这是一个非常通用的方法：它适用于嵌套列表，数组列表列表-
不管这些数组的形状是不同还是相等。当数据聚集在一个单独的阵列中时，它也是有效的，这实际上是最棘手的情况。（到目前为止发布的其他方法在这种情况下不起作用。）
让我们从困难的情况开始，一个大数组：
# create example# pick outer shape and inner shape>>> osh, ish = (2, 3), (2, 5)# total shape>>> tsh = (*osh, *ish)# make data>>> data = np.arange(np.prod(tsh)).reshape(tsh)>>># recalculate inner shape to cater for different inner shapes# this will return the consensus bit of all inner shapes>>> ish = np.shape(data)[len(osh):]>>> # block them>>> data_blocked = np.frompyfunc(np.reshape(data, (-1, *ish)).__getitem__, 1, 1)(range(np.prod(osh))).reshape(osh)>>> # admire>>> data_blockedarray([[array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]), array([[10, 11, 12, 13, 14], [15, 16, 17, 18, 19]]), array([[20, 21, 22, 23, 24], [25, 26, 27, 28, 29]])], [array([[30, 31, 32, 33, 34], [35, 36, 37, 38, 39]]), array([[40, 41, 42, 43, 44], [45, 46, 47, 48, 49]]), array([[50, 51, 52, 53, 54], [55, 56, 57, 58, 59]])]], dtype=object)
使用OP的示例，它是数组列表的列表：
>>> x = np.array([[1, 2, 3], [4, 5, 6]])>>> y = np.array([[7, 8, 9], [0, 1, 2]])>>> u = np.array([[3, 4, 5], [6, 7, 8]])>>> v = np.array([[9, 0, 1], [2, 3, 4]])>>> data = [[x, y], [u, v]]>>> >>> osh = (2,2)>>> ish = np.shape(data)[len(osh):]>>> >>> data_blocked = np.frompyfunc(np.reshape(data, (-1, *ish)).__getitem__, 1, 1)(range(np.prod(osh))).reshape(osh)>>> data_blockedarray([[array([[1, 2, 3], [4, 5, 6]]), array([[7, 8, 9], [0, 1, 2]])], [array([[3, 4, 5], [6, 7, 8]]), array([[9, 0, 1], [2, 3, 4]])]], dtype=object)
还有一个具有不同形状子数组的示例（请注意
v.T）：
>>> data = [[x, y], [u, v.T]]>>> >>> osh = (2,2)>>> ish = np.shape(data)[len(osh):]>>> data_blocked = np.frompyfunc(np.reshape(data, (-1, *ish)).__getitem__, 1, 1)(range(np.prod(osh))).reshape(osh)>>> data_blockedarray([[array([[1, 2, 3], [4, 5, 6]]), array([[7, 8, 9], [0, 1, 2]])], [array([[3, 4, 5], [6, 7, 8]]), array([[9, 2], [0, 3], [1, 4]])]], dtype=object)
