Import numpy AS NP '' ' to create an array of 1: np.array ([]) type 2 array object: type () 3. Data Type: a.dtype type shape 4. arrays: (4,2, 3) bytes of each array element 5. define: array.itemsize '' ' # # Create a three-dimensional array a = [[1,2,1], [l, 3,4 ]] B = [[. 5 , 6,1], [1,7,8 ]] C = [[9,10,1], [11,12,1 ]] D = [[, 13, 14], [, 15, 16 ]] array_test = np.array ([A, B, C, D], DTYPE = ' float64 ' ) Print (array_test.shape) Print (array_test.ndim) Print (array_test.size) Print (array_test) '' ' (4, 2, 3) 3 24 Explanation: Type Shape: (4, 2, 3) axes axes: the axis-dimensional array is called, the number of axes of three-dimensional array is referred to herein rank: shape three integer array length size: (total number of elements in the hierarchy) = 24 [3 * 2 * 4] homogeneity: a [4 wherein each element are the same type, such as a], [2 two elements of an element of a per are the same types, such as [1,2,1]], [3 three elements [1,2,3] an element of] Therefore, a Shape (4,2,3) '' ' Print (type (array_test)) '' ' array class: <class' numpy.ndarray '> ' '' Print (array_test.dtype) '' ' data type: Int32 ' '' Print (array_test.itemsize) # #. 4 # # Create the like difference array arange_array = np.arange (0,12) .reshape (3,4- ) linspace_array = np.linspace (0,10,5).reshape(5,1) Print ( ' axes ' , arange_array.ndim) Print ( ' size: ' , linspace_array.size) Print (arange_array) Print (linspace_array) ' '' axes 2 size:. 5 [[2. 3. 1 0] [. 4. 5. 6 7] [8 9 10 11]] [[0.5] [2.5] [5] [7.5] [10]] '' ' # # Create a random number sequence # D random number sequence rand_1d = np.random.random ( . 4 ) Print (rand_1d) '' ' [0.08525778 0.12143347 .56587575 .83590871] ' '' #The one dimensional into 2D Print (rand_1d.reshape (2,2 & )) '' ' [[0.08525778 0.12143347] [.56587575 0.83590871]] ' '' # # passed directly generate multidimensional array shape can Print (np.random .random ((3,3 ))) '' ' [[0.56859463 0.98880884 .52755145] [0.26863131 0.71508455 .22285108] [0.31286731 0.2290022 0.7223287]] ' ''