def boxes_to_dataframe(self):
    'convert boxes to pandas dataframe'
    boxes = self.boxes
    (box_lims, uncs) = _get_sorted_box_lims(boxes, _make_box(self.x))
    nr_boxes = len(boxes)
    dtype = float
    index = ['box {}'.format((i + 1)) for i in range(nr_boxes)]
    for value in box_lims[0].dtypes:
        if (value == object):
            dtype = object
            break
    columns = pd.MultiIndex.from_product([index, ['min', 'max']])
    df_boxes = pd.DataFrame(np.zeros((len(uncs), (nr_boxes * 2))), index=uncs, dtype=dtype, columns=columns)
    for (i, box) in enumerate(box_lims):
        for unc in uncs:
            values = box.loc[:, unc]
            values = values.rename({0: 'min', 1: 'max'})
            df_boxes.loc[unc][index[i]] = values
    return df_boxes
convert boxes to pandas dataframe
def boxes_to_dataframe(self):
    
    boxes = self.boxes
    (box_lims, uncs) = _get_sorted_box_lims(boxes, _make_box(self.x))
    nr_boxes = len(boxes)
    dtype = float
    index = ['box {}'.format((i + 1)) for i in range(nr_boxes)]
    for value in box_lims[0].dtypes:
        if (value == object):
            dtype = object
            break
    columns = pd.MultiIndex.from_product([index, ['min', 'max']])
    df_boxes = pd.DataFrame(np.zeros((len(uncs), (nr_boxes * 2))), index=uncs, dtype=dtype, columns=columns)
    for (i, box) in enumerate(box_lims):
        for unc in uncs:
            values = box.loc[:, unc]
            values = values.rename({0: 'min', 1: 'max'})
            df_boxes.loc[unc][index[i]] = values
    return df_boxes