diff --git a/main_yuanyou.py b/main_yuanyou.py index eb9d602..5add65e 100644 --- a/main_yuanyou.py +++ b/main_yuanyou.py @@ -249,7 +249,8 @@ def predict_main(): # 模型报告 logger.info('制作报告ing') title = f'{settings}--{end_time}-预测报告' # 报告标题 - + reportname = f'Brent原油大模型预测--{end_time}.pdf' # 报告文件名 + reportname = reportname.replace(':', '-') # 替换冒号 brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time, reportname=reportname,sqlitedb=sqlitedb), @@ -282,6 +283,6 @@ if __name__ == '__main__': global end_time is_on = True # 遍历2024-11-25 到 2024-12-3 之间的工作日日期 - for i_time in pd.date_range('2024-12-24', '2024-12-25', freq='B'): + for i_time in pd.date_range('2024-12-27', '2024-12-28', freq='B'): end_time = i_time.strftime('%Y-%m-%d') predict_main() diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index 82f66f2..d91afb9 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -237,21 +237,24 @@ def model_losss(sqlitedb,end_time): # 预测数据处理 predict # df_combined = loadcsv(os.path.join(dataset,"cross_validation.csv")) # df_combined = dateConvert(df_combined) - df_combined = sqlitedb.select_data('accuracy') + df_combined = sqlitedb.select_data('accuracy',where_condition=f"created_dt <= '{end_time}'") + df_combined4 = df_combined.copy() # 备份df_combined,后面画图需要 # 删除缺失值大于80%的列 + logger.info(df_combined.shape) df_combined = df_combined.loc[:, df_combined.isnull().mean() < 0.8] + logger.info(df_combined.shape) # 删除缺失值 df_combined.dropna(inplace=True) + logger.info(df_combined.shape) # 其他列转为数值类型 df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in ['CREAT_DATE','ds','created_dt'] }) # 使用 groupby 和 transform 结合 lambda 函数来获取每个分组中 cutoff 的最小值，并创建一个新的列来存储这个最大值 - df_combined['max_cutoff'] = df_combined.groupby('ds')['CREAT_DATE'].transform('min') + df_combined['max_cutoff'] = df_combined.groupby('ds')['CREAT_DATE'].transform('max') # 然后筛选出那些 cutoff 等于 max_cutoff 的行，这样就得到了每个分组中 cutoff 最大的行，并保留了其他列 df_combined = df_combined[df_combined['CREAT_DATE'] == df_combined['max_cutoff']] - df_combined4 = df_combined.copy() # 备份df_combined,后面画图需要 # 删除模型生成的cutoff列 - df_combined.drop(columns=['CREAT_DATE', 'max_cutoff','created_dt','min_within_quantile','max_within_quantile','id','min_price','max_price','LOW_PRICE','HIGH_PRICE'], inplace=True) + df_combined.drop(columns=['CREAT_DATE', 'max_cutoff','created_dt','min_within_quantile','max_within_quantile','id','min_price','max_price','LOW_PRICE','HIGH_PRICE','mean'], inplace=True) # 获取模型名称 modelnames = df_combined.columns.to_list()[1:] if 'y' in modelnames: @@ -333,17 +336,14 @@ def model_losss(sqlitedb,end_time): names_df['columns'] = names_df.apply(add_rote_column, axis=1) def add_upper_lower_bound(row): - + print(row['columns']) + print(type(row['columns'])) # 计算上边界值 - upper_bound = row.max() + upper_bound = df_combined3.loc[row.name,row['columns']].max() # 计算下边界值 - lower_bound = row.min() + lower_bound = df_combined3.loc[row.name,row['columns']].min() return pd.Series([lower_bound, upper_bound], index=['min_within_quantile', 'max_within_quantile']) - - # df_combined3[['min_within_quantile','max_within_quantile']] = names_df.apply(add_upper_lower_bound, axis=1) - - # 取前五最佳模型的最大最小值作为上下边界值 - df_combined3[['min_within_quantile','max_within_quantile']]= df_combined3[modelnames].apply(add_upper_lower_bound, axis=1) + df_combined3[['min_within_quantile','max_within_quantile']] = names_df.apply(add_upper_lower_bound, axis=1) def find_closest_values(row): x = row.y @@ -419,7 +419,7 @@ def model_losss(sqlitedb,end_time): for id in ids: row = predict_y[predict_y['id'] == id] try: - sqlitedb.update_data('accuracy',f"min_price = {row['min_price'].values[0]},max_price = {row['max_price'].values[0]},mean={row['mean']}",f"id = {id}") + sqlitedb.update_data('accuracy',f"min_price = {row['min_price'].values[0]},max_price = {row['max_price'].values[0]},mean={row['mean'].values[0]}",f"id = {id}") except: logger.error(f'更新accuracy表中的min_price,max_price,mean值失败，row={row}') @@ -467,10 +467,8 @@ def model_losss(sqlitedb,end_time): accuracy_rote = 0 for i,group in df3.groupby('CREAT_DATE'): accuracy_rote += (group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1] - df3.to_csv(os.path.join(dataset,f'accuracy_{endtime}.csv'),index=False) df4 = pd.DataFrame(columns=['开始日期','结束日期','准确率']) df4.loc[len(df4)] = {'开始日期':ds_dates[0],'结束日期':ds_dates[-1],'准确率':accuracy_rote} - df4.to_csv(os.path.join(dataset,f'accuracy_rote_{endtime}.csv'),index=False) df4.to_sql("accuracy_rote", con=sqlitedb.connection, if_exists='append', index=False) create_dates,ds_dates = get_week_date(end_time) _get_accuracy_rate(df,create_dates,ds_dates,end_time) @@ -536,18 +534,20 @@ def model_losss(sqlitedb,end_time): plt.xlabel('日期') plt.ylabel('价格') - plt.savefig(os.path.join(dataset,f'{end_time}历史价格-预测值.png'), bbox_inches='tight') + plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight') plt.close() def _plt_modeltopten_predict_ture(df): - lens = df.shape[0] if df.shape[0] < 180 else 90 + df['max_cutoff'] = df.groupby('ds')['CREAT_DATE'].transform('max') + df = df[df['CREAT_DATE'] == df['max_cutoff']] + df['mean'] = df['mean'].astype(float) + lens = df.shape[0] if df.shape[0] < 180 else 180 df = df[-lens:] # 取180个数据点画图 - df['mean_price'] = df[allmodelnames[:10]].mean(axis=1) # 历史价格 plt.figure(figsize=(20, 10)) plt.plot(df['ds'], df['y'], label='真实值') - plt.plot(df['ds'], df['mean_price'], label='模型前十均值', linestyle='--', color='orange') + plt.plot(df['ds'], df['mean'], label='模型前十均值', linestyle='--', color='orange') # 颜色填充 plt.fill_between(df['ds'], df['max_price'], df['min_price'], alpha=0.2) # markers = ['o', 's', '^', 'D', 'v', '*', 'p', 'h', 'H', '+', 'x', 'd'] @@ -568,7 +568,7 @@ def model_losss(sqlitedb,end_time): plt.xlabel('日期') plt.ylabel('价格') - plt.savefig(os.path.join(dataset,f'{end_time}历史价格-预测值1.png'), bbox_inches='tight') + plt.savefig(os.path.join(dataset,'历史价格-预测值1.png'), bbox_inches='tight') plt.close() @@ -935,10 +935,9 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu # except Exception as e: # print(f'绘制第{i+1}个特征{col}与价格散点图时出错：{e}') - - + ### 添加标题 - content.append(Graphs.draw_title(f'{y}{time}预测报告')) + content.append(Graphs.draw_title(f'{y}{end_time}预测报告')) ### 预测结果 content.append(Graphs.draw_little_title('一、预测结果：')) @@ -946,16 +945,14 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu content.append(Graphs.draw_img(os.path.join(dataset,'历史价格-预测值.png'))) # 波动率画图逻辑 content.append(Graphs.draw_text('图示说明：')) - content.append(Graphs.draw_text('1. 确定波动率置信区间：统计近60个交易日的真实价格波动率，找出在 10% ，90% 的分位值作为波动率置信区间;')) - content.append(Graphs.draw_text('2. 确定通道上界：在所有模型的预测结果中 <= 前一天真实价格 乘以 90%的置信波动分位数')) - content.append(Graphs.draw_text('3. 确定通道下界：在所有模型的预测结果中 >= 前一天真实价格 乘以 10%的置信波动分位数')) - content.append(Graphs.draw_text('4. 预测结果没有真实值作为参考依据，通道上界取近60个交易日内预测在上界值的模型对应的预测值，通道下界同理；')) - content.append(Graphs.draw_text('5. 预测结果选用近20个交易日内，最多接近真实值的模型的预测值对应的预测结果；')) - content.append(Graphs.draw_text('6. 