numpy.zeros_like — NumPy v1.13 Manual (original) (raw)
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. |
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| Returns: | out : ndarray Array of zeros with the same shape and type as a. |
See also
Return an array of ones with shape and type of input.
Return an empty array with shape and type of input.
Return a new array setting values to zero.
Return a new array setting values to one.
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.])