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martindurant avatar martindurant commented on September 17, 2024

Fastparquet only support one-level nested structures (struct, list or map or primitive types) https://fastparquet.readthedocs.io/en/latest/details.html#reading-nested-schema
This is because pandas without arrow doesn't really support anything else either.

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olegsinavski avatar olegsinavski commented on September 17, 2024

Thanks, that is indeed in the docs, so not a bug unfortunately..

@martindurant What do you mean it's not supported by pandas? After parquet is read (with arrow by default), I get a nice numpy array of strings there which I can access, map through etc.

Also, wouldn't it be better to hard-crash on parse failure as opposed to silently producing nans? It took me ages to find this since its a just one column out of many I have in the format. Maybe I should file a separate issue on that though..

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martindurant avatar martindurant commented on September 17, 2024

After parquet is read (with arrow by default)

Either you get a native arrow column with a set of arrays in the background or a numpy array ob objects. fastpython could in theory do the latter, but it would be a lot of work and be really slow loopy python code.

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olegsinavski avatar olegsinavski commented on September 17, 2024

Sounds good! So what do you think about hard-crashing instead of silently ignoring data in this case? I don't think ignoring a specific column would fly in production systems..

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martindurant avatar martindurant commented on September 17, 2024

A warning may be OK, but I don't think we want to force users to list specific columns they want to read just to avoid those they can't

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olegsinavski avatar olegsinavski commented on September 17, 2024

It's probably more frequent to either want all the data (in that case, a user would expect things to be read correctly) or already specify what you want anyway. I haven't seen a library where data is ignored silently just for the syntactic convenience. Also, "ignore_columns" could be an option. But that's just an opinion. Thank you a lot for the very fast library!

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