/NumPy 1.17

# numpy.ones_like

`numpy.ones_like(a, dtype=None, order='K', subok=True, shape=None)` [source]

Return an array of ones 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. `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. `out : ndarray` Array of ones with the same shape and type as `a`.

`empty_like`
Return an empty array with shape and type of input.
`zeros_like`
Return an array of zeros with shape and type of input.
`full_like`
Return a new array with shape of input filled with value.
`ones`
Return a new array setting values to one.

#### Examples

```>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> np.ones_like(x)
array([[1, 1, 1],
[1, 1, 1]])
```
```>>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.])
>>> np.ones_like(y)
array([1.,  1.,  1.])
```