ARROW-2135: [Python] Fix NaN conversion when casting from Numpy array#1681
ARROW-2135: [Python] Fix NaN conversion when casting from Numpy array#1681pitrou wants to merge 1 commit into
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For some reason (macro expansion?) these #ifs wouldn't work correctly here, even though NPY_INT64 is defined to NPY_LONG.
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Hmm, actually, that must be because NPY_LONGLONG is not a macro...
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std::fill(null_bitmap_data_, null_bitmap_data_ + null_bytes, 0) is a bit more idiomatic.
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Hmm, perhaps. This is really a copy/paste of NumPyConverter::InitNullBitmap()...
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Possibly time for a subclass then?
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Is there already a test for things like a = [1.0, 2.0, 3.1, np.nan] where a user passes in an integer type?
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You mean for the truncation behavior? Let me look.
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No, I don't think so. I'm not sure we specify the truncation mode anywhere either?
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It looks like it's a hard cast:
In [7]: pa.array([1, 2, 3.190, np.nan], type=pa.int64())
Out[6]:
<pyarrow.lib.Int64Array object at 0x7f537e42dd68>
[
1,
2,
3,
NA
]
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That's fine. Was just wondering.
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inline is redundant here: http://en.cppreference.com/w/cpp/language/inline.
A function defined entirely inside a class/struct/union definition, whether it's a member function or a non-member friend function, is implicitly an inline function.
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I see. This is really using the same convention as the rest of the file, though.
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Hm, so that's also called isnull. Shouldn't that mean v == Py_None?
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Probably needs a test as well since it isn't failing.
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Nice catch :-) I'm not sure how to test it. Defining isnull is necessary for compiling, but that path isn't taken at runtime as object arrays are handled separately.
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At some point we may want to have an STL-compatible view class that makes interacting with iterators constructs in the STL much easier. We have a lot of code that is manually handling iteration using a size/count and a buffer.
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Which iterators are you thinking about? Do you mean the ndarray 1d iterator?
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That's one, though I added begin()/end() for that in #1651.
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By the way, I don't know what that is, but this is required to have the tests pass. Why do we always treat NaT as null but not floating-point NaN? @wesm
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AFAIU There's no other way to interpret NaT other than NULL (unless there's a standard that defines it in a different way than "missing"). nan is part of the IEEE floating point specification (as I'm sure you know) and it has a different meaning than null.
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I addressed some review comments now. |
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AppVeyor build at https://ci.appveyor.com/project/pitrou/arrow/build/1.0.157 |
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Rebased. |
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+1, thanks for cleaning up the int/uint size issues here, much cleaner now
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see ARROW-2298 for adding an option about NaN conversions |
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