slice_cols = array_2d[:, 0:3] print(slice_cols) 选择每隔一行的元素,从第一列到第三列: python slice_step = array_2d[::2, 0:3] print(slice_step) 选择特定的行和列,例如第一行第三列和第三行第一列: python specific_elements = array_2d[[0, 2], [2, 0]] print(specific_elements...
0:3]print("二维切片示例:\n",slice_2d)# 输出:# [[0 1 2]# [5 6 7]]# 选择所有行的某几列slice_2d_cols=array_2d[:,1:4]print("二维切片选择列示例:\n",slice_2d_cols)# 输出:
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 by its position, keeping in mind that indice...
arr[5:8] =12array([0,1,2,3,4,12,12,12,8,9]) # 切片可以修改原数组的值 arr_slice = arr[5:8] arr_slice[1] =12345arr array([0,1,2,3,4,12,12345,12,8,9]) # 构建二维数组 arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) arr2d[2] array([7,8,9]...
[5:8] arr_slice[1] = 12345 arr array([ 0, 1, 2, 3, 4, 12, 12345, 12, 8, 9]) # 构建二维数组 arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) arr2d[2] array([7, 8, 9]) # index 二维数组 arr2d[0][2] 3 # index二维数组 arr2d[0, 2] 3 # ...
Example: 2D NumPy Array Slicing importnumpyasnp# create a 2D arrayarray1 = np.array([[1,3,5,7], [9,11,13,15], [2,4,6,8]])# slice the array to get the first two rows and columnssubarray1 = array1[:2, :2]# slice the array to get the last two rows and columnssubarray2...
切片访问 arr[5:8] array([5, 6, 7]) # 切片修改 arr[5:8] = 12 array([ 0, 1, 2, 3, 4, 12, 12, 12, 8, 9]) # 切片可以修改原数组的值 arr_slice = arr[5:8] arr_slice[1] = 12345 arr array([ 0, 1, 2, 3, 4, 12, 12345, 12, 8, 9]) # 构建二维数组 arr2d =...
array([ -1, 2000, -1, 7])如果想复制ndarray的数据，需要使用copy方法。another_slice=a[...
array([5,6,7]) 切片赋值操作 1.切片赋一个值对应原来数组中的值也会变 >>>arr[5:8]=12>>>arr array([0,1,2,3,4,12,12,12,8,9]) >>>importnumpyasnp>>>arr=np.arange(10)>>>arr_slice=arr[5:8]>>>arr_slice[0]=-1>>>arr_slice ...
numpy array 求索引值 numpy根据索引取值 文章目录 slice() 冒号分隔start:stop:step 整数数组索引 布尔索引 slice() ndarray 数组可以基于 0 - n 的下标进行索引,切片对象可以通过内置的 slice 函数,并设置 start, stop 及 step 参数进行,从原数组中切割出一个新数组。