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https://issues.apache.org/jira/browse/ARROW-16843?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17556254#comment-17556254
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Thomas Buhrmann commented on ARROW-16843:
-----------------------------------------
Yes, fair enough, datasets of multiple CSVs may be awkward. As to how common it
is to have 64-bit numbers, I guess the most common use case would be IDs. But
really those are often treated best as strings anyways, i.e., they're not
usually used to calculate with. So as long as there's a way (configuration) for
them not to be converted to floats that would probably be sufficient. (In fact,
even in the use case I mentioned earlier, Twitter IDs, the official API
recommends to treat them as strings:
[https://developer.twitter.com/en/docs/twitter-ids).]
> [Python][CSV] CSV reader performs unsafe type conversion
> --------------------------------------------------------
>
> Key: ARROW-16843
> URL: https://issues.apache.org/jira/browse/ARROW-16843
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 8.0.0
> Reporter: Thomas Buhrmann
> Priority: Major
>
> Hi, I've noticed that although pa.scalar and pa.array behave correctly when
> given the largest possible (uint64) value (i.e. they fail correctly when
> trying to cast to float e.g.), the CSV reader happily converts strings
> representing uint64 values to float (see example below). Is this intended?
> Would it be possible to have a safe-conversion-only option?
> The problem is that at the moment the only safe option to read a CSV whose
> types are not known in advance is to read without any conversion (string
> only) and perform the type inference oneself.
> It would be ok if Uint64 types couldn't be inferred, as long as the
> corresponding columns aren't coerced in a destructive manner to float. I.e.,
> if they were left as string columns, one could then implement a custom
> conversion, while still benefiting from the correct and automatic conversion
> of the remaining columns.
>
> The following correctly rejects the float type for uint64 values:
> {code:java}
> import pyarrow as pa
> uint64_max = 18_446_744_073_709_551_615
> type_ = pa.uint64()
> uint64_scalar = pa.scalar(uint64_max, type=type_)
> uint64_array = pa.array([uint64_max], type=type_)
> try:
> f = pa.scalar(uint64_max, type=pa.float64())
> except Exception as exc:
> print(exc)
>
> try:
> f = pa.scalar(uint64_max // 2, type=pa.float64())
> except Exception as exc:
> print(exc) {code}
> {code:java}
> >> PyLong is too large to fit int64
> >> Integer value 9223372036854775807 is outside of the range exactly
> >> representable by a IEEE 754 double precision value
> {code}
> The CSV reader, on the other hand, doesn't infer UInt64 types (which is fine,
> as documented here
> [https://arrow.apache.org/docs/cpp/csv.html#data-types),|https://arrow.apache.org/docs/cpp/csv.html#data-types)]
> but does coerce values to float which shouldn't be coercable according to
> above examples:
> {code:java}
> import io
> csv = "int64,uint64\n0,0\n4294967295,18446744073709551615"
> tbl = pa.csv.read_csv(io.BytesIO(csv.encode("utf-8")))
> print(tbl.schema)
> print(tbl.column("uint64")[1] == uint64_scalar)
> print(tbl.column("uint64")[1].cast(pa.uint64())) {code}
> {code:java}
> int64: int64
> uint64: double
> False
> 0
> {code}
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