numpy.zeros_like

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

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

Parameters:

a : array_like The shape and data-type of a define these same attributes of 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’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. New in version 1.6.0. subok : bool, optional. If True, then the newly created array will use the sub-class type of ‘a’, otherwise it will be a base-class array. Defaults to True.

Returns:

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

See also

ones_like

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

empty_like

Return an empty array with shape and type of input.

zeros

Return a new array setting values to zero.

ones

Return a new array setting values to one.

empty

Return a new uninitialized array.

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=np.float)
>>> y
array([ 0.,  1.,  2.])
>>> np.zeros_like(y)
array([ 0.,  0.,  0.])