Parameters :
array: [array_like] Input array shape: [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 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
Return:
Array which is reshaped without changing the data.
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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
