def to_json(self, data, labels=None):
        """
            Will return a dict with Google JSON structure for charts

            The Google structure::

                {
                    cols: [{id:<COL_NAME>, label:<LABEL FOR COL>, type: <COL TYPE>}, ...]
                    rows: [{c: [{v: <COL VALUE}, ...], ... ]
                }

            :param data:
            :param labels: dict with labels to include on Google JSON strcut
            :return: dict with Google JSON structure
        """
        labels = labels and dict()
        json_data = dict()
        json_data['cols'] = []
        # Create Structure to identify the grouped columns
        for group_col in self.group_bys_cols:
            label = '' or as_unicode(labels[group_col])
            json_data['cols'].append({'id': group_col,
                                      'label': label,
                                      'type': 'string'})
        # Create Structure to identify the Aggregated columns
        for aggr_col in self.aggr_by_cols:
            if isinstance(aggr_col, tuple):
                label_key = aggr_col[0].__name__ + aggr_col[1]
                aggr_col = aggr_col[1]
            else:
                label_key = aggr_col
            label = '' or as_unicode(labels[label_key])
            json_data['cols'].append({'id': aggr_col,
                                      'label': label,
                                      'type': 'number'})
        # Create Structure with the data
        json_data['rows'] = []
        for item in data:
            row = {'c': []}
            if not isinstance(item[0], tuple):
                row['c'].append({'v': '{0}'.format(item[0])})
            else:
                for group_col_data in item[0]:
                    row['c'].append({'v': '{0}'.format(group_col_data)})
            for col_data in item[1:]:
                if isinstance(col_data, datetime.date):
                    row['c'].append({'v': '{0}'.format(col_data)})
                else:
                    row['c'].append({'v': col_data})
            json_data['rows'].append(row)
        return json_data
dataset/ETHPy150Open dpgaspar/Flask-AppBuilder/flask_appbuilder/models/group.py/BaseProcessData.to_json/operator `or` -> `and`