in1d(ar1, ar2, assume_unique=False, invert=False)
Returns a boolean array the same length as :None:None:`ar1` that is True where an element of :None:None:`ar1` is in :None:None:`ar2` and False otherwise.
We recommend using isin
instead of in1d
for new code.
in1d
can be considered as an element-wise function version of the python keyword :None:None:`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 :None:None:`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.
Input array.
The values against which to test each value of :None:None:`ar1`.
If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.
If True, the values in the returned array are inverted (that is, False where an element of :None:None:`ar1` is in :None:None:`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))
.
The values :None:None:`ar1[in1d]` are in :None:None:`ar2`.
Test whether each element of a 1-D array is also present in a second array.
isin
Version of this function that preserves the shape of ar1.
numpy.lib.arraysetops
Module with a number of other functions for performing set operations on arrays.
>>> 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])See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.isin
numpy.in1d
numpy.ma.extras.in1d
numpy.ma.extras.isin
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