预测结果在通道外的，代表最接近真实值的预测结果不在置信波动范围内。')) + content.append(Graphs.draw_text(' 确定波动率置信区间：设置残差置信阈值，以每周最佳模型为基准，选取在置信区间的预测值作为置信区间;')) + # 添加历史走势及预测价格的走势图片 content.append(Graphs.draw_img(os.path.join(dataset,'历史价格-预测值1.png'))) - + content.append(Graphs.draw_text('图示说明：')) + content.append(Graphs.draw_text(' 确定波动率置信区间：使用模型评估指标MAE得到前十个模型，取平均值上下1.5作为价格波动置信区间;')) + # 取df中y列为空的行 import pandas as pd @@ -990,6 +987,8 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu content.append(Graphs.draw_little_title('二、上一预测周期偏差率分析：')) df = pd.read_csv(os.path.join(dataset,'testandpredict_groupby.csv'),encoding='utf-8') df4 = df.copy() # 计算偏差率使用 + # 去掉created_dt 列 + df4 = df4.drop(columns=['created_dt']) # 计算模型偏差率 #计算各列对于y列的差值百分比 df3 = pd.DataFrame() # 存储偏差率 @@ -1134,7 +1133,7 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu eval_df = eval_df.T # df重置索引 eval_df = eval_df.reset_index() - eval_df = eval_df.T + # eval_df = eval_df.T # # 添加表格 data = eval_df.values.tolist() col_width = 500/len(eval_df.columns) diff --git a/测试环境获取市场信息平台数据项.ipynb b/测试环境获取市场信息平台数据项.ipynb index 9d097d1..348ad1d 100644 --- a/测试环境获取市场信息平台数据项.ipynb +++ b/测试环境获取市场信息平台数据项.ipynb @@ -44,31 +44,31 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "id": "a058f507", "metadata": {}, "outputs": [], "source": [ - "login_pushreport_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n", - "upload_url = \"http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave\"\n", - "upload_warning_url = \"http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save\"\n", - "query_data_list_item_nos_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos\"\n", + "# login_pushreport_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n", + "# upload_url = \"http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave\"\n", + "# upload_warning_url = \"http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save\"\n", + "# query_data_list_item_nos_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos\"\n", "\n", - "login_data = {\n", - " \"data\": {\n", - " \"account\": \"api_dev\",\n", - " \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n", - " \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n", - " \"terminal\": \"API\"\n", - " },\n", - " \"funcModule\": \"API\",\n", - " \"funcOperation\": \"获取token\"\n", - "}" + "# login_data = {\n", + "# \"data\": {\n", + "# \"account\": \"api_dev\",\n", + "# \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n", + "# \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n", + "# \"terminal\": \"API\"\n", + "# },\n", + "# \"funcModule\": \"API\",\n", + "# \"funcOperation\": \"获取token\"\n", + "# }" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "id": "8af0311d", "metadata": {}, "outputs": [ @@ -78,7 +78,7 @@ "'http://10.200.32.39/jingbo-api/api/server/login'" ] }, - "execution_count": 4, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "id": "2b330ee3-c006-4ab1-8558-59c51ac8d86f", "metadata": {}, "outputs": [ @@ -104,7 +104,7 @@ " 'funcOperation': '获取token'}" ] }, - "execution_count": 5, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -115,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "id": "dcb6100a-ed2b-4077-a1a9-361c6cb565f9", "metadata": {}, "outputs": [], @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "id": "22c0c7c4", "metadata": {}, "outputs": [ @@ -140,7 +140,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTczNTMxNTg5NywianRpIjoiNDk1NGEyNDYyYThkNDc1YjkwMTMwMDRjNGNlZWY4NTcifQ.ABXdVR-X4ZOtzPJN2slCa1HwqQgmwGjDxOX4aKqaBWU', 'md5Token': 'd870019072ff52333add0545d26398a8'}, 'status': True}\n" + "{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTczNTMxNjAyMywianRpIjoiYjRhYTcxMWI2OTQxNDk3MjkzMWY2MDM2MDg1YWQ1NTIifQ.LP01YpDRKIqnc8Xw9Dzhy7rTV8qgcX5J-0MSlGMFGEo', 'md5Token': 'b3d0f00333a7b1e10844dcd84262326c'}, 'status': True}\n" ] } ], @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "id": "54942e1a", "metadata": {}, "outputs": [ @@ -202,31 +202,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\n", - "404 Not Found\n", - "\n", - "

404 Not Found

\n", - "

The requested URL was not found on this server. Sorry for the inconvenience.
