numpy.ravel() in Python

The numpy.ravel() functions returns contiguous flattened array(1D array with all the input-array elements and with the same type as it). A copy is made only if needed. 
Syntax : 

numpy.ravel(array, order = 'C')

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 reshape(-1, order=order)

Output : 

Original array : 
 [[ 0 1 2 3 4]
 [ 5 6 7 8 9]
 [10 11 12 13 14]]

ravel() : [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]

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

Code 2 :Showing ordering manipulation

Output : 

Original array : 
 [[ 0 1 2 3 4]
 [ 5 6 7 8 9]
 [10 11 12 13 14]]

About numpy.ravel() : <built-in method ravel of numpy.ndarray object at 0x000001F10F3F8930>

numpy.ravel() : [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]

Maintains A Order : [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]

array2 
 [[[ 0 2 4]
 [ 1 3 5]]

 [[ 6 8 10]
 [ 7 9 11]]]

Maintains A Order : [ 0 1 2 3 4 5 6 7 8 9 10 11]

Note : 
These codes won’t run on online IDE's. Please run them on your systems to explore the working.