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
# Python Program illustrating# numpy.reshape() methodimport 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 columnsarray2 = geek.arange(8).reshape(2, 4)print("\narray reshaped with 2 rows and 4 columns : \n", array2)# shape array with 4 rows and 2 columnsarray3 = geek.arange(8).reshape(4, 2)print("\narray reshaped with 4 rows and 2 columns : \n", array3)# Constructs 3D arrayarray4 = geek.arange(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]]] [[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.
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