** Parameters :**

** array: ** [array_like] Input array. ** 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: **

Flattened array having same type as the Input array and and order as per choice.

** Code 1: Shows that array.ravel is equivalent to reshaping (-1, order = order) **

` # Python program illustrating ` ` # numpy.ravel () method ` ` ` ` import ` ` numpy as geek ` ` array ` ` = ` ` geek.arange (` ` 15 ` `). reshape (` ` 3 ` `, ` ` 5 ` `) ` ` print ` ` (` ` "Original array:" ` `, array) ` ` ` ` # Outpue comes as [0 1 2 ..., 12 13 14] ` ` # as long output, so this is the path ` ` # shows output in Python ` ` print ` ` (` ` "ravel ():" ` `, array.ravel () ) ` ` # This shows that array.ravel is equivalent to changing the form (- 1, order = order). ` ` print ` ` (` `" numpy.ravel () == numpy.reshape (-1) "` `) ` ` print ` ` (` ` "Reshaping array:" ` `, array.reshape (` ` - ` ` 1 ` `)) ` |

** Output: **

Original array: [[0 1 2 3 4] [5 6 7 8 9] [10 11 12 13 14]] ravel (): [0 1 2 ..., 12 13 14] numpy.ravel () == numpy.reshape (-1) Reshaping array: [0 1 2 ..., 12 13 14]

** Code 2: Shows order manipulation **

** Output: **

Original array: [[0 1 2 3 4] [5 6 7 8 9] [10 11 12 13 14]] About numpy.ravel (): numpy.ravel (): [0 1 2 ..., 12 13 14] Maintains A Order: [0 1 2 ..., 12 13 14] array2 [[[0 2 4] [1 3 5]] [[6 8 10] [7 9 11]]] Maintains A Order: [0 1 2 ..., 9 10 11]

** Links: **

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

** Notes: **

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

This article is provided by ** Mohit Gupta_OMG
**

**
**
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` # Python program illustrating ` ` # numpy.ravel () method ` ` import ` ` numpy as geek ` ` ` ` array ` ` = ` ` geek.arange (` ` 15 ` `). Reshape (` ` 3 ` `, ` ` 5 ` `) ` print ` (` ` "Original array:" ` ` , array) ` ` # Outpue comes as [0 1 2 ... , 12 13 14] ` ` # as long output, so this is the way ` ` # shows output in Python ` ` # Near: ` ` print ` ` (` ` "About numpy.ravel ():" ` `, array.ravel) ` ` print ` ` (` ` "numpy.ravel ():" ` `, array.ravel ()) ` ` # Maintain the order of “A” and “F” ` ` print ` ` (` ` "Maintains A Order:" ` ` , array.ravel (order ` ` = ` ` ’A’ ` `)) ` ` # K-order to save order ` ` # "K" means it is not "A" or "F" ` ` array2 ` ` = ` ` geek.arange (` ` 12 ` `). reshape (` ` 2 ` `, ` ` 3 ` `, ` ` 2 ` `). swapaxes (` ` 1 ` `, ` ` 2 ` `) ` ` print ` ` (` `" ar ray2 "` `, array2) ` ` print ` ` ( ` ` "Maintains A Order:" ` `, array2.ravel (order ` ` = ` ` ’K’ ` `)) ` |