numpy.MaskedArray.reshape() is used to reshape a masked array without changing its data. Returns a masked array containing the same data but with a new shape. The result is a representation of the original array; if this is not possible, a ValueError is raised.
numpy.ma.reshape (shape, order)
shape: [int or tuple of ints] The new shape should be compatible with the original shape.
order: [`C`, `F`, `A`, `K`, optional] By default, `C` index order is used.
– & gt; The elements of a are read using this index order.
– & gt; `C` means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest.
– & gt; `F` means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest.
– & gt; `A` means to read the elements in Fortran-like index order if m is Fortran contiguous in memory, C-like order otherwise.
– & gt; `K` means to read the elements in the order they occur in memory, except for reversing the data when strides are negative.
Return: [reshaped_array] A new view on the array.
Code # 1:
Input array: [1 2 3 -1] Masked array: [- 2 - -1 ] Output 2D masked array: [[- 2] [- -1]]
Code # 2:
Input array: [[[2.0e + 08 3.0e-05]] [[-4.5e + 01 2.0e + 05]]] 3D Masked array: [[[- 3e-05]] [[-45.0 200000.0]]] Output 2D masked array: [[- 3e-05 -45.0 200000.0]] Output 1D masked array: [- 3e-05 -45.0 200000.0]