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...
# 创建二维数组array_2d=np.array([[0,1,2,3,4],[5,6,7,8,9],[10,11,12,13,14],[15,16,17,18,19],[20,21,22,23,24]])# 切片提取前两行和前三列slice_2d=array_2d[0:2,0:3]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...
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]...
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...
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...
# 数组索引print("第二个元素:",array_1d[1])# 切片操作slice_array=array_2d[:,0]# 取二维数组的第一列print("第一列:",slice_array) 1. 2. 3. 4. 5. 6. 4. 使用条件筛选数组 可以使用条件表达式对数组进行筛选,这使得数据处理变得简洁明了。
array([2, 3, 1, 0]) >>> type(x) <class 'numpy.ndarray'> >>> x.dtype dtype('int32') >>> x = np.array((1, 2, 3)) # 元组方式 >>> x array([1, 2, 3]) >>> x = np.array([[ 1.+0.j, 2.+0.j], [ 0.+0.j, 0.+0.j], [ 1.+1.j, 3.+0.j]]) # ...
arr = np.array(n) arr[start:end] arr[start:end:step] 2,浅拷贝(视图) 数组切片是原始数组的浅拷贝,也叫视图,修改切片中的原始,会直接修改原始数据。 对于一维数组,数组切片上的任何修改都会直接修改原始数组: arr = np.arange(10) arr_slice=arr[5:8] ...
a_slice=a[2:6]a_slice[1]=1000a# 原始ndarray也被修改！输出：array([ 1, 5, -1,...
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 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) ...