def box_cox(table):
    """
    box-cox transform table
    """
    from scipy.stats import boxcox as bc
    t = []
    for i in table:
        if min(i) == 0:
            scale = min([j for j in i if j != 0]) * 10e-10
        else:
            scale = 0
        t.append(np.ndarray.tolist(bc(np.array([j + scale for j in i]))[0]))
    return t
def box_cox ( table ) : from scipy . stats import boxcox as bc t = [ ] for i in table : if min ( i ) == 0 : scale = min ( [ j for j in i if j != 0 ] ) * 10e-10 else : scale = 0 t . append ( np . ndarray . tolist ( bc ( np . array ( [ j + scale for j in i ] ) ) [ 0 ] ) ) return t
box-cox transform table