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