stat_boxplot.py 文件源码

def compute_group(cls, data, scales, **params):
        labels = ['x', 'y']
        X = np.array(data[labels])
        res = boxplot_stats(X, whis=params['coef'], labels=labels)[1]
        try:
            n = data['weight'].sum()
        except KeyError:
            n = len(data['y'])

        if len(np.unique(data['x'])) > 1:
            width = np.ptp(data['x']) * 0.9
        else:
            width = params['width']

        if pdtypes.is_categorical(data['x']):
            x = data['x'].iloc[0]
        else:
            x = np.mean([data['x'].min(), data['x'].max()])

        d = {'ymin': res['whislo'],
             'lower': res['q1'],
             'middle': [res['med']],
             'upper': res['q3'],
             'ymax': res['whishi'],
             'outliers': [res['fliers']],
             'notchupper': res['med']+1.58*res['iqr']/np.sqrt(n),
             'notchlower': res['med']-1.58*res['iqr']/np.sqrt(n),
             'x': x,
             'width': width,
             'relvarwidth': np.sqrt(n)}
        return pd.DataFrame(d)