补充9月数据

浙江行业电量/输出为3.py

@@ -52,6 +52,7 @@ def run(file_dir,excel):
 
     data.drop(columns=[i for i in data.columns if (data[i] == 0).sum() / len(data) >= 0.5], inplace=True)  # 去除0值列
     print('len(data):', len(data))
+    list_app = []
     for industry in data.columns:
         df = data[industry]
         df = df[df.values != 0]  # 去除0值行
@@ -117,7 +118,7 @@ def run(file_dir,excel):
 
 
         # 创建测试集
-        result_list = []
+        # result_list = []
         # 以x为基础实际数据，滚动预测未来3天
         x = torch.from_numpy(df[-14:-4]).to(device)
         pred = model(x.reshape(-1,1,DAYS_FOR_TRAIN)).view(-1).detach().numpy()
@@ -141,9 +142,12 @@ def run(file_dir,excel):
         result_eight = pd.DataFrame({'pred': np.round(pred,1),'real': df[-3:]})
         target = (result_eight['pred'].sum() - result_eight['real'].sum()) / df[-31:].sum()
         result_eight['loss_rate'] = round(target, 5)
+        result_eight['industry'] = industry
+        list_app.append(result_eight)
         print(target)
         print(result_eight)
-
+    final_df = pd.concat(list_app,ignore_index=True)
+    print(final_df)
 
         # result_eight.to_csv(f'./月底预测结果/9月{excel[:2]}.txt', sep='\t', mode='a')
         # with open(fr'./偏差/9月底偏差率.txt', 'a', encoding='utf-8') as f: