stat_smooth.py 文件源码

def compute_group(cls, data, scales, **params):
        data = data.sort_values('x')
        n = params['n']

        x_unique = data['x'].unique()

        if len(x_unique) < 2:
            # Not enough data to fit
            return pd.DataFrame()

        if data['x'].dtype.kind == 'i':
            if params['fullrange']:
                xseq = scales.x.dimension()
            else:
                xseq = np.sort(x_unique)
        else:
            if params['fullrange']:
                rangee = scales.x.dimension()
            else:
                rangee = [data['x'].min(), data['x'].max()]
            xseq = np.linspace(rangee[0], rangee[1], n)

        return predictdf(data, xseq, **params)