** Parameters :**

array:[array_like] Input arrayshape:[int or tuples of int] eg if we are aranging an array with 10 elements then shaping it like numpy.reshape (4, 8) is wrong; we canorder:[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: **

Array which is reshaped without changing the data.

` `

```
```
` # Python program illustrating `

` # numpy.reshape () method `

` import `

` numpy as geek `

` array `

` = `

` geek.arange (`

` 8 `

`) `

` print `

` (`

` "Original array:" `

`, array) `

` # form array with 2 rows and 4 columns `

` array `

` = `

` geek.arange (`

` 8 `

). reshape ( ` 2 `

`, `

` 4 `

`) `

` print `

` (`

` "array reshaped with 2 rows and 4 columns:" `

`, array) `

` `

` # array of form with 2 rows and 4 columns `

` array `

` = `

` geek.arange (`

` 8 `

`). reshape (`

` 4 `

`, `

` 2 `

`) `

` print `

` (`

` "array reshaped with 2 rows and 4 columns:" `

`, array) `

` # Creates a 3D array `

` array `

` = `

` geek. arange (`

` 8 `

`). reshape (`

` 2 `

`, `

` 2 `

`, `

` 2 `

`) `

` print `

` (`

` "Original array reshaped to 3D:" `

`, array) `

` `

** 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 2 rows and 4 columns: [[0 1] [2 3] [4 5] [6 7]] Original array reshaped to 3D: [[[0 1] [2 3]] [[4 5] [6 7]]]

** Links: **

https://docs.scipy.org /doc/numpy-dev/reference/generated/numpy.reshape.html

** Notes: **

These codes will not work for online IDs. Please run them on your systems to see how they work

This article is provided by ** Mohit Gupta_OMG**

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