numpy.in1d(ar1, ar2, assume_unique=False)

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

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

Parameters :

ar1 : array_like, shape (M,)

Input array.

ar2 : array_like

The values against which to test each value of ar1.

assume_unique : bool, optional

If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.

Returns :

mask : ndarray of bools, shape(M,)

The values ar1[mask] are in ar2.

See also

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 1D sequences. in1d(a, b) is roughly equivalent to np.array([item in b for item in a]).

New in version 1.4.0.


>>> test = np.array([0, 1, 2, 5, 0])
>>> states = [0, 2]
>>> mask = np.in1d(test, states)
>>> mask
array([ True, False,  True, False,  True], dtype=bool)
>>> test[mask]
array([0, 2, 0])

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