Python PDF Table Extractor
Budget / Salary€8–30
TypeFreelance project
LocationRemote
Posted3 hours ago
I have a set of PDFs that contain nothing but text-based tables. Their filenames are completely random, so the script will need to scan a given folder (and its sub-folders, if I point it there) and process everything it finds.
The deliverable I need is a clean, well-commented Python script that:
• Reads every PDF in the target directory, detects each table, and exports all rows into a single Excel workbook.
• Removes any duplicate header rows as it goes, so the final sheet shows each column header only once at the top.
• Appends data from every file in order of discovery; file name or source page can be logged in an extra column for traceability.
• Runs from the command line with a simple argument such as python extract_tables.py --path /my/pdfs.
• Uses reliable open-source libraries suited to text-based tables (Camelot, tabula-py, pdfplumber, pandas or similar—happy with your preference as long as it is pip-installable).
Acceptance criteria
1. Running the script on a sample folder I supply produces one .xlsx file containing all rows with no duplicate headers.
2. No data loss: numeric and text values must match the source PDFs exactly.
3. Clear README explaining prerequisites and usage.
If you have experience automating PDF-to-Excel workflows in Python, this should be straightforward. Let me know which library you would use and roughly how long you expect the task to take.
The deliverable I need is a clean, well-commented Python script that:
• Reads every PDF in the target directory, detects each table, and exports all rows into a single Excel workbook.
• Removes any duplicate header rows as it goes, so the final sheet shows each column header only once at the top.
• Appends data from every file in order of discovery; file name or source page can be logged in an extra column for traceability.
• Runs from the command line with a simple argument such as python extract_tables.py --path /my/pdfs.
• Uses reliable open-source libraries suited to text-based tables (Camelot, tabula-py, pdfplumber, pandas or similar—happy with your preference as long as it is pip-installable).
Acceptance criteria
1. Running the script on a sample folder I supply produces one .xlsx file containing all rows with no duplicate headers.
2. No data loss: numeric and text values must match the source PDFs exactly.
3. Clear README explaining prerequisites and usage.
If you have experience automating PDF-to-Excel workflows in Python, this should be straightforward. Let me know which library you would use and roughly how long you expect the task to take.
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