diff --git a/aisenzhecode/沥青/定性模型数据项12-11.xls b/aisenzhecode/沥青/定性模型数据项12-11.xls index a7e4b8b..8a5346d 100644 Binary files a/aisenzhecode/沥青/定性模型数据项12-11.xls and b/aisenzhecode/沥青/定性模型数据项12-11.xls differ diff --git a/aisenzhecode/沥青/日度价格预测_最佳模型.pkl b/aisenzhecode/沥青/日度价格预测_最佳模型.pkl index 99ad69c..987c812 100644 Binary files a/aisenzhecode/沥青/日度价格预测_最佳模型.pkl and b/aisenzhecode/沥青/日度价格预测_最佳模型.pkl differ diff --git a/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb b/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb index ca9cba7..331f99e 100644 --- a/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb +++ b/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -211,7 +211,6 @@ " \n", " \n", " \n", - " \n", "def start_1():\n", " workbook = xlrd.open_workbook(read_file_path_name)\n", "\n", @@ -472,22 +471,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "20241209\n" + "20241217\n" ] } ], "source": [ "from datetime import datetime, timedelta\n", "\n", - "start_date = datetime(2024, 12, 9)\n", - "end_date = datetime(2024, 12, 10)\n", + "start_date = datetime(2024, 12, 17)\n", + "end_date = datetime(2024, 12, 18)\n", "\n", "while start_date < end_date:\n", " print(start_date.strftime('%Y%m%d'))\n", diff --git a/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb b/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb index 0d58135..1bc0950 100644 --- a/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb +++ b/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb @@ -795,14 +795,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "20241212\n" + "20241217\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19760\\2239815117.py:299: UserWarning:\n", + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_4500\\2239815117.py:299: UserWarning:\n", "\n", "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", "\n" @@ -812,7 +812,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Using matplotlib backend: \n", + "Using matplotlib backend: \n", "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n", "Populating the interactive namespace from numpy and matplotlib\n", "Fitting 3 folds for each of 180 candidates, totalling 540 fits\n" @@ -824,7 +824,7 @@ "text": [ "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\magics\\pylab.py:162: UserWarning:\n", "\n", - "pylab import has clobbered these variables: ['datetime', 'plot', 'random', '__version__']\n", + "pylab import has clobbered these variables: ['__version__', 'plot', 'random', 'datetime']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", "\n" ] @@ -844,108 +844,33 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19760\\2239815117.py:239: UserWarning:\n", + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_4500\\2239815117.py:239: UserWarning:\n", "\n", "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "日期\n", - "2024-12-12 3552.045898\n", - "Name: 日度预测价格, dtype: float32\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19760\\2239815117.py:273: FutureWarning:\n", + "\n", + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_4500\\2239815117.py:273: FutureWarning:\n", "\n", "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", "\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\"confirmFlg\":false,\"status\":true}\n", - "新增数据： ['2024-12-12', 7957.0, 7066.0, 0.1, 0.0, 3650.0, 0.9, 0.0, 0.0, 3540.0, 7.9, 0.2, 0.2, 3500.0, 0.6, '', 3500.0, 71.05, 73.53, '', 3510.0, 29.5977, '', '', '', '', 229522.1, 11025.3, 3426.4568, '', '', 42263.29278, 6647.44, 3970.0]\n", - "20241213\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19760\\2239815117.py:299: UserWarning:\n", - "\n", - "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using matplotlib backend: QtAgg\n", - "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n", - "Populating