numpy.ma.empty_like¶

numpy.ma.empty_like(prototype,dtype=None,order='K',subok=True) = <numpy.ma.core._convert2ma object>¶

Return a new array with the same shape and type as a given array.

Parameters:

prototype:array_like The shape and data-type ofprototype define these same attributesof the returned array. dtype:data-type, optional Overrides the data type of the result. New in version 1.6.0. order:{‘C’, ‘F’, ‘A’, or ‘K’}, optional Overrides the memory layout of the result. ‘C’ means C-order,‘F’ means F-order, ‘A’ means ‘F’ ifprototype is Fortrancontiguous, ‘C’ otherwise. ‘K’ means match the layout ofprototypeas closely as possible. New in version 1.6.0. subok:bool, optional. If True, then the newly created array will use the sub-classtype of ‘a’, otherwise it will be a base-class array. Defaultsto True.

Returns:

out:ndarray Array of uninitialized (arbitrary) data with the sameshape and type asprototype.

See also

ones_like

Return an array of ones with shape and type of input.

zeros_like

Return an array of zeros with shape and type of input.

full_like

Return a new array with shape of input filled with value.

empty

Return a new uninitialized array.

Notes

This function doesnot initialize the returned array; to do that usezeros_like orones_like instead. It may be marginally faster thanthe functions that do set the array values.

Examples

>>>a=([1,2,3],[4,5,6])# a is array-like>>>np.empty_like(a)array([[-1073741821, -1073741821,           3],    #random       [          0,           0, -1073741821]])>>>a=np.array([[1.,2.,3.],[4.,5.,6.]])>>>np.empty_like(a)array([[ -2.00000715e+000,   1.48219694e-323,  -2.00000572e+000],#random       [  4.38791518e-305,  -2.00000715e+000,   4.17269252e-309]])