Working with PDFs is one of the most common (and frustrating) data engineering tasks. Unlike CSV or Excel files, PDFs are designed for visual presentation, not structured data extraction. Choosing the right Python library can save you hours of cleanup or completely break your pipeline.
In this article, we compare the most popular Python tools for extracting tables and text from PDFs, focusing on accuracy, complexity, and real-world use cases.
Quick Comparison Overview
Different tools shine in different scenarios. Here is a high-level summary before diving deeper:
Tabula-py is best for clean, well-structured tables.
Camelot is excellent for wide and complex layouts.
pdfplumber is flexible and powerful for irregular tables.
PyMuPDF is fast for text extraction but needs extra parsing.
Tesseract OCR is the only option for scanned PDFs.
pdfquery is perfect when exact coordinates are required.
Tabula-py
Type: Text-based
Output: Pandas DataFrame
Complexity: Medium
Tabula-py is a Python wrapper for Tabula (Java-based) and is one of the most popular tools for table extraction.
Pros:
Very easy to use
Direct output as Pandas DataFrames
Great results on clean, grid-based tables
Cons:
Struggles with complex layouts
Requires Java
Best use case: clean, well-formatted tables with clear borders.
Camelot
Type: Text-based
Output: Pandas DataFrame
Complexity: Medium
Camelot is often considered more accurate than Tabula, especially for wide tables or complex page layouts.
Pros:
Excellent precision
Handles complex table structures better than Tabula
Supports both lattice and stream parsing modes
Cons:
Slightly steeper learning curve
Can fail on very irregular tables
Best use case: wide tables and complex layouts where precision matters.
pdfplumber
Type: Text-based with parsing
Output: Requires processing
Complexity: Medium–High
pdfplumber offers low-level access to PDF elements and is extremely flexible.
Pros:
Highly customizable
Excellent for irregular or borderless tables
Can extract text, lines, and coordinates
Cons:
Requires manual parsing logic
More coding effort compared to Tabula or Camelot
Best use case: irregular tables or PDFs where automated tools fail.
PyMuPDF (fitz)
Type: Text-based
Output: Text only
Complexity: Medium
PyMuPDF is fast and efficient but does not natively extract tables.
Pros:
Very fast
High-quality text extraction
Good for preprocessing PDFs
Cons:
No built-in table extraction
Requires custom parsing
Best use case: fast text extraction when you plan to build your own table parser.
Tesseract OCR
Type: Image-based
Output: Text only
Complexity: High
When PDFs are scanned images, OCR is the only viable solution.
Pros:
Works with scanned PDFs
Supports multiple languages
Cons:
Lower accuracy than text-based tools
No table awareness
Requires image preprocessing
Best use case: scanned documents with no embedded text.
pdfquery
Type: Text-based
Output: Text with coordinates
Complexity: High
pdfquery is ideal when you need pixel-level control.
Pros:
Precise coordinate-based extraction
Ideal for fixed-layout documents
Powerful for automation
Cons:
Complex setup
Not beginner-friendly
Best use case: PDFs with consistent layouts where exact positioning matters.