show you practical techniques for parsing PDFs, extracting tables, and handling scanned documents with OCR. PDF Parsing Libraries Python offers several PDF parsing options, each with different strengths: Library Best For Tables OCR Speed PyPDF2 Text extraction No No Fast pdfplumber Tables & layout Yes No Medium Camelot Table extraction Yes No Medium pytesseract Scanned PDFs No Yes Slow pymupdf (fitz) Full-featured Yes Yes Fast Basic Text Extraction For simple text PDFs, PyPDF2 or pymupdf works well: import fitz # pymupdf def extract_text_from_pdf ( pdf_path ): doc = fitz . open ( pdf_path ) text = "" for page in doc : text += page . get_text () doc . close () return text # Usage text = extract_text_from_pdf ( " report.pdf " ) print ( text [: 500 ]) For