nums.numpy.zeros_like

nums.numpy.zeros_like(prototype, dtype=None, order='K', shape=None)

Return an array of zeros with the same shape and type as a given array.

This docstring was copied from numpy.zeros_like.

Some inconsistencies with the NumS version may exist.

prototypearray_like

The shape and data-type of prototype define these same attributes of the returned array.

dtypedata-type, optional

Overrides the data type of the result.

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’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible.

shapeint or sequence of ints, optional.

Overrides the shape of the result. If order=’K’ and the number of dimensions is unchanged, will try to keep order, otherwise, order=’C’ is implied.

outBlockArray

Array of zeros with the same shape and type as prototype.

empty_like : Return an empty array with shape and type of input. ones_like : Return an array of ones with shape and type of input. full_like : Return a new array with shape of input filled with value. zeros : Return a new array setting values to zero.

Only order=’K’ is supported.

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> x = nps.arange(6)  
>>> x = x.reshape((2, 3))  
>>> x.get()  
array([[0, 1, 2],
       [3, 4, 5]])
>>> nps.zeros_like(x).get()  
array([[0, 0, 0],
       [0, 0, 0]])

>>> y = nps.arange(3, dtype=float)  
>>> y.get()  
array([0., 1., 2.])
>>> nps.zeros_like(y).get()  
array([0.,  0.,  0.])