Working with PDFs can be challenging, especially when you need to extract data from tables or unstructured text. This repository contains a Python tutorial that demonstrates how to convert PDF files to Excel spreadsheets. It focuses on transforming the data in a way that makes it easier for you to analyze, validate, and visualize it.
The code leverages the Sensible API, which allows for fast, precise, and adaptable document data extraction. You'll find the implementation particularly useful if you're interested in getting specific data out of complex PDF documents.
- Converts PDF files to Excel while preserving the data structure
- Employs Sensible API for accurate and fast data extraction
- Explains how to create custom configurations for specific data extraction needs
- Complete guide on prerequisites, API setup, and Python code implementation
The tutorial walks you through:
- Setting up your Python environment
- Creating a Sensible account and generating an API key
- Building custom configurations using SenseML, Sensible's query language
- Writing Python code to perform the data extraction and conversion
We use WHO's weekly COVID-19 situation reports as our sample PDFs to show you how to extract data like new cases, deaths, etc., and save it into an Excel file.
Clone this repository to get started with the code and follow the step-by-step tutorial for an in-depth understanding.
Clone this repository to get started with the code and follow the step-by-step tutorial for an in-depth understanding.
- Clone this repository:
git clone https://github.com/sensible-hq/tutorial-pdf-to-excel.git - Follow the tutorial here.
Feel free to report issues or make pull requests if you have suggestions for improvement. Your feedback is appreciated!
This project is licensed under the MIT License. See the LICENSE.md file for details.