Should a dtype=object array be treated more like Python lists for type detection/coercion reasons? Currently, they are treated quite differently:
>>> import numpy as np >>> np.isfinite([1,2,3]) array([ True, True, True]) >>> np.isfinite(np.asarray([1,2,3], dtype=object)) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' The reason I ask is that we ran into something similar when trying to pass wrappers around Julia arrays to Python. A Julia `Any[]` array is much like a Python list or a Numpy `object` array, and exposed in Python as a subtype of MutableSequence, but we found that it was treated by NumPy as more like a Numpy `object` array than a Python list (https://github.com/JuliaPy/PythonCall.jl/issues/486). Would it be desirable to treat a 1d Numpy `object` array more like a Python `list`? Or is there a way for externally defined types to opt-in to the `list` behavior? (I couldn't figure out in the numpy source code where `list` is being special-cased?) _______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
