Extract PDF Tables in 3 Lines with Camelot: A Python Guide

Extracting tables from PDF files is often a painful task, especially for researchers and analysts who need the data in a usable format. Camelot, a Python library, simplifies this process by allowing users to read PDF tables and convert them directly into Pandas DataFrames with just three lines of code.

PDFs are widely used for formal documents, but their fixed layout makes table extraction difficult. Camelot addresses this challenge by providing a straightforward API to locate and extract tabular data from text‑based PDFs.

Project address : https://github.com/camelot-dev/camelot

What is Camelot? Camelot is a Python tool designed to extract table data from PDF files and output it in common formats such as CSV, JSON, Excel, HTML, or SQLite.

Typical usage mirrors Pandas file handling:

import camelot
tables = camelot.read_pdf('foo.pdf')  # similar to pandas.read_csv
print(tables[0].df)  # get a pandas DataFrame!
tables.export('foo.csv', f='csv', compress=True)  # export to CSV, JSON, etc.
print(tables[0].parsing_report)
# {'accuracy': 99.02, 'whitespace': 12.24, 'order': 1, 'page': 1}

The library handles merged cells by inserting empty rows, providing a stable output.

Installation methods

Conda (simplest): conda install -c conda-forge camelot-py Pip (most popular): pip install camelot-py[cv] From source:

git clone https://www.github.com/camelot-dev/camelot
cd camelot
pip install ".[cv]"