The numpy.reshape() function shapes an array without changing data of array.
Syntax: numpy.reshape(array, shape, order = 'C')
array : [array_like]Input array shape : [int or tuples of int] e.g. if we are aranging an array with 10 elements then shaping it like numpy.reshape(4, 8) is wrong; we can 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
Array which is reshaped without changing the data.
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]]]
These codes won’t run on online-ID. Please run them on your systems to explore the working
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