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 ofthe 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’ ifa is Fortran contiguous,‘C’ otherwise. ‘K’ means match the layout ofa as closelyas 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 zeros with the same shape and type asa.

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))>>> xarray([[0, 1, 2],       [3, 4, 5]])>>> np.zeros_like(x)array([[0, 0, 0],       [0, 0, 0]])

>>>

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