`numpy:intersect1d`

source: /numpy/lib/_arraysetops_impl.py :666

Signature

   def     intersect1d (    ar1    ,    ar2    ,    assume_unique    =  False   ,    return_indices    =  False     )

Summary

Find the intersection of two arrays.

Extended Summary

Return the sorted, unique values that are in both of the input arrays.

Parameters

ar1, 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.

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.

Examples

import numpy as np
np.intersect1d([1, 3, 4, 3], [3, 1, 2, 1])

✓

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]))

✓

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
xy, x[x_ind], y[y_ind]

✓

Aliases

numpy.intersect1d