numpy.zeros_like

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 as a.

See also
ones_likeReturn an array of ones with shape and type of input.empty_likeReturn an empty array with shape and type of input.zerosReturn a new array setting values to zero.onesReturn a new array setting values to one.emptyReturn a new uninitialized array.
Example
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s

>>>

>>> 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.])