numpy.intersect1d(ar1, ar2, assume_unique=False, return_indices=False)[source]
Find the intersection of two arrays. 返回两个数组中共同的元素 Return the sorted, unique values that are in both of the input arrays.
注意：是排序后的 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. 默认是False，如果是True，假定输入的数组中元素
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. 默认是False，如果是True，返回共同元素的索引位置，因为返回共同元素是排序后的，所以索引位置是排序后的元素位置。
如果共同元素在一个数组中有多次出现，只返回第一次出现的索引位置
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.
>>> 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]))
