in1d(ar1, ar2, assume_unique=False, invert=False)¶
Test whether each element of a 1-D array is also present in a second array.
LAX-backend implementation of
In the JAX version, the assume_unique argument is not referenced.
Original docstring below.
Returns a boolean array 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,) array_like) – 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.
invert (bool, optional) – If True, the values in the returned array 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)
in1d – The values ar1[in1d] are in ar2.
- Return type
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
np.array([item in b for item in a]). However, this idea fails if ar2 is a set, or similar (non-sequence) container: As
ar2is converted to an array, in those cases
asarray(ar2)is an object array rather than the expected array of contained values.
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]) >>> test[mask] array([0, 2, 0]) >>> mask = np.in1d(test, states, invert=True) >>> mask array([False, True, False, True, False]) >>> test[mask] array([1, 5])