numpy的isin函数

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numpy.isin(element, test_elements, assume_unique=False, invert=False)

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. See notes for behavior with non-array-like parameters.

  • 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)).

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>>> element = 2*np.arange(4).reshape((2, 2))
>>> element
array([[0, 2],
       [4, 6]])
>>> test_elements = [1, 2, 4, 8]
>>> mask = np.isin(element, test_elements)
>>> mask
array([[ False,  True],
       [ True,  False]], dtype=bool)
>>> element[mask]
array([2, 4])
>>> mask = np.isin(element, test_elements, invert=True)
>>> mask
array([[ True, False],
       [ False, True]], dtype=bool)
>>> element[mask]
array([0, 6])
Because of how array handles sets, the following does not work as expected:

>>> test_set = {1, 2, 4, 8}
>>> np.isin(element, test_set)
array([[ False, False],
       [ False, False]], dtype=bool)
Casting the set to a list gives the expected result:

>>> np.isin(element, list(test_set))
array([[ False,  True],
       [ True,  False]], dtype=bool)