xorbits.numpy.intersect1d#

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.

Parameters

  • 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 ar1 or ar2 are 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).

Returns

  • 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.

See also

numpy.lib.arraysetops

Module with a number of other functions for performing set operations on arrays.

Examples

>>> 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]))

Warning

This method has not been implemented yet. Xorbits will try to execute it with numpy.

This docstring was copied from numpy.