numpy.zeros_like

numpy.zeros_like(a, dtype=None, order='K', subok=True, shape=None)
[source]

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

a : array_like 
The shape and datatype of a define these same attributes of the returned array. 
dtype : datatype, optional 
Overrides the data type of the result. 
order : {‘C’, ‘F’, ‘A’, or ‘K’}, optional 
Overrides the memory layout of the result. ‘C’ means Corder, ‘F’ means Forder, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. 
subok : bool, optional. 
If True, then the newly created array will use the subclass type of ‘a’, otherwise it will be a baseclass array. Defaults to True. 
shape : int 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. 
Returns: 

out : ndarray 
Array of zeros with the same shape and type as a . 
See also

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.
Examples
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> np.zeros_like(x)
array([[0, 0, 0],
[0, 0, 0]])
>>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.])
>>> np.zeros_like(y)
array([0., 0., 0.])