Using Tabula to Extract Table Data With Mixed Rows and Columns

What will you learn?

In this tutorial, you will master the art of efficiently extracting table data with mixed rows and columns using Tabula in Python.

Introduction to the Problem and Solution

Dealing with PDF files that contain tables with mixed rows and columns can pose a challenge when it comes to accurately extracting data. Enter Tabula � a robust Python tool that simplifies the process of parsing tables from PDFs seamlessly. By harnessing the power of Tabula, you can effortlessly extract tabular data even from complex layouts.


# Import necessary libraries
import tabula

# Specify the file path of the PDF document
file_path = "path_to_your_pdf_file.pdf"

# Use Tabula to extract table data into a DataFrame
dfs = tabula.read_pdf(file_path, pages='all', lattice=True)

# Display the extracted DataFrames (tables)
for df in dfs:

# For more detailed configuration options, refer to the official documentation: [Tabula Documentation](

# Copyright PHD

Note: Prior to running this code snippet, make sure to install tabula-py by executing pip install tabula-py.


Here is a breakdown of how Tabula’s read_pdf() function works:

  • The pages=’all’ parameter extracts tables from all pages of the PDF.
  • Setting lattice=True aids in accurately detecting cell boundaries for tables with mixed rows and columns.
  • Each extracted table is stored as a DataFrame, providing flexibility for further processing or analysis based on your needs.
    1. How accurate is Tabula in extracting complex tables?

      • While Tabula offers good accuracy in extracting complex tables, manual verification may be necessary in certain scenarios.
    2. Can I customize the extraction process for specific table structures?

      • Yes, Tabula provides various configuration options allowing customization such as specifying area coordinates or adjusting detection parameters for better results.
    3. Is it possible to handle multi-page tables using Tabula?

      • Absolutely! By defining page ranges or utilizing features like pages=’all’, multi-page tables can be effectively extracted.
    4. Does Tabula support exporting extracted data into different formats?

      • Presently, direct export capabilities are limited; however, you can easily convert DataFrames obtained from Tabula into desired formats using pandas functions like .to_csv().
    5. How does ‘lattice’ mode help in handling mixed rows and columns?

      • The ‘lattice’ mode aids in accurately detecting grid lines within complex table layouts, facilitating better parsing of cells.
    6. Can we integrate Tablua with other Python libraries for advanced analysis?

      • Certainly! Extracted tabular data can seamlessly integrate with popular libraries like pandas or NumPy for extensive analysis tasks beyond basic extraction needs.
    7. Are there any known limitations while working with large-sized PDFs?

      • Processing very large PDF files may lead to memory issues; it’s advisable to split such files if feasible or explore server-based solutions for scalability.
    8. Can I contribute or report issues related to Tablua’s functionality?

      • Yes! As an open-source project on GitHub, contributions through pull requests and issue reporting are encouraged by actively involved developers.

To wrap up, Tabua emerges as a powerful solution for extracting tabular data from PDFs featuring mixed row and column layouts. Delve into its capabilities and configuration options to unlock seamless extraction possibilities!


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