BUG: Raise TypeError for mismatched signed/unsigned dtypes in IntervalIndex.from_arrays #62376
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…lIndex.from_arrays
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@jbrockmendel please review these changes when you get a chance. |
pandas/core/indexes/interval.py
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| copy: bool = False, | ||
| dtype: Dtype | None = None, | ||
| ) -> IntervalIndex: | ||
| # Check for mismatched signed/unsigned integer dtypes |
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i think it would make more sense to do this on the IntervalArray.from_arrays method
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Thanks! I will update changes in IntervalArray.from_arrays
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@jbrockmendel I’ve updated this as per your suggestions. |
pandas/core/arrays/interval.py
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| ) -> Self: | ||
| # Check for mismatched signed/unsigned integer dtypes | ||
| left_dtype = getattr(left, "dtype", None) | ||
| right_dtype = getattr(right, "dtype", None) |
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i think putting this after the _maybe_convert_platform_interval calls would be more robust. e.g. if one is a list and the other is uint64?
Also is it just int vs uint we care about, or also e.g. int32 vs int64?
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We are checking int vs unit .
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is there a reason not to move this to after the _maybe_convert_platform_interval calls?
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no, i have updated, also added whatsnew note, please let me know if this needs improvement.
pandas/core/arrays/interval.py
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| # Check for mismatched signed/unsigned integer dtypes | ||
| left_dtype = getattr(left, "dtype", None) | ||
| right_dtype = getattr(right, "dtype", None) |
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the getattr should be unnecessary. the attribute should always be there now that this is moved to after
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thanks, got it. i have updated the code and removed none check for dtype.
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@jbrockmendel could you please check if this CI failure is related to the changes in this PR?
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I'm pretty sure that's affecting all PRs and is unrelated to this. |
pandas/core/arrays/interval.py
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| if ( | ||
| left_dtype.kind in "iu" | ||
| and right_dtype.kind in "iu" | ||
| and left_dtype.kind != right_dtype.kind |
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can't we just compare if left.dtype != right.dtype at this point?
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yes, this will be more clear . i will update with this .
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@jbrockmendel i guess we should not use if left.dtype != right.dtype as this will only restrict to int64 vs uint64 and cause failure in other cases . should i revert the changes to previous one.
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what cases? im pretty sure we always want matching dtypes
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=========================== short test summary info ============================
FAILED pandas/tests/indexes/categorical/test_astype.py::TestAstype::test_astype - TypeError: Left and right arrays must have matching dtypes. Got float64 and int64.
FAILED pandas/tests/indexes/interval/test_constructors.py::TestFromArrays::test_mixed_float_int[int64-float64] - TypeError: Left and right arrays must have matching dtypes. Got int64 and float64.
FAILED pandas/tests/indexes/interval/test_constructors.py::TestFromArrays::test_mixed_float_int[float64-int64] - TypeError: Left and right arrays must have matching dtypes. Got float64 and int64.
FAILED
got these these check fails, after applying changes.
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Looks like it is casting mixed int/float to float/float in ensure_simple_new_inputs. So putting this check after that should do the trick
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sure, that make sense.
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just to confirm before applying changes , i should move these check after ensure_simple_new_inputs, or should I use a strict if left.dtype != right.dtype check after that step?
| dtype=dtype, | ||
| ) | ||
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| # Check for mismatched signed/unsigned integer dtypes after casting |
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im confused as to why this affects from_arrays, since that goes through simple_new and not __new__
2 participants
This PR adds a check in I
ntervalIndex.from_arraysto raise aTypeErrorwhen integer arrays have mismatched signedness. Includes a unit test to verify the behavior.Please let me know if my approach or fix needs any improvements . I’m open to feedback and happy to make changes based on suggestions.
Thankyou !