Python numpy.reshape()用法及代碼示例

關於：
numpy.reshape(array, shape, order = ‘C’)：在不更改數組數據的情況下對數組進行整形。

參數：

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 
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 Program illustrating 
# numpy.reshape() method 
  
import numpy as geek 
  
array = geek.arange(8) 
print("Original array : \n", array) 
  
# shape array with 2 rows and 4 columns 
array = geek.arange(8).reshape(2, 4) 
print("\narray reshaped with 2 rows and 4 columns : \n", array) 
  
# shape array with 2 rows and 4 columns 
array = geek.arange(8).reshape(4 ,2) 
print("\narray reshaped with 2 rows and 4 columns : \n", array) 
  
# Constructs 3D array 
array = geek.arange(8).reshape(2, 2, 2) 
print("\nOriginal array reshaped to 3D : \n", array)

輸出：

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

Original array reshaped to 3D : 
 [[[0 1]
  [2 3]]

 [[4 5]
  [6 7]]]

參考文獻：
https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.reshape.html