改变一个NumPy数组的尺寸

让我们讨论一下如何改变一个数组的尺寸。在NumPy中，这可以通过多种方式实现。让我们来讨论一下每一种方法。

方法#1：使用Shape()

语法 :

array_name.shape()

# importing numpy
import numpy as np
  
  
def main():
  
    # initialising array
    print('Initialised array')
    gfg = np.array([1, 2, 3, 4])
    print(gfg)
  
    # checking current shape
    print('current shape of the array')
    print(gfg.shape)
      
    # modifying array according to new dimensions
    print('changing shape to 2,3')
    gfg.shape = (2, 2)
    print(gfg)
  
if __name__ == "__main__":
    main()

输出:

Initialised array
[1 2 3 4]
current shape of the array
(4,)
changing shape to 2,3
[[1 2]
 [3 4]]

方法二：使用 reshape()。

reshape()函数的顺序参数是高级的，是可选的。当我们使用C和F时，输出是不同的，因为NumPy改变结果数组的索引的方式不同。顺序A使NumPy根据内存块的可用大小，从C或F中选择最佳的顺序。

C级和F级之间的区别

语法 :

numpy.reshape(array_name, newshape, order= 'C' or 'F' or 'A')

# importing numpy
import numpy as np
  
  
def main():
  
    # initialising array
    gfg = np.arange(1, 10)
    print('initialised array')
    print(gfg)
      
    # reshaping array into a 3x3 with order C
    print('3x3 order C array')
    print(np.reshape(gfg, (3, 3), order='C'))
  
    # reshaping array into a 3x3 with order F
    print('3x3 order F array')
    print(np.reshape(gfg, (3, 3), order='F'))
  
    # reshaping array into a 3x3 with order A
    print('3x3 order A array')
    print(np.reshape(gfg, (3, 3), order='A'))
  
  
if __name__ == "__main__":
    main()

输出 :

initialised array
[1 2 3 4 5 6 7 8 9]
3x3 order C array
[[1 2 3]
 [4 5 6]
 [7 8 9]]
3x3 order F array
[[1 4 7]
 [2 5 8]
 [3 6 9]]
3x3 order A array
[[1 2 3]
 [4 5 6]
 [7 8 9]]

方法#3：使用 resize()

数组的形状也可以用resize()方法来改变。如果指定的尺寸大于实际的数组，那么新数组中多余的空间将被原数组的重复拷贝所填充。

语法 :

numpy.resize(a, new_shape)

# importing numpy
import numpy as np
  
  
def main():
  
    # initialise array
    gfg = np.arange(1, 10)
    print('initialised array')
    print(gfg)
      
    # resezed array with dimensions in
    # range of original array
    np.resize(gfg, (3, 3))
    print('3x3 array')
    print(gfg)
      
    # re array with dimensions larger than
    # original array
    np.resize(gfg, (4, 4))
      
    # extra spaces will be filled with repeated
    # copies of original array
    print('4x4 array')
    print(gfg)
      
    # resize array with dimensions larger than 
    # original array
    gfg.resize(5, 5)
      
    # extra spaces will be filled with zeros
    print('5x5 array')
    print(gfg)
  
  
if __name__ == "__main__":
    main()

输出 :

initialised array
[1 2 3 4 5 6 7 8 9]
3x3 array
[1 2 3 4 5 6 7 8 9]
4x4 array
[1 2 3 4 5 6 7 8 9]
5x5 array
[[1 2 3 4 5]
 [6 7 8 9 0]
 [0 0 0 0 0]
 [0 0 0 0 0]
 [0 0 0 0 0]]