numpy.full_like#
- numpy.full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None, *, device=None)[source]#
Return a full array with the same shape and type as a given array.
- Parameters:
- aarray_like
The shape and data-type of a define these same attributes of the returned array.
- fill_valuearray_like
Fill value.
- dtypedata-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.
- subokbool, 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.
- shapeint 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.
- devicestr, optional
The device on which to place the created array. Default: None. For Array-API interoperability only, so must be
"cpu"
if passed.New in version 2.0.0.
- Returns:
- outndarray
Array of fill_value with the same shape and type as a.
See also
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])
>>> y = np.zeros([2, 2, 3], dtype=int) >>> np.full_like(y, [0, 0, 255]) array([[[ 0, 0, 255], [ 0, 0, 255]], [[ 0, 0, 255], [ 0, 0, 255]]])