# numpy.intersect1d

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, 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.

  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]))