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.
We recommend using isin
instead of in1d
for new code.
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
isin
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]) >>> 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])
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Licensed under the NumPy License.
https://docs.scipy.org/doc/numpy-1.14.2/reference/generated/numpy.in1d.html