the interactive namespace from numpy and matplotlib\n", - "Fitting 3 folds for each of 180 candidates, totalling 540 fits\n", - "Best score: 0.997\n", - "Best parameters set:\n", - "\tlearning_rate: 0.1\n", - "\tmax_depth: 8\n", - "\tn_estimators: 100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19760\\2239815117.py:239: UserWarning:\n", - "\n", - "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", - "\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ "日期\n", - "2024-12-13 3504.912354\n", - "Name: 日度预测价格, dtype: float32\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19760\\2239815117.py:273: FutureWarning:\n", - "\n", - "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "2024-12-17 3501.835693\n", + "Name: 日度预测价格, dtype: float32\n", "{\"confirmFlg\":false,\"status\":true}\n", - "新增数据： ['2024-12-13', 7957.0, 7066.0, 0.1, 0.0, 3650.0, 0.55, 0.0, 0.0, 3540.0, 7.9, 0.2, 0.2, 3600.0, 0.6, '', 3500.0, 71.05, 73.41, '', 3512.0, 29.5977, '', '', '', '', 229522.1, 8091.12, 3417.4442, '', '', 41436.2654696, 6913.5165, '']\n" + "新增数据： ['2024-12-17', 7957.0, 6984.0, 0.0, 0.25, 3650.0, 0.55, 0.0, 0.0, 3520.0, 7.9, 0.2, 0.2, 3500.0, 0.75, '', 3500.0, 74.35, 72.69, '', 3522.0, 28.8998, '', '', '', 13.58990112, 229522.1, 7722.02, 3511.1707, '', '', 40514.8218813, 7088.86, '']\n" ] } ], "source": [ "from datetime import datetime, timedelta\n", "\n", - "start_date = datetime(2024, 12, 12)\n", - "end_date = datetime(2024, 12, 14)\n", + "start_date = datetime(2024, 12, 17)\n", + "end_date = datetime(2024, 12, 18)\n", "\n", "while start_date < end_date:\n", " print(start_date.strftime('%Y%m%d'))\n", diff --git a/aisenzhecode/沥青/沥青数据项.xls b/aisenzhecode/沥青/沥青数据项.xls index ce5e7e6..c008979 100644 Binary files a/aisenzhecode/沥青/沥青数据项.xls and b/aisenzhecode/沥青/沥青数据项.xls differ diff --git a/auptest.py b/auptest.py index 543054e..da53b5a 100644 --- a/auptest.py +++ b/auptest.py @@ -34,14 +34,15 @@ auth = HttpNtlmAuth(f'{graphql_username}', f'{graphql_password}') # 请求头设置 headers = {'content-type': 'application/json;charset=UTF-8'} - def insert_api_log(request_time, request_url, request_method, request_params, response_content, response_time): ''' 请求日志表 v_tbl_aup_api_log 写入 ''' try: # 建立数据库连接 - cnx = mysql.connector.connect(**config) + global cnx + if cnx is None: + cnx = mysql.connector.connect(**config) cursor = cnx.cursor() # 先查询表中已有记录的数量，用于生成新记录的ID # count_query = "SELECT max(ID) FROM v_tbl_aup_api_log" @@ -70,12 +71,14 @@ def insert_api_log(request_time, request_url, request_method, request_params, re print(f"Error: {err}") finally: # 关闭游标和连接 - if cursor: - cursor.close() - if cnx: - cnx.close() + try: + if cursor: + cursor.close() + except UnboundLocalError: + pass +cnx = None tags_metadata = [ @@ -369,8 +372,7 @@ async def generate_graphql_query( full_path = str(request.url.path) session = requests.Session() try: - response = await session.post(url=url, headers=headers, json=payload_json, auth=auth, verify=False, timeout=300) - print(type(response)) + response = session.post(url=url, headers=headers, json=payload_json, auth=auth, verify=False, timeout=300) except requests.exceptions.ConnectTimeout as e: # 构造符合错误情况的响应数据字典 error_response_data = { @@ -408,12 +410,13 @@ async def generate_graphql_query( if response.status_code!