isinstance alternative to overwrite default values

My question is about the isinstance function but I'll give an example:

I am trying to implement min-max normalization in pandas, and specifically I need to be able to set arbitrary max and min values.

The following code seems to work:

def normalize_parameter(
    array, 
    fill_na=True, 
    min_bound=True, 
    max_bound=True,
    feature_range=(0, 1), 
    reverse=False
):
    """ Min-max normalization. 
    
    
    Parameters
    ----------
    array : pd.Series
        column to normalize
    fill_na : bool
        NaN policy: fillna with 0 (True) or not (False)
    min_bound, max_bound : bool
        min and max values, default (min, max values of an array)
    feature_range : tuple (min, max), default=(0, 1)
        scale of normalization
    reverse : bool, default=False
    """
    s = array.fillna(0) if fill_na else array
    if isinstance(min_bound, bool):
        min_bound = s.min()
    if isinstance(max_bound, bool):
        max_bound = s.max()
    print(f"{feature_range=}; bounds: {min_bound=}, {max_bound=}; nan policy: {fill_na=}")

    _min, _max = feature_range
    min_max_normalization = _min + ((s - min_bound) * (_max - _min) / (max_bound - min_bound))
    return 1 - min_max_normalization if reverse else min_max_normalization

# takes min and max values of an array  
normalize_parameter(array)
# takes min value of an array and max value of 1
normalize_parameter(array, max_bound=1)

But I feel this part specifically could be changed and be more pythonic.

if isinstance(min_bound, bool):
    min_bound = s.min()

I thought this would work:

min_bound = min_bound or s.min()

But it doesn't if min_bound = 0 as 0 == False.

Do you think there's a better way or should I stick to isinstance?