# 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]))

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