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.
New in version 1.6.0.
 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.
New in version 1.6.0.
 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.
New in version 1.17.0.
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.])