= 200: raise HTTPException(status_code=response.status_code, detail=response.text) + print(response.json()) return response.json() except TypeError as e: return {"error": str(e)} @app.get("/get_cases",tags=['get_cases']) -async def post_cases_query_async(request: Request): +async def get_cases_query_async(request: Request): payload_json2 = { "query": templates.case_qurey } @@ -421,7 +424,12 @@ async def post_cases_query_async(request: Request): request_time = datetime.now() session = requests.Session() try: - response = await session.post(url=url, headers=headers, json=payload_json2, auth=auth, verify=False) + response = session.post(url=url, headers=headers, json=payload_json2, auth=auth, verify=False) + # 将JSON字符串解析为Python字典对象 + res = response.json() + # # 提取name列表 + # name_list = [item["name"] for item in res["data"]["cases"]["items"]] + # res['name_list'] = name_list except requests.exceptions.ConnectTimeout as e: # 构造符合错误情况的响应数据字典 error_response_data = { @@ -429,8 +437,8 @@ async def post_cases_query_async(request: Request): "data": {}, "status_code": 503 # 使用合适的状态码，如503表示服务暂时不可用，可根据具体错误类型调整 } - response = error_response_data - raise HTTPException(status_code=503, detail=response) # 抛出合适状态码的HTTPException + res = error_response_data + raise HTTPException(status_code=503, detail=res) # 抛出合适状态码的HTTPException except requests.exceptions.RequestException as e: # 捕获其他请求相关的异常，统一处理 error_response_data = { @@ -439,14 +447,10 @@ async def post_cases_query_async(request: Request): "status_code": 500 } - response = error_response_data - raise HTTPException(status_code=500, detail=response) + res = error_response_data + raise HTTPException(status_code=500, detail=res) finally: response_time = datetime.now() - try: - res = response.json() - except (UnboundLocalError,AttributeError): - res = response # 调用插入日志的函数，将相关信息记录到数据库中（假设insert_api_log函数已正确定义且可访问） insert_api_log( request_time, @@ -460,16 +464,11 @@ async def post_cases_query_async(request: Request): if response.status_code!= 200: raise HTTPException(status_code=response.status_code, detail=response.text) - # 将JSON字符串解析为Python字典对象 - data_dict = response.json() - # 提取name列表 - name_list = [item["name"] for item in data_dict["data"]["cases"]["items"]] - data_dict['name_lsit'] = name_list - return json.dumps(data_dict) + return res if __name__ == "__main__": import uvicorn - uvicorn.run(app, host="127.0.0.1", port=8003) + uvicorn.run(app, host="0.0.0.0", port=8003) # query = """ diff --git a/原油价格预测准确率计算.ipynb b/原油价格预测准确率计算.ipynb index e024d65..3b669f3 100644 --- a/原油价格预测准确率计算.ipynb +++ b/原油价格预测准确率计算.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 13, + "execution_count": 41, "id": "9daadf20-caa6-4b25-901c-6cc3ef563f58", "metadata": {}, "outputs": [ @@ -126,19 +126,19 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 79, "id": "0d77ab7d", "metadata": {}, "outputs": [], "source": [ "# 模型评估前五均值 \n", - "df['min_price'] = df.iloc[:,1:6].mean(axis=1) -1.5\n", - "df['max_price'] = df.iloc[:,1:6].mean(axis=1) +1.5" + "df['min_price'] = df.iloc[:,1:11].mean(axis=1) -1\n", + "df['max_price'] = df.iloc[:,1:11].mean(axis=1) +1" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 80, "id": "e51c3fd0-6bff-45de-b8b6-971e7986c7a7", "metadata": {}, "outputs": [ @@ -146,114 +146,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " 开始日期 结束日期 准确率\n", - "0 2024-09-27 2024-10-04 0\n", - " 开始日期 结束日期 准确率\n", - "0 2024-09-27 2024-10-04 0\n", - " 开始日期 结束日期 准确率\n", - "0 2024-09-27 2024-10-04 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 开始日期 结束日期 准确率\n", - "0 2024-09-27 2024-10-04 0\n", - " 开始日期 结束日期 准确率\n", - "0 2024-09-27 2024-10-04 0\n", " 开始日期 结束日期 准确率\n", - "0 2024-10-04 2024-10-11 0.433988\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-04 2024-10-11 0.433988\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-04 2024-10-11 0.433988\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-04 2024-10-11 0.433988\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-04 2024-10-11 0.433988\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-11 2024-10-18 0.367557\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-11 2024-10-18 0.367557\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-11 2024-10-18 0.367557\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-11 2024-10-18 0.367557\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-11 2024-10-18 0.367557\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-18 2024-10-25 0.342808\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-18 2024-10-25 0.342808\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-18 2024-10-25 0.342808\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-18 2024-10-25 0.342808\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-18 2024-10-25 0.342808\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-25 2024-11-01 0.397058\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-25 2024-11-01 0.397058\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-25 2024-11-01 0.397058\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-25 2024-11-01 0.397058\n", - " 开始日期 结束日期 准确率\n", - "0 2024-10-25 2024-11-01 0.397058\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-01 2024-11-08 0.666605\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-01 2024-11-08 0.666605\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-01 2024-11-08 0.666605\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-01 2024-11-08 0.666605\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-01 2024-11-08 0.666605\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-08 2024-11-15 0.805488\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-08 2024-11-15 0.805488\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-08 2024-11-15 0.805488\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-08 2024-11-15 0.805488\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-08 2024-11-15 0.805488\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-15 2024-11-22 0.744558\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-15 2024-11-22 0.744558\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-15 2024-11-22 0.744558\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-15 2024-11-22 0.744558\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-15 2024-11-22 0.744558\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-22 2024-11-29 0.351228\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-22 2024-11-29 0.351228\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-22 2024-11-29 0.351228\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-22 2024-11-29 0.351228\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-22 2024-11-29 0.351228\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-29 2024-12-06 0.727334\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-29 2024-12-06 0.727334\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-29 2024-12-06 0.727334\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-29 2024-12-06 0.727334\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-29 2024-12-06 0.727334\n", - " 开始日期 结束日期 准确率\n", - "0 2024-12-06 2024-12-13 0.835391\n" + "0 2024-09-30 2024-10-04 0\n", + "0 2024-10-07 2024-10-11 0.509306\n", + "0 2024-10-14 2024-10-18 0.498337\n", + "0 2024-10-21 2024-10-25 0.437821\n", + "0 2024-10-28 2024-11-01 0.436979\n", + "0 2024-11-04 2024-11-08 0.482764\n", + "0 2024-11-11 2024-11-15 0.682747\n", + "0 2024-11-18 2024-11-22 0.694599\n", + "0 2024-11-25 2024-11-29 0.380507\n", + "0 2024-12-02 2024-12-06 0.732552\n", + "0 2024-12-09 2024-12-13 0.559709\n" ] } ], @@ -288,40 +192,50 @@ " endtime = end_time\n", " endtimeweek = datetime.datetime.strptime(endtime, '%Y-%m-%d')\n", " up_week = endtimeweek - datetime.timedelta(days=endtimeweek.weekday() + 14)\n", - " up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(14)][4:-2]\n", - " up_week_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates]\n", - " return