- xorbits.numpy.intersect1d(ar1, ar2, assume_unique=False, return_indices=False)#
Find the intersection of two arrays.
Return the sorted, unique values that are in both of the input arrays.
ar1 (array_like) – Input arrays. Will be flattened if not already 1D.
ar2 (array_like) – Input arrays. Will be flattened if not already 1D.
assume_unique (bool) – If True, the input arrays are both assumed to be unique, which can speed up the calculation. If True but
ar2are not unique, incorrect results and out-of-bounds indices could result. Default is False.
return_indices (bool) –
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.
New in version 1.15.0(numpy).
intersect1d (ndarray) – Sorted 1D array of common and unique elements.
comm1 (ndarray) – The indices of the first occurrences of the common values in ar1. Only provided if return_indices is True.
comm2 (ndarray) – The indices of the first occurrences of the common values in ar2. Only provided if return_indices is True.
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()
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]))
This method has not been implemented yet. Xorbits will try to execute it with numpy.
This docstring was copied from numpy.