# numpy.intersect1d¶

`numpy.``intersect1d`(ar1, ar2, assume_unique=False, return_indices=False)[source]

Find the intersection of two arrays.

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. 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. 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.

`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()
```

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]))

numpy.in1d

numpy.isin