[jira] [Commented] (ARROW-16843) [Python][CSV] CSV reader performs unsafe type conversion

["Antoine Pitrou (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/ARROW-16843?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17555150#comment-17555150 ] 

Antoine Pitrou commented on ARROW-16843:
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Note that's not really the same situation: in the first case, you are converting between number types, where you presumably expect the conversion to be exact. In the second case, you are converting from string to number, where it's not unreasonable to accept some possible precision loss.

It's also likely that raising an error would break a non-tiny number of real-world CSV reading use cases.


> [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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