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I would add high-volume parsing / text processing to the list of bad fits for Python, although I'm not sure if there are native extensions for the different use cases?

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Quite possibly; do you specifically mean NLP work? I'll admit, it's not something I work in myself, spaCy seems to be the go-to high-performance NLP library, and does appear to use C under the hood, but I couldn't say how it performs compared to other languages.

I had SAX-style parsing of XML and XSL transformation as concrete use cases in mind, because that happened to be what I worked with. I believe I went with Node.js at the time, which had a library that was much easier to work with than what was common for Python. Although, I mostly used Microsoft's or Saxon's XSLT processors overall in that job.

Another use case was parsing proprietary, text-based file formats with ancient encodings. I believe I did use Python for that as there wasn't that much data to convert anyway and it just worked.

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