mars.tensor.in1d(ar1: Union[TileableType, ndarray], ar2: Union[TileableType, ndarray, list], assume_unique: bool = False, invert: bool = False)[source]#

Test whether each element of a 1-D tensor is also present in a second tensor.

Returns a boolean tensor the same length as ar1 that is True where an element of ar1 is in ar2 and False otherwise.

We recommend using isin() instead of in1d for new code.

  • ar1 ((M,) Tensor) – Input tensor.

  • ar2 (array_like) – The values against which to test each value of ar1.

  • assume_unique (bool, optional) – If True, the input tensors 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 tensor are inverted (that is, False where an element of ar1 is in ar2 and True otherwise). Default is False. np.in1d(a, b, invert=True) is equivalent to (but is faster than) np.invert(in1d(a, b)).


in1d – The values ar1[in1d] are in ar2.

Return type

(M,) Tensor, bool

See also


Version of this function that preserves the shape of ar1.


Module with a number of other functions for performing set operations on arrays.


in1d can be considered as an element-wise function version of the python keyword in, for 1-D sequences. in1d(a, b) is roughly equivalent to mt.array([item in b for item in a]). However, this idea fails if ar2 is a set, or similar (non-sequence) container: As ar2 is converted to a tensor, in those cases asarray(ar2) is an object tensor rather than the expected tensor of contained values.


>>> import mars.tensor as mt
>>> test = mt.array([0, 1, 2, 5, 0])
>>> states = [0, 2]
>>> mask = mt.in1d(test, states)
>>> mask.execute()
array([ True, False,  True, False,  True])
>>> test[mask].execute()
array([0, 2, 0])
>>> mask = mt.in1d(test, states, invert=True)
>>> mask.execute()
array([False,  True, False,  True, False])
>>> test[mask].execute()
array([1, 5])