numpy.reshape() in Python

About :
numpy.reshape(array, shape, order = ‘C’) : shapes an array without changing data of array.

Parameters :

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

Return :

Array which is reshaped without changing the data.
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# 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)

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Output :

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]]]

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

Note :
These codes won’t run on online-ID. Please run them on your systems to explore the working
.
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