/NumPy 1.17

# numpy.full_like

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

Return a full array 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. `fill_value : scalar` Fill value. `dtype : data-type, optional` Overrides the data type of the result. `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. `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 `fill_value` with the same shape and type as `a`.

`empty_like`
Return an empty array with shape and type of input.
`ones_like`
Return an array of ones with shape and type of input.
`zeros_like`
Return an array of zeros with shape and type of input.
`full`
Return a new array of given shape filled with value.

#### Examples

```>>> x = np.arange(6, dtype=int)
>>> np.full_like(x, 1)
array([1, 1, 1, 1, 1, 1])
>>> np.full_like(x, 0.1)
array([0, 0, 0, 0, 0, 0])
>>> np.full_like(x, 0.1, dtype=np.double)
array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
>>> np.full_like(x, np.nan, dtype=np.double)
array([nan, nan, nan, nan, nan, nan])
```
```>>> y = np.arange(6, dtype=np.double)
>>> np.full_like(y, 0.1)
array([0.1,  0.1,  0.1,  0.1,  0.1,  0.1])
```

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