52 lines
1.7 KiB
ReStructuredText
52 lines
1.7 KiB
ReStructuredText
.. _faq:
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Frequently Asked Questions
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==========================
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This part of the documentation answers some common questions. To add questions, please open an issue `here <https://github.com/camelot-dev/camelot/issues/new>`_.
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How to reduce memory usage for long PDFs?
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-----------------------------------------
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During table extraction from long PDF documents, RAM usage can grow significantly.
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A simple workaround is to divide the extraction into chunks, and save extracted data to disk at the end of every chunk.
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For more details, check out this code snippet from `@anakin87 <https://github.com/anakin87>`_:
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::
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import camelot
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def chunks(l, n):
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"""Yield successive n-sized chunks from l."""
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for i in range(0, len(l), n):
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yield l[i : i + n]
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def extract_tables(filepath, pages, chunks=50, export_path=".", params={}):
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"""
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Divide the extraction work into n chunks. At the end of every chunk,
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save data on disk and free RAM.
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filepath : str
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Filepath or URL of the PDF file.
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pages : str, optional (default: '1')
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Comma-separated page numbers.
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Example: '1,3,4' or '1,4-end' or 'all'.
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"""
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# get list of pages from camelot.handlers.PDFHandler
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handler = camelot.handlers.PDFHandler(filepath)
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page_list = handler._get_pages(filepath, pages=pages)
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# chunk pages list
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page_chunks = list(chunks(page_list, chunks))
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# extraction and export
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for chunk in page_chunks:
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pages_string = str(chunk).replace("[", "").replace("]", "")
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tables = camelot.read_pdf(filepath, pages=pages_string, **params)
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tables.export(f"{export_path}/tables.csv")
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