Extract PDF Tables to Excel with Python | Split by Header into Multiple Sheets
Chapters
00:00 – Introduction: The 107-Page PDF Extraction Challenge
01:00 – Project Goal: Extract PDF Tables into Multi-Sheet Excel
01:30 – Code Overview: PyMuPDF with Helper Functions
02:00 – Explaining get_table_boundaries() Function
04:00 – Explaining get_column_x_coordinate() Function
04:30 – Main Script: Opening PDF & Defining Columns
06:00 – Creating Page-to-Header Mapping for Grouping
08:30 – Main Loop: Processing Pages 5 to 107 with PyMuPDF
10:30 – Smart Table Detection Logic (Unit vs. Season Tables)
12:00 – Extracting Tables with find_tables() Method
14:00 – Data Cleaning & Converting Strings to Numeric
15:30 – Grouping Extracted Tables & Writing to Excel Sheets
16:30 – Final Output: Organized 3-Sheet Excel File
Struggling with large, messy PDFs full of tables? 📑 In this advanced Python tutorial, I’ll show you how to extract tables from a 107-page PDF and automatically split them into multiple Excel sheets grouped by their headers.
Using PyMuPDF (fitz) with smart table detection, we’ll solve common challenges like:
✔️ Tables spanning across multiple pages
✔️ PDFs with no borders or gridlines
✔️ Dynamic headers that change by section
✔️ Data cleaning & converting values into numeric formats
✔️ Grouping results by header and exporting to Excel sheets
By the end, you’ll have a robust, reusable Python script that transforms unstructured PDF data into clean, structured Excel files — saving hours of manual work. 🚀
#Python #DataExtraction #PDF #Automation #PyMuPDF #Excel #DataAnalysis #PythonTutorial
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