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  "description": "Learn how to extract tables from a PDF in Python with Docling and LlamaParse, and compare local parsing against a cloud API on speed and cost.",
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          "text": "\u003cp\u003eBoth Docling and LlamaParse turn a PDF\u0026rsquo;s tables into structured output you can work with. Docling detects tables locally and exports each one to a \u003ca href=\"https://realpython.com/pandas-dataframe/\"\u003epandas DataFrame\u003c/a\u003e, while LlamaParse returns them as HTML blocks inside its Markdown response.\u003c/p\u003e"
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          "text": "\u003cp\u003eDocling is the safer default because nothing leaves your hardware. LlamaParse sends every document to LlamaIndex\u0026rsquo;s servers, so it suits data you\u0026rsquo;re allowed to upload.\u003c/p\u003e"
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          "text": "\u003cp\u003eIn this tutorial\u0026rsquo;s single ten-page run, LlamaParse finished in about 30 seconds because parsing happens in the cloud, while Docling took about 82 seconds on CPU. Real timings depend on your hardware, your network, and the complexity of the PDF.\u003c/p\u003e"
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          "text": "\u003cp\u003eYes. Docling attaches every detected table to the parsed document, and \u003ccode\u003e.export_to_dataframe()\u003c/code\u003e turns a single table into a pandas DataFrame without reparsing the file.\u003c/p\u003e"
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