up_week_dates\n", + " up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(14)]\n", + " create_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates[4:-3]]\n", + " ds_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates[-7:-2]]\n", + " return create_dates,ds_dates\n", "\n", "# 计算准确率并保存结果\n", - "def _get_accuracy_rate(df,up_week_dates,endtime):\n", + "def _get_accuracy_rate(df,create_dates,ds_dates,endtime):\n", " df3 = df.copy()\n", - " df3 = df3[df3['CREAT_DATE'].isin(up_week_dates)]\n", - " df3 = df3[df3['ds'].isin(up_week_dates)]\n", + " df3 = df3[df3['CREAT_DATE'].isin(create_dates)]\n", + " df3 = df3[df3['ds'].isin(ds_dates)]\n", " accuracy_rote = 0\n", - " for i,group in df3.groupby('ds'):\n", + " for i,group in df3.groupby('CREAT_DATE'):\n", + " # print('日期：',i)\n", + " # print(group)\n", " # print('权重：',weight_dict[len(group)-1])\n", " # print('准确率：',(group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1])\n", " accuracy_rote += (group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1]\n", " df3.to_csv(os.path.join(dataset,f'accuracy_{endtime}.csv'),index=False)\n", " df4 = pd.DataFrame(columns=['开始日期','结束日期','准确率'])\n", - " df4.loc[len(df4)] = {'开始日期':up_week_dates[0],'结束日期':up_week_dates[-1],'准确率':accuracy_rote}\n", + " df4.loc[len(df4)] = {'开始日期':ds_dates[0],'结束日期':ds_dates[-1],'准确率':accuracy_rote}\n", " df4.to_csv(os.path.join(dataset,f'accuracy_rote_{endtime}.csv'),index=False)\n", - " print(df4)\n", + " # print(df4)\n", + " return df4\n", " # df4.to_sql(\"accuracy_rote\", con=sqlitedb.connection, if_exists='append', index=False)\n", "\n", "\n", "end_times = df['CREAT_DATE'].unique()\n", + "df7 = pd.DataFrame(columns=['开始日期','结束日期','准确率'])\n", "for endtime in end_times:\n", - " up_week_dates = get_week_date(endtime)\n", - " _get_accuracy_rate(df,up_week_dates,endtime)\n", - "\n", + " create_dates,ds_dates = get_week_date(endtime)\n", + " df8 = _get_accuracy_rate(df,create_dates,ds_dates,endtime)\n", + " df7 = pd.concat([df7,df8],axis=0)\n", + "df7.to_csv(os.path.join(dataset,f'accuracy_rote_all.csv'),index=False)\n", + "# print(df7)\n", + "# df7去重\n", + "df7 = df7.drop_duplicates()\n", + "print(df7)\n", "# 打印结果\n", - "\n" + " \n" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 62, "id": "0f942c69", "metadata": {}, "outputs": [ @@ -330,18 +244,48 @@ "output_type": "stream", "text": [ "(255, 6)\n", - " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", - "0 2024-10-08 78.172055 81.172055 76.36 76.36 2024-10-07\n", - "1 2024-10-09 78.017734 81.017734 75.15 75.15 2024-10-07\n", - "2 2024-10-10 78.196563 81.196563 76.72 76.72 2024-10-07\n", - "3 2024-10-11 78.244970 81.244970 78.04 78.04 2024-10-07\n", - "4 2024-10-14 78.448240 81.448240 74.86 74.86 2024-10-07\n", - "5 2024-10-09 75.442758 78.442758 75.15 75.15 2024-10-08\n", - "6 2024-10-10 75.684414 78.684414 76.72 76.72 2024-10-08\n", - "7 2024-10-11 75.675482 78.675482 78.04 78.04 2024-10-08\n", - "8 2024-10-14 75.819379 78.819379 74.86 74.86 2024-10-08\n", - "9 2024-10-15 75.830953 78.830953 73.34 73.34 2024-10-08\n", - "['2024-10-07', '2024-10-08', '2024-10-09', '2024-10-10', '2024-10-11', '2024-10-12', '2024-10-13']\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "0 2024-10-08 78.172055 81.172055 76.36 76.36 2024-10-07\n", + "1 2024-10-09 78.017734 81.017734 75.15 75.15 2024-10-07\n", + "2 2024-10-10 78.196563 81.196563 76.72 76.72 2024-10-07\n", + "3 2024-10-11 78.244970 81.244970 78.04 78.04 2024-10-07\n", + "4 2024-10-14 78.448240 81.448240 74.86 74.86 2024-10-07\n", + "5 2024-10-09 75.442758 78.442758 75.15 75.15 2024-10-08\n", + "6 2024-10-10 75.684414 78.684414 76.72 76.72 2024-10-08\n", + "7 2024-10-11 75.675482 78.675482 78.04 78.04 2024-10-08\n", + "8 2024-10-14 75.819379 78.819379 74.86 74.86 2024-10-08\n", + "9 2024-10-15 75.830953 78.830953 73.34 73.34 2024-10-08\n", + "10 2024-10-10 75.439643 78.439643 76.72 76.72 2024-10-09\n", + "11 