arr=np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])print("Original array from numpyarray.com:")print(arr)sliced=arr[:,::2]# 选择所有行,每隔一列print("Sliced array:")print(sliced)flattened_sliced=sliced.flatten()print("Flattened sliced array:")print(flattened_sliced) Python Copy...
importnumpyasnp# 创建一维数组arr1d=np.array([1,2,3,4,5])print("1D array:",arr1d)# 创建二维数组arr2d=np.array([[1,2,3],[4,5,6]])print("2D array:\n",arr2d)# 创建三维数组arr3d=np.array([[[1,2],[3,4]],[[5,6],[7,8]]])print("3D array:\n",arr3d) Python Copy...
两者的区别在于返回拷贝(copy)还是返回视图(view),numpy.flatten()返回一份拷贝,对拷贝所做的修改不会影响(reflects)原始矩阵,而numpy.ravel()返回的是视图(view,也颇有几分C/C++引用reference的意味),会影响(reflects)原始矩阵。 两者功能 In[14]:x=np.array([[1,2],[3,4]]) # flattenh函数和ravel函数...
两者的区别在于返回拷贝(copy)还是返回视图(view),numpy.flatten()返回一份拷贝,对拷贝所做的修改不会影响(reflects)原始矩阵,而numpy.ravel()返回的是视图(view,也颇有几分C/C++引用reference的意味),会影响(reflects)原始矩阵。 两者功能 In [14]: x=np.array([[1,2],[3,4]])# flattenh函数和ravel函数...
而numpy.ravel()返回的是视图(view),会影响(reflects)原始矩阵。 1、二者的功能 >>> x = np.array([[1, 2], [3, 4]])>>>x array([[1, 2], [3, 4]])>>>x.flatten() array([1, 2, 3, 4])>>>x.ravel() array([1, 2, 3, 4]) ...
1、用于array对象 >>>from numpy import*>>>a=array([[1,2],[3,4],[5,6]])>>>a array([[1,2],[3,4],[5,6]])>>>a.flatten()array([1,2,3,4,5,6])>>>a.flatten('F')array([1,3,5,2,4,6])# 按列排序>>>a.flatten('A')array([1,2,3,4,5,6])>>> ...
python importnumpyasnp classDebug: def__init__(self): self.array1=np.array([[1,2], [3,4]]) self.array2=np.ones((2,2,2)) defmainProgram(self): print("The value of array1 is: ") print(self.array1) print("The value of flattened array is: ") ...
array([[1,2],[3,4]]) # flattenh函数和ravel函数在降维时默认是行序优先 In [15]: x.flatten() Out[15]: array([1, 2, 3, 4]) In [17]: x.ravel() Out[17]: array([1, 2, 3, 4]) # 传入'F'参数表示列序优先 In [18]: x.flatten('F') Out[18]: array([1, 3, 2, 4...
import numpy as np # 随机生成一个2维数组,4行3列 arr1 = np.random.randint(1, 10, size=[4, 3]) arr1 array([[9, 8, 5], [3, 2, 5], [8, 7, 4], [5, 9, 2]]) # 查看数组1的维数 arr1.ndim 2 # 将数组arr1以行为主要顺序进行拉平降维 ...
The resulting flattened array is [2, 4, 3, 5], which represents the original array 'y' flattened column-wise. Pictorial Presentation: Python - NumPy Code Editor: Previous:ndarray.flat() Next:Transpose-like operations moveaxis()