📜  Numpy 中 reshape() 和 resize() 方法的区别

📅  最后修改于: 2022-05-13 01:55:21.374000             🧑  作者: Mango

Numpy 中 reshape() 和 resize() 方法的区别

这俩 numpy.reshape() numpy.resize() 方法用于更改 NumPy 数组的大小。它们的区别在于 reshape() 不改变原始数组,只返回改变后的数组,而 resize() 方法不返回任何内容,直接改变原始数组。

示例 1:使用 reshape()

Python3
# importing the module
import numpy as np 
    
# creating an array 
gfg = np.array([1, 2, 3, 4, 5, 6]) 
print("Original array:")
display(gfg)  
  
# using reshape()
print("Changed array")
display(gfg.reshape(2, 3)) 
    
print("Original array:")
display(gfg)


Python3
# importing the module
import numpy as np 
    
# creating an array 
gfg = np.array([1, 2, 3, 4, 5, 6]) 
print("Original array:")
display(gfg)  
  
# using resize()
print("Changed array")
# this will print nothing as None is returned
display(gfg.resize(2, 3)) 
    
print("Original array:")
display(gfg)


       

输出:

      

示例 2:使用 resize()

Python3

# importing the module
import numpy as np 
    
# creating an array 
gfg = np.array([1, 2, 3, 4, 5, 6]) 
print("Original array:")
display(gfg)  
  
# using resize()
print("Changed array")
# this will print nothing as None is returned
display(gfg.resize(2, 3)) 
    
print("Original array:")
display(gfg)

输出: