nanmean

Performs a NaN skipping mean operation along the given axes. Uses all axes by default.

Parameters:

Name	Type	Description	Default
x	SparseArray	The array to perform the reduction on.	required
axis	Union[int, Iterable[int]]	The axes along which to compute the mean. Uses all axes by default.	None
keepdims	bool_	Whether or not to keep the dimensions of the original array.	False
dtype	dtype	The data type of the output array.	None

Returns:

Type	Description
COO	The reduced output sparse array.

See Also

• sparse.COO.mean : Function without NaN skipping.
• numpy.nanmean : Equivalent Numpy function.

Source code in sparse/numba_backend/_coo/common.py

def nanmean(x, axis=None, keepdims=False, dtype=None, out=None):
    """
    Performs a `NaN` skipping mean operation along the given axes. Uses all axes by default.

    Parameters
    ----------
    x : SparseArray
        The array to perform the reduction on.
    axis : Union[int, Iterable[int]], optional
        The axes along which to compute the mean. Uses all axes by default.
    keepdims : bool, optional
        Whether or not to keep the dimensions of the original array.
    dtype : numpy.dtype
        The data type of the output array.

    Returns
    -------
    COO
        The reduced output sparse array.

    See Also
    --------
    - [`sparse.COO.mean`][] : Function without `NaN` skipping.
    - [`numpy.nanmean`][] : Equivalent Numpy function.
    """
    assert out is None
    x = asCOO(x, name="nanmean")

    if not (np.issubdtype(x.dtype, np.floating) or np.issubdtype(x.dtype, np.complexfloating)):
        return x.mean(axis=axis, keepdims=keepdims, dtype=dtype)

    mask = np.isnan(x)
    x2 = where(mask, 0, x)

    # Count the number non-nan elements along axis
    nancount = mask.sum(axis=axis, dtype="i8", keepdims=keepdims)
    if axis is None:
        axis = tuple(range(x.ndim))
    elif not isinstance(axis, tuple):
        axis = (axis,)
    den = reduce(operator.mul, (x.shape[i] for i in axis), 1)
    den -= nancount

    if (den == 0).any():
        warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=1)

    num = np.sum(x2, axis=axis, dtype=dtype, keepdims=keepdims)

    with np.errstate(invalid="ignore", divide="ignore"):
        if num.ndim:
            return np.true_divide(num, den, casting="unsafe")
        return (num / den).astype(dtype if dtype is not None else x.dtype)