cupynumeric.isin#

cupynumeric.isin( element: Any, test_elements: Any, assume_unique: bool = False, invert: bool = False, kind: Literal['sort', 'table'] | None = None, ) → ndarray#

Calculates element in test_elements, broadcasting over element only. Returns a boolean array of the same shape as element that is True where an element of element is in test_elements and False otherwise.

Parameters:

element (array_like) – Input array.
test_elements (array_like) – The values against which to test each value of element. This argument is flattened if it is an array or array_like.
assume_unique (bool, optional) – If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.
invert (bool, optional) – If True, the values in the returned array are inverted, as if calculating element not in test_elements. Default is False. np.isin(a, b, invert=True) is equivalent to (but faster than) np.invert(np.isin(a, b)).
kind ({None, 'sort', 'table'}, optional) –
The algorithm to use. This will not affect the final result, but will affect the speed and memory use. The default, None, will select automatically based on memory considerations.
- If ‘sort’, will use a mergesort-based approach. This will have a memory usage of roughly 6 times the sum of the sizes of element and test_elements, not accounting for size of dtypes.
- If ‘table’, will use a lookup table approach similar to a counting sort. This is only available for boolean and integer arrays. This will have a memory usage of the size of element plus the max-min value of test_elements. This tends to be the faster method if the following relationship is true: len(element) >> (max(test_elements)-min(test_elements))

Returns:

isin – Has the same shape as element. The values element[isin] are in test_elements.

Return type:

ndarray, bool