\n", - "Please report this message and include the following information to us.
\n", - "Thank you very much!

\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
URL:http://10.200.32.39/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos
Server:iz2zeb0rw4fi7a0b3dh8jfz
Date:2024/12/27 14:11:26
\n", - "
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] } ], @@ -248,22 +224,28 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "id": "5272c84b", "metadata": {}, "outputs": [ { - "ename": "JSONDecodeError", - "evalue": "Expecting value: line 1 column 1 (char 0)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mJSONDecodeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[11], line 4\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjson\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m json_data \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(items_res\u001b[38;5;241m.\u001b[39mtext)\n\u001b[0;32m 5\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(json_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# 按照dataItemNo 分组 得到多个dataframe ，最后根据dataDate merge 成一个dataframe\u001b[39;00m\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\json\\__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[1;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[0;32m 341\u001b[0m s \u001b[38;5;241m=\u001b[39m s\u001b[38;5;241m.\u001b[39mdecode(detect_encoding(s), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msurrogatepass\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m 344\u001b[0m parse_int \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m parse_float \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m 345\u001b[0m parse_constant \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_pairs_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n\u001b[1;32m--> 346\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _default_decoder\u001b[38;5;241m.\u001b[39mdecode(s)\n\u001b[0;32m 347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 348\u001b[0m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m JSONDecoder\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\json\\decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[1;34m(self, s, _w)\u001b[0m\n\u001b[0;32m 332\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecode\u001b[39m(\u001b[38;5;28mself\u001b[39m, s, _w\u001b[38;5;241m=\u001b[39mWHITESPACE\u001b[38;5;241m.\u001b[39mmatch):\n\u001b[0;32m 333\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[0;32m 334\u001b[0m \u001b[38;5;124;03m containing a JSON document).\u001b[39;00m\n\u001b[0;32m 335\u001b[0m \n\u001b[0;32m 336\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 337\u001b[0m obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw_decode(s, idx\u001b[38;5;241m=\u001b[39m_w(s, \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mend())\n\u001b[0;32m 338\u001b[0m end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n\u001b[0;32m 339\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(s):\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\json\\decoder.py:355\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[1;34m(self, s, idx)\u001b[0m\n\u001b[0;32m 353\u001b[0m obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscan_once(s, idx)\n\u001b[0;32m 354\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m--> 355\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m JSONDecodeError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpecting value\u001b[39m\u001b[38;5;124m\"\u001b[39m, s, err\u001b[38;5;241m.\u001b[39mvalue) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj, end\n", - "\u001b[1;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)" + "name": "stdout", + "output_type": "stream", + "text": [ + " dataDate Brentzdj Brentzgj\n", + "0 20240709 84.53 NaN\n", + "1 20241224 72.46 NaN\n", + "2 20200102 65.72 NaN\n", + "3 20200103 66.22 NaN\n", + "4 20200106 68.21 NaN\n", + "... ... ... ...\n", + "2563 20240814 NaN 81.44\n", + "2564 20240806 NaN 77.85\n", + "2565 20240807 NaN 78.87\n", + "2566 20240808 NaN 79.37\n", + "2567 20240426 NaN 88.64\n", + "\n", + "[2568 rows x 3 columns]\n" ] } ],