def METHOD_NAME(self):
    P_loss_kWh = self.P_loss_kWh.fillna(value=0)
    P_loss_kWh = pd.DataFrame(P_loss_kWh.sum(axis=0), columns=['P_loss_kWh'])  # format individual loss data
    Q_loss_kWh = abs(self.thermal_loss_edges_kWh).fillna(value=0)
    Q_loss_kWh = pd.DataFrame(Q_loss_kWh.sum(axis=0), columns=['Q_loss_kWh'])  # format individual loss data
    total_df = pd.DataFrame(P_loss_kWh.values + Q_loss_kWh.values, index=Q_loss_kWh.index,
                         columns=['total'])  # calculate total loss
    merged_df = P_loss_kWh.join(Q_loss_kWh).join(total_df)
    anchors = []
    load_names = []
    median = []
    peak = []
    total_perc = []
    for field in ['P_loss_kWh', 'Q_loss_kWh']:
        # calculate graph
        anchors.append(', '.join(calc_top_three_anchor_loads(merged_df, field)))
        load_names.append(NAMING[field])  # get correct name
        median.append(round(merged_df[field].median(), 2))  # calculate median
        peak.append(round(merged_df[field].abs().max(), 2))  # calculate peak value
        local_total = round(merged_df[field].sum(), 2)  # calculate total for this building
        total_perc.append(str(local_total) + " (" + str(
            min((local_total / total_df.sum().values * 100).round(1),
                100.0)) + " %)")  # transform value to percentage
    column_names = ['Loss Name', 'Total [kWh/yr]', 'Peak [kWh/yr]', 'Median [kWh/yr]', 'Highest 3 Losses']
    column_values = [load_names, total_perc, peak, median, anchors]
    table_df = pd.DataFrame({cn: cv for cn, cv in zip(column_names, column_values)}, columns=column_names)
    return table_df