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:

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`.

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.

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.])

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.zeros_like.html

doc_NumPy

2017-01-10 18:19:14

Comments