2.2.2: Slicing NumPy Arrays 切片 NumPy 数组 It’s easy to index and slice NumPy arrays regardless of their dimension,meaning whether they are vectors or matrices. 索引和切片NumPy数组很容易,不管它们的维数如何,也就是说它们是向量还是矩阵。 With one-dimension arrays, we can index a given element...
array([[6,8,5,3,6],[0,4,9,8,2]]) print(" ** 2D NumPy Array Slicing ** ") print(nparr[1, 2:5]) Accessing second array with 0 as starting index and 3 as ending index. print(" ** 2D NumPy Array Slicing with default start for 2nd dimension ** ") print(nparr[1,:3])...
对于多维 NumPy 数组,您可以通过用逗号分隔的切片来对每个维度进行切片。让我们从二维数组开始: arr2d = np.array( [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15]] ) ic(arr2d[1:3]) ic(arr2d[:, 1:3]) ic(arr2d[1:3, 1:3]) row_slice = slice(1,...
result, chunks =slice_array("y","x", ([5,5], [5,5]), (slice(0,7),1))assertchunks == ((5,2),) 开发者ID:rla3rd,项目名称:dask,代码行数:9,代码来源:test_slicing.py 示例4: test_slicing_with_numpy_arrays ▲点赞 1▼ deftest_slicing_with_numpy_arrays():a, bd1 =slice_array(...
The first element of the NumPy array: 1 The second element of the NumPy array: 2 The third element of the NumPy array: 3 Accessing elements of a 2D NumPy Array The syntax for accessing the elements of a 2D NumPy array is as follows: ...
import numpy as np # Step 1: Create a 1D array of 20 elements original_1d_array = np.arange(20) print("Original 1D array:\n", original_1d_array) # Step 2: Reshape the 1D array into a (4, 5) matrix reshaped_matrix = original_1d_array.reshape(4, 5) print("\nReshaped (4, ...
import numpy as np a = np.array([[2,3,4,3,6], [1,2,5,7,5], [3,4,3,3,5], [5,6,3,2,1], [3,1,4,5,6]]) print(a[1:3,1:]) #[[2 5 7 5] [4 3 3 5]] 三维数组 对3 维或更多维数组进行切片遵循与 2D 数组相同的逻辑, ...
#Numpy array np1 = np.random.rand(3,4) print(np1) my_torch = my_torch.reshape(2,5) print(my_torch) #2d tensor tensor_2d = torch.randn(3,4) print(tensor_2d) #Reshape if we don't know number of items my_torch2 = torch.arange(10) print(my_torch2) #3d tensor tensor_3d =...
Striding in the positive and negative direction can also be achieved with the same semantics as numpy arrays. This can be done over multiple dimensions. reversed = array[::-1] every_second = array[::2] every_second_reversed = array[::-2] quarter_resolution = image[::2, ::2] ...
arr_2d[0:2,1:3]Out:array([[1,2],[5,6]]) 代码块 预览复制 上述案例在axis=0方向上选择了第 0 和第 1 行,在axis=1方向上选择了第 1 列和第 2 列,两种切片方向的聚焦部分即为切片索引的结果。 需要指出的是,如果切片只有冒号,表示选取该方向的整个轴。例如,利用该方法,可以对二维数组进行列方向...