full

Return a SparseArray of given shape and type, filled with fill_value.

Parameters:

Name	Type	Description	Default
shape	int or tuple of ints	Shape of the new array, e.g., (2, 3) or 2.	required
fill_value	scalar	Fill value.	required
dtype	data - type	The desired data-type for the array. The default, None, means np.array(fill_value).dtype.	None
format	str	A format string.	'coo'
compressed_axes	iterable	The axes to compress if returning a GCXS array.	required
order	(C, None)	Values except these are not currently supported and raise a NotImplementedError.	'C'

Returns:

Name	Type	Description
out	SparseArray	Array of fill_value with the given shape and dtype.

Examples:

>>> full(5, 9).todense()
array([9, 9, 9, 9, 9])

>>> full((2, 2), 9, dtype=float).todense()
array([[9., 9.],
       [9., 9.]])

Source code in sparse/numba_backend/_common.py

@_check_device
def full(shape, fill_value, dtype=None, format="coo", order="C", *, device=None, **kwargs):
    """Return a SparseArray of given shape and type, filled with `fill_value`.

    Parameters
    ----------
    shape : int or tuple of ints
        Shape of the new array, e.g., ``(2, 3)`` or ``2``.
    fill_value : scalar
        Fill value.
    dtype : data-type, optional
        The desired data-type for the array. The default, `None`, means
        `np.array(fill_value).dtype`.
    format : str, optional
        A format string.
    compressed_axes : iterable, optional
        The axes to compress if returning a GCXS array.
    order : {'C', None}
        Values except these are not currently supported and raise a
        NotImplementedError.

    Returns
    -------
    out : SparseArray
        Array of `fill_value` with the given shape and dtype.

    Examples
    --------
    >>> full(5, 9).todense()  # doctest: +NORMALIZE_WHITESPACE
    array([9, 9, 9, 9, 9])

    >>> full((2, 2), 9, dtype=float).todense()  # doctest: +SKIP
    array([[9., 9.],
           [9., 9.]])
    """
    from sparse import COO

    if dtype is None:
        dtype = np.array(fill_value).dtype
    if not isinstance(shape, tuple):
        shape = (shape,)
    if order not in {"C", None}:
        raise NotImplementedError("Currently, only 'C' and None are supported.")
    data = np.empty(0, dtype=dtype)
    coords = np.empty((len(shape), 0), dtype=np.intp)
    return COO(
        coords,
        data=data,
        shape=shape,
        fill_value=fill_value,
        has_duplicates=False,
        sorted=True,
    ).asformat(format, **kwargs)