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.
This article is contributed by Mohit Gupta_OMG 😀. If you like Lazyroar and would like to contribute, you can also write an article using write.geeksforgeeks.org or mail your article to review-team@geeksforgeeks.org. See your article appearing on the Lazyroar main page and help other Geeks.
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.