2024-10-11 75.214194 78.214194 78.04 78.04 2024-10-09\n", + "12 2024-10-14 75.775363 78.775363 74.86 74.86 2024-10-09\n", + "13 2024-10-15 75.869936 78.869936 73.34 73.34 2024-10-09\n", + "14 2024-10-16 76.203067 79.203067 73.42 73.42 2024-10-09\n", + "15 2024-10-11 77.558740 80.558740 78.04 78.04 2024-10-10\n", + "16 2024-10-14 77.326639 80.326639 74.86 74.86 2024-10-10\n", + "17 2024-10-15 77.711800 80.711800 73.34 73.34 2024-10-10\n", + "18 2024-10-16 78.129062 81.129062 73.42 73.42 2024-10-10\n", + "19 2024-10-17 78.237173 81.237173 73.26 73.26 2024-10-10\n", + "['2024-09-27', '2024-09-28', '2024-09-29', '2024-09-30', '2024-10-01', '2024-10-02', '2024-10-03', '2024-10-04']\n", + "(0, 6)\n", + "Empty DataFrame\n", + "Columns: [ds, min_price, max_price, LOW_PRICE, LOW_PRICE, CREAT_DATE]\n", + "Index: []\n", + "['2024-09-27', '2024-09-28', '2024-09-29', '2024-09-30', '2024-10-01', '2024-10-02', '2024-10-03', '2024-10-04']\n", + "(0, 6)\n", + "Empty DataFrame\n", + "Columns: [ds, min_price, max_price, LOW_PRICE, LOW_PRICE, CREAT_DATE]\n", + "Index: []\n", + "['2024-09-27', '2024-09-28', '2024-09-29', '2024-09-30', '2024-10-01', '2024-10-02', '2024-10-03', '2024-10-04']\n", + "(0, 6)\n", + "Empty DataFrame\n", + "Columns: [ds, min_price, max_price, LOW_PRICE, LOW_PRICE, CREAT_DATE]\n", + "Index: []\n", + "['2024-09-27', '2024-09-28', '2024-09-29', '2024-09-30', '2024-10-01', '2024-10-02', '2024-10-03', '2024-10-04']\n", + "(0, 6)\n", + "Empty DataFrame\n", + "Columns: [ds, min_price, max_price, LOW_PRICE, LOW_PRICE, CREAT_DATE]\n", + "Index: []\n", + "['2024-10-04', '2024-10-05', '2024-10-06', '2024-10-07', '2024-10-08', '2024-10-09', '2024-10-10', '2024-10-11']\n", "(10, 6)\n", " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", "0 2024-10-08 78.172055 81.172055 76.36 76.36 2024-10-07\n", @@ -353,34 +297,1100 @@ "7 2024-10-11 75.675482 78.675482 78.04 78.04 2024-10-08\n", "10 2024-10-10 75.439643 78.439643 76.72 76.72 2024-10-09\n", "11 2024-10-11 75.214194 78.214194 78.04 78.04 2024-10-09\n", - "15 2024-10-11 77.558740 80.558740 78.04 78.04 2024-10-10\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Per-column arrays must each be 1-dimensional", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[31], line 33\u001b[0m\n\u001b[0;32m 31\u001b[0m sns\u001b[38;5;241m.\u001b[39mscatterplot(x\u001b[38;5;241m=\u001b[39mdf6\u001b[38;5;241m.\u001b[39mindex,y\u001b[38;5;241m=\u001b[39mdf6[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin_price\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues,data\u001b[38;5;241m=\u001b[39mdf6)\n\u001b[0;32m 32\u001b[0m sns\u001b[38;5;241m.\u001b[39mscatterplot(x\u001b[38;5;241m=\u001b[39mdf6\u001b[38;5;241m.\u001b[39mindex,y\u001b[38;5;241m=\u001b[39mdf6[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax_price\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues,data\u001b[38;5;241m=\u001b[39mdf6)\n\u001b[1;32m---> 33\u001b[0m sns\u001b[38;5;241m.\u001b[39mscatterplot(x\u001b[38;5;241m=\u001b[39mdf6\u001b[38;5;241m.\u001b[39mindex,y\u001b[38;5;241m=\u001b[39mdf6[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLOW_PRICE\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues,data\u001b[38;5;241m=\u001b[39mdf6)\n\u001b[0;32m 34\u001b[0m sns\u001b[38;5;241m.\u001b[39mscatterplot(x\u001b[38;5;241m=\u001b[39mdf6\u001b[38;5;241m.\u001b[39mindex,y\u001b[38;5;241m=\u001b[39mdf6[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHIGH_PRICE\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues,data\u001b[38;5;241m=\u001b[39mdf6)\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\relational.py:742\u001b[0m, in \u001b[0;36mscatterplot\u001b[1;34m(data, x, y, hue, size, style, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, style_order, legend