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numpy.reshape() in Python

  • Difficulty Level : Medium
  • Last Updated : 23 Nov, 2021

The numpy.reshape() function shapes an array without changing the data of the array.

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

Parameters : 

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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. 
         β€˜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.


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

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 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]]]

References : 

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

This article is contributed by Mohit Gupta_OMG πŸ˜€. If you like GeeksforGeeks and would like to contribute, you can also write an article using or mail your article to See your article appearing on the GeeksforGeeks main page and help other Geeks.
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