feat: reduce np.ndarray #1462
feat: reduce np.ndarray #1462
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| ) -> tuple[np.ndarray, np.ndarray]: ... | ||
| ) -> tuple[npt.NDArray[Any], npt.NDArray[Any]]: ... |
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is this a chance to make this more precise, like
tuple[np_1d_array[np.intp], np_1d_array[np.intp]]?
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I have no insight into this class / this class method, nor is it documented in any detail (personally I don't even know what "window bounds" means). It would be great if you suggest a few tests, or can we leave it in another PR?
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sure happy to leave it til later!
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| class CategoricalIndex(ExtensionIndex[S1], accessor.PandasDelegate): | ||
| codes: np.ndarray = ... | ||
| codes: npt.NDArray[Any] = ... |
| ) -> tuple[np.ndarray, np.ndarray]: ... | ||
| ) -> tuple[npt.NDArray[Any], npt.NDArray[Any]]: ... |
There was a problem hiding this comment.
I have no insight into this class / this class method, nor is it documented in any detail (personally I don't even know what "window bounds" means). It would be great if you suggest a few tests, or can we leave it in another PR?
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| class CategoricalIndex(ExtensionIndex[S1], accessor.PandasDelegate): | ||
| codes: np.ndarray = ... | ||
| codes: npt.NDArray[Any] = ... |
| mid = _MidDescriptor() | ||
| length = _LengthDescriptor() |
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i'm fairly confused by these , aren't they just float and int respectively? not sure what these descriptors are
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- It seems we cannot overload
property-ies - For
mid, it isfloatforintandfloat, butTimestampforTimestamp, andTimedeltaforTimedelta - For
length, it isfloatforfloat,intforint, butTimedeltaforTimestampandTimedelta
| @property | ||
| def is_monotonic_increasing(self) -> bool: ... | ||
| def clear_mapping(self) -> None: ... | ||
| class IntervalTree(IntervalMixin): ... |
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if the above comment is right, this can probably just be removed?
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class IntervalTree is something implemented in pandas with Cython. I don't understand what's happening with Ctyhon, but would like to leave its presence in the stubs, even though it's empty. Please tell me if you have a good argument to remove it completely.
pandas-stubs/core/base.pyi
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| | np_ndarray_float | ||
| | np_ndarray_complex | ||
| | dict[str, np.ndarray] | ||
| | dict[str, np_1darray[Any]] |
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the general pattern you seem to be following is:
- for function arguments, don't be restrictive on shape
- for return types, be as precise as possible
Given that NumListLike is often used as argument, would this one be better as Any shape?
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for function arguments, don't be restrictive on shape
This is relevant until the end of python 3.10 because it can only use an old version of numpy, which does not give the correct shape upon construction, i.e. np.array([1, 2, 3]) does not give a 1-d array with that old version, in static typing.
would this one be better as Any shape?
Yep dc85974
| mid = _MidDescriptor() | ||
| length = _LengthDescriptor() |
There was a problem hiding this comment.
- It seems we cannot overload
property-ies - For
mid, it isfloatforintandfloat, butTimestampforTimestamp, andTimedeltaforTimedelta - For
length, it isfloatforfloat,intforint, butTimedeltaforTimestampandTimedelta
| @property | ||
| def is_monotonic_increasing(self) -> bool: ... | ||
| def clear_mapping(self) -> None: ... | ||
| class IntervalTree(IntervalMixin): ... |
There was a problem hiding this comment.
class IntervalTree is something implemented in pandas with Cython. I don't understand what's happening with Ctyhon, but would like to leave its presence in the stubs, even though it's empty. Please tell me if you have a good argument to remove it completely.
pandas-stubs/core/base.pyi
Outdated
| | np_ndarray_float | ||
| | np_ndarray_complex | ||
| | dict[str, np.ndarray] | ||
| | dict[str, np_1darray[Any]] |
There was a problem hiding this comment.
for function arguments, don't be restrictive on shape
This is relevant until the end of python 3.10 because it can only use an old version of numpy, which does not give the correct shape upon construction, i.e. np.array([1, 2, 3]) does not give a 1-d array with that old version, in static typing.
would this one be better as Any shape?
Yep dc85974
2 participants
npt.NDArrayto benpt.NDArray[Any]