Mastering Extracting Text and Tables from PDF with Python: A Comprehensive Guide to the Best Libraries

Extracting text and parsing tables from PDF files are common yet challenging tasks for developers and data analysts. PDFs are designed for presentation rather than data extraction, making it difficult to programmatically retrieve structured information, such as text and tables, from these files. However, Python, with its rich ecosystem of libraries, offers powerful tools to address this challenge. In this article, we will explore the best Python libraries for extracting text and parsing tables in PDFs, their features, use cases, and how to implement them effectively.

Why Extracting Text and Parsing Tables from PDFs is Challenging

PDFs are not inherently designed to store structured data. From a technical perspective, a PDF file sees text and tables as mere text and graphical elements without any inherent understanding of rows, columns, or boundaries. This lack of structural metadata makes text extraction and table extraction non-trivial tasks. Additionally, the diversity in table formats—ranging from simple grids to complex, multi-level tables—compounds the problem (Artifex Blog, 2023).

Python Libraries for Extracting Text and Parsing Tables in PDFs

Python offers several libraries tailored for extracting text and tables from PDFs. Below, we provide an in-depth analysis of the most popular and reliable options.

1. Camelot

Camelot is a lightweight and user-friendly Python library specifically designed for table extraction. It offers two modes of operation: Stream Mode for tables without visible borders and Lattice Mode for tables with gridlines.

Key Features:

• Table Detection Modes: Stream and Lattice modes handle a variety of table layouts.

• Output Formats: Supports exporting tables to CSV, JSON, Excel, and Pandas DataFrame.

• Visualization: Allows users to visualize table boundaries and extraction processes.

• Accuracy: Detects multiple tables on a single page and handles complex layouts effectively.

Use Case:

Camelot excels in scenarios where tables are well-structured and have clear boundaries. It is particularly effective for extracting tables from financial reports and invoices (Towards Dev, 2025).

Installation and Example:

To install Camelot, use the following command:

pip install camelot-py\[cv\]

Basic usage for table extraction:

import camelot

tables = camelot.read\_pdf("example.pdf", pages="1")

tables\[0\].to\_csv("output.csv")

2. Tabula-py

Tabula-py is a Python wrapper for the Java-based Tabula library. It is widely used for extracting tables from PDFs, especially multi-page documents.

Key Features:

• Java Backend: Utilizes Tabula’s Java library for robust PDF processing.

• Multi-Page Support: Extracts tables across multiple pages in a single operation.

• Export Options: Outputs data in formats like CSV, JSON, and Pandas DataFrame.

Use Case:

Tabula-py is ideal for extracting tables from large, multi-page PDFs, such as research papers and government reports (GeeksforGeeks, 2023).

Installation and Example:

To install Tabula-py, use:

pip install tabula-py

Basic usage for table extraction:

from tabula import read\_pdf

df = read\_pdf("example.pdf", pages="all")

df.to\_csv("output.csv")

3. Pdfplumber

Pdfplumber is renowned for its precision and flexibility in handling complex table structures. It provides detailed control over the extraction process, making it suitable for non-standard table layouts. It can also be used for text extraction.

Key Features:

• High Precision: Accurately extracts intricate table structures.

• Detailed Control: Offers fine-grained control over the extraction process.

• Versatility: Supports both text and table extraction.

• Output Options: Exports data as CSV, JSON, or Pandas DataFrame.

Use Case:

Pdfplumber is best suited for extracting tables from PDFs with irregular or nested table layouts, such as legal documents and academic papers. It can also be used for accurate text extraction in these complex documents (Unstract Blog, 2024).

Installation and Example:

To install Pdfplumber:

pip install pdfplumber

Basic usage for table extraction:

import pdfplumber

with pdfplumber.open("example.pdf") as pdf:
     page = pdf.pages[0]
     table = page.extract_table()

     print(table)

For text extraction:

import pdfplumber

with pdfplumber.open("example.pdf") as pdf:
    for page in pdf.pages:
        text = page.extract_text()
        print(text)

4. PyMuPDF (Fitz)

PyMuPDF is a high-performance library for rendering and extracting content from PDFs. With version 1.23.0, it introduced table recognition capabilities. It is also good at text extraction.

Key Features:

• Lightweight: No mandatory dependencies, making it easy to install and use.

• Table Recognition: Identifies and extracts tables programmatically.

• Multi-Format Support: Handles PDFs, XPS, and e-books.

• Text Extraction: Can efficiently extract text from PDFs.

Use Case:

PyMuPDF is ideal for extracting tables from PDFs with minimal graphical elements or for use cases requiring high-speed processing. It is also suitable for quick text extraction from PDFs (Artifex Blog, 2023).

Installation and Example:

To install PyMuPDF:

pip install pymupdf

Basic usage for table extraction:

import fitz

doc = fitz.open("example.pdf")

page = doc\[0\]

table = page.find_tables()

for tab in table:
    print(tab.extract())

For text extraction:

import fitz

doc = fitz.open("example.pdf")
for page in doc:
    text = page.get_text()
    print(text)

5. PDFMiner.six

PDFMiner.six is a community-maintained fork of the original PDFMiner library. It is primarily a text extraction tool but can also extract tables with some customization.

Key Features:

• Text Parsing: Extracts text and metadata from PDFs.

• Customizable: Requires manual configuration for table extraction.

Use Case:

PDFMiner.six is suitable for developers who need a highly customizable solution for extracting both text and tables (LibHunt, 2023).

Installation and Example:

To install PDFMiner.six:

pip install pdfminer.six

Basic usage for text extraction:

from pdfminer.high\_level import extract\_text

text = extract\_text("example.pdf")

print(text)

Comparison of Libraries

Conclusion

Extracting text and parsing tables from PDFs in Python is no longer a daunting task, thanks to the wide range of libraries available. Each library discussed—Camelot, Tabula-py, Pdfplumber, PyMuPDF, and PDFMiner.six—has unique strengths and use cases.

For simple, well-structured tables, Camelot is an excellent choice. If you are working with multi-page PDFs for table extraction, Tabula-py is highly effective. For complex or irregular layouts, whether for table or text extraction, Pdfplumber offers unmatched precision and control. Developers seeking lightweight solutions for both text and table extraction can opt for PyMuPDF, while those requiring high customization for text and table extraction should consider PDFMiner.six.

By selecting the right library for your specific needs, you can efficiently extract text and tabular data from PDFs and unlock valuable insights.

📖See Also

• Undatas-io-2025-New-Upgrades-and-Features
• UndatasIO-Feature-Upgrade-Series1-Layout-Recognition-Enhancements
• UndatasIO-Feature-Upgrade-Series2-OCR-Multilingual-Expansion
• Undatas-io-Feature-Upgrade-Series3-Advanced-Table-Processing-Capabilities
• Undatas-io-2025-New-Upgrades-and-Features-French
• Undatas-io-2025-New-Upgrades-and-Features-Korean