numpy.in1d(ar1, ar2, assume_unique=False, invert=False)[source]
Test whether each element of a 1-D 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 : (M,) array_like Input array. ar2 : array_like The values against which to test each value of 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 New in version 1.8.0. |
---|---|
Returns: |
in1d : (M,) ndarray, bool The values |
See also
numpy.lib.arraysetops
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 ar2
is 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], dtype=bool) >>> test[mask] array([0, 2, 0]) >>> mask = np.in1d(test, states, invert=True) >>> mask array([False, True, False, True, False], dtype=bool) >>> test[mask] array([1, 5])
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https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.in1d.html