Python中的 numpy.reshape()

numpy.reshape()函数在不更改数组数据的情况下对数组进行整形。

Syntax: numpy.reshape(array, shape, order = 'C')

参数 ：

array : [array_like]Input array
shape : [int or tuples of int] e.g. if we are aranging an array with 10 elements then shaping
        it like numpy.reshape(4, 8) is wrong; we can do numpy.reshape(2, 5) or (5, 2)
order  : [C-contiguous, F-contiguous, A-contiguous; optional]         
         C-contiguous order in memory(last index varies the fastest)
         C order means that operating row-rise on the array will be slightly quicker
         FORTRAN-contiguous order in memory (first index varies the fastest).
         F order means that column-wise operations will be faster. 
         ‘A’ means to read / write the elements in Fortran-like index order if,
         array is Fortran contiguous in memory, C-like order otherwise

返回 ：

Array which is reshaped without changing the data.

Python

# Python Program illustrating
# numpy.reshape() method
 
import numpy as geek
 
# array = geek.arrange(8)
# The 'numpy' module has no attribute 'arrange'
array1 = geek.arange(8)
print("Original array : \n", array1)
 
# shape array with 2 rows and 4 columns
array2 = geek.arange(8).reshape(2, 4)
print("\narray reshaped with 2 rows and 4 columns : \n",
      array2)
 
# shape array with 4 rows and 2 columns
array3 = geek.arange(8).reshape(4, 2)
print("\narray reshaped with 2 rows and 4 columns : \n",
      array3)
 
# Constructs 3D array
array4 = geek.arange(8).reshape(2, 2, 2)
print("\nOriginal array reshaped to 3D : \n",
      array4)

输出 ：

Original array : 
 [0 1 2 3 4 5 6 7]

array reshaped with 2 rows and 4 columns : 
 [[0 1 2 3]
 [4 5 6 7]]

array reshaped with 4 rows and 2 columns : 
 [[0 1]
 [2 3]
 [4 5]
 [6 7]]

Original array reshaped to 3D : 
 [[[0 1]
  [2 3]]
 [[4 5]
  [6 7]]]
  
 
 [[0 1 2 3]
 [4 5 6 7]]

参考 ：

• https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.reshape.html

注意：这些代码不会在在线 IDE 上运行。因此，请在您的系统上运行它们以探索其工作原理。