Numpy中Flatten() 和 Ravel()函数的区别

Numpy中有两种将 ndarray 转换为一维数组类似的方法：Flatten() 和 Ravel()。

import numpy as np a = np.array( [ (1,7,3,4),(3,2,4,1) ] ) #OUTPUT: print( a.flatten() ) # [ 1,7,3,4,3,2,4,1 ]  print ( a.ravel() ) # [ 1,7,3,4,3,2,4,1 ] 

那么问题是：为什么有两个 numpy 函数来完成相同的任务？Flatten() 和 Ravel() 的区别：

a.ravel():

• 只返回原始数组的引用/视图
• 如果修改数组，就会注意到原始数组的值也会发生变化。
• Ravel() 比 flatten() 更快，因为它不占用任何内存。
• Ravel() 是一个库级函数。

a.flatten() ：

• 返回原始数组的副本；
• 如果您修改此数组的任何值，原始数组的值不受影响。
• Flatten() 比 ravel() 相对慢，因为它占用内存。
• Flatten 是一个 ndarray 对象的方法。

让我们看看这段代码的区别：

# Python code to differentiate # between flatten and ravel in numpy import numpy as np  # Create a numpy array a = np.array([(1,2,3,4),(3,1,4,2)])  # Let's print the array a print ("Original array:n ") print(a)  # To check the dimension of array (dimension =2) # ( and type is numpy.ndarray ) print ("Dimension of array-> " , (a.ndim))   print("nOutput for RAVEL n") # Convert nd array to 1D array b = a.ravel()  # Ravel only passes a view of # original array to array 'b' print(b) b[0]=1000 print(b)  # Note here that value of original # array 'a' at also a[0][0] becomes 1000 print(a)  # Just to check the dimension i.e. 1 # (and type is same numpy.ndarray ) print ("Dimension of array->" ,(b.ndim))  print("nOutput for FLATTEN n")  # Convert nd array to 1D array c = a.flatten()  # Flatten passes copy of # original array to 'c' print(c) c[0]=0 print(c)  # Note that by changing # value of c there is no # affect on value of original # array 'a' print(a)  print ("Dimension of array-> " , (c.ndim)) 

运行结果如下：

Original array:  [[1 2 3 4]  [3 1 4 2]] Dimension of array->  2  Output for RAVEL   [1 2 3 4 3 1 4 2] [1000    2    3    4    3    1    4    2] [[1000    2    3    4]  [   3    1    4    2]] Dimension of array-> 1  Output for FLATTEN   [1000    2    3    4    3    1    4    2] [0 2 3 4 3 1 4 2] [[1000    2    3    4]  [   3    1    4    2]] Dimension of array->  1