numpy.zeros

numpy.full

# 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. out : ndarray Array of zeros with the same shape and type as a.

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