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# 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

 `# Python Program illustrating``# numpy.reshape() method` `import` `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 columns``array2 ``=` `geek.arange(``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.arange(``8``).reshape(``4``, ``2``)``print``(``"\narray reshaped with 4 rows and 2 columns : \n"``,``      ``array3)` `# Constructs 3D array``array4 ``=` `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 GeeksforGeeks 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 GeeksforGeeks main page and help other Geeks.