nanmin

Minimize along the given axes, skipping NaN values. 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 minimize. 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.min : Function without NaN skipping.
• numpy.nanmin : Equivalent Numpy function.

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

def nanmin(x, axis=None, keepdims=False, dtype=None, out=None):
    """
    Minimize along the given axes, skipping ``NaN`` values. 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 minimize. 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.min`][] : Function without `NaN` skipping.
    - [`numpy.nanmin`][] : Equivalent Numpy function.
    """
    assert out is None
    x = asCOO(x, name="nanmin")

    ar = x.reduce(np.fmin, axis=axis, keepdims=keepdims, dtype=dtype)

    if (isscalar(ar) and np.isnan(ar)) or np.isnan(ar.data).any():
        warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=1)

    return ar