numpy.ravel(array, order = ‘C’) : 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.
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
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)
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 :Showing ordering manipulation
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]
These codes won’t run on online-ID. Please run them on your systems to explore the working
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