intersect1d(ar1, ar2, assume_unique=False, return_indices=False)
Return the sorted, unique values that are in both of the input arrays.
Input arrays. Will be flattened if not already 1D.
If True, the input arrays are both assumed to be unique, which can speed up the calculation. If True but ar1
or ar2
are not unique, incorrect results and out-of-bounds indices could result. Default is False.
If True, the indices which correspond to the intersection of the two arrays are returned. The first instance of a value is used if there are multiple. Default is False.
Sorted 1D array of common and unique elements.
The indices of the first occurrences of the common values in :None:None:`ar1`. Only provided if :None:None:`return_indices` is True.
The indices of the first occurrences of the common values in :None:None:`ar2`. Only provided if :None:None:`return_indices` is True.
Find the intersection of two arrays.
numpy.lib.arraysetops
Module with a number of other functions for performing set operations on arrays.
>>> np.intersect1d([1, 3, 4, 3], [3, 1, 2, 1]) array([1, 3])
To intersect more than two arrays, use functools.reduce:
>>> from functools import reduce
... reduce(np.intersect1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2])) array([3])
To return the indices of the values common to the input arrays along with the intersected values:
>>> x = np.array([1, 1, 2, 3, 4])
... y = np.array([2, 1, 4, 6])
... xy, x_ind, y_ind = np.intersect1d(x, y, return_indices=True)
... x_ind, y_ind (array([0, 2, 4]), array([1, 0, 2]))
>>> xy, x[x_ind], y[y_ind] (array([1, 2, 4]), array([1, 2, 4]), array([1, 2, 4]))See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.ma.extras.intersect1d
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