min

Calculates the minimum value of the input array x.

Parameters:

Name	Type	Description	Default
x		input array. Should have a real-valued data type.	required
axis		axis or axes along which minimum values are computed. By default, the minimum value must be computed over the entire array. If a tuple of integers, minimum values must be computed over multiple axes. Default: None.	None
keepdims		If True, the reduced axes (dimensions) are included in the result as singleton dimensions. Accordingly, the result must be compatible with the input array. Otherwise, if False, the reduced axes (dimensions) are not be included in the result. Default: False.	False

Returns:

Name	Type	Description
out	array	if the minimum value was computed over the entire array, a zero-dimensional array containing the minimum value. Otherwise, a non-zero-dimensional array containing the minimum values. The returned array must have the same data type as x.

Special Cases

For floating-point operands, if x_i is NaN, the minimum value is NaN (i.e., NaN values propagate).

Examples:

>>> a = sparse.COO.from_numpy(np.array([[0, -1], [-2, 0]]))
>>> o = sparse.min(a, axis=1)
>>> o.todense()
array([-1, -2])

Source code in sparse/numba_backend/_common.py

def min(x, /, *, axis=None, keepdims=False):
    """
    Calculates the minimum value of the input array ``x``.

    Parameters
    ----------
    x: array
        input array. Should have a real-valued data type.
    axis: Optional[Union[int, Tuple[int, ...]]]
        axis or axes along which minimum values are computed.
        By default, the minimum value must be computed over the entire array.
        If a tuple of integers, minimum values must be computed over multiple axes. Default: ``None``.
    keepdims: bool
        If ``True``, the reduced axes (dimensions) are included in the result as singleton dimensions.
        Accordingly, the result must be compatible with the input array.
        Otherwise, if ``False``, the reduced axes (dimensions) are not be included in the result. Default: ``False``.

    Returns
    -------
    out: array
        if the minimum value was computed over the entire array, a zero-dimensional array containing the minimum value.
        Otherwise, a non-zero-dimensional array containing the minimum values.
        The returned array must have the same data type as ``x``.

    Special Cases
    -------------
    For floating-point operands, if ``x_i`` is ``NaN``, the minimum value is ``NaN`` (i.e., ``NaN`` values propagate).

    Examples
    --------
    >>> a = sparse.COO.from_numpy(np.array([[0, -1], [-2, 0]]))
    >>> o = sparse.min(a, axis=1)
    >>> o.todense()
    array([-1, -2])
    """
    return x.min(axis=axis, keepdims=keepdims)