Numpy中Flatten()和Ravel()函数的区别

我们有两种类似的方法将ndarray数组转换为1D数组: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 ]

这里出现了一个问题，为什么有两个numpy函数来完成相同的任务?

Flatten()和Ravel()的区别

.ravel ():

(i)只返回原始数组的引用/视图

(ii)如果你修改数组，你会注意到原始数组的值也改变了。

(iii) Ravel比flatten()更快，因为它不占用任何内存。

(iv) Ravel是一个图书馆级的功能。

.flatten ():

(i)返回原数组的副本

(ii)如果修改此数组的任何值，原数组的值都不受影响。

(iii) Flatten()比ravel()要慢一些，因为它占用内存。

Flatten是一个ndarray对象的方法。

让我们通过这段代码来检查区别

# 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))

OUTPUT:
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