numpy.reshape() in Python
The numpy.reshape() function shapes an array without changing the data of the array.
Syntax:
numpy.reshape(array, shape, order = 'C')
Parameters :
array : [array_like]Input array
shape : [int or tuples of int] e.g. if we are arranging 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
Return Type:
Array which is reshaped without changing the data.
Example
Python
import numpy as geek
array1 = geek.arange( 8 )
print ( "Original array : \n" , array1)
array2 = geek.arange( 8 ).reshape( 2 , 4 )
print ( "\narray reshaped with 2 rows and 4 columns : \n" ,
array2)
array3 = geek.arange( 8 ).reshape( 4 , 2 )
print ( "\narray reshaped with 4 rows and 2 columns : \n" ,
array3)
array4 = geek.arange( 8 ).reshape( 2 , 2 , 2 )
print ( "\nOriginal array reshaped to 3D : \n" ,
array4)
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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 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]]
References :
Note: These codes won’t run on online IDE’s. So please, run them on your systems to explore the working.
Last Updated :
08 Mar, 2024
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