diff --git a/lib/tools.py b/lib/tools.py
index 3536cce..5a4a043 100644
--- a/lib/tools.py
+++ b/lib/tools.py
@@ -959,7 +959,7 @@ def find_best_models(date='', global_config=None):
             ciridate = last_trading_day_str
             global_config['logger'].info(f'计算预测{last_trading_day}的次日{last_trading_day}最佳模型')
             global_config['logger'].info(
-                f'{date}真实价格：{true_price[true_price["ds"] == last_trading_day_str]["y"].values[0]}')
+                f'{date}真实价格：{true_price[true_price["ds"] == ciridate]["y"].values[0]}')
             price = df[['data_date', wd, 'model_id']]
             price = price[(price['data_date'] == ciridate)
                           | (price['data_date'] == date)]
@@ -1098,7 +1098,7 @@ def plot_pp_predict_result(y_hat, global_config,wd='yuedu'):
         y = pd.read_csv('juxitingdataset/指标数据.csv')[['ds', 'y']][-30:]
         xgx_df = pd.read_csv('juxitingdataset/相关系数.csv')
     xgx_df = xgx_df.rename(columns={xgx_df.columns[0]: '指标', xgx_df.columns[1]: '系数'})
-    top_10_correlations = xgx_df.sort_values(by='系数', ascending=False)[1:11].round().astype({'系数': int})
+    top_10_correlations = xgx_df.sort_values(by='系数', ascending=False)[1:11].round(2)
     y['ds'] = pd.to_datetime(y['ds'])
     y = y[y['ds'] < y_hat['ds'].iloc[0]]
 
@@ -1138,7 +1138,7 @@ def plot_pp_predict_result(y_hat, global_config,wd='yuedu'):
                           'next_march_price': '次三月', 'next_april_price': '次四月',
                           }, inplace=True)
     columns = y_hat.columns.tolist()
-    data = [round(num) for num in y_hat.values.ravel().tolist()]
+    data = [[round(num) for num in y_hat.values.ravel().tolist()]]
 
     # 将日期转换为字符串格式
     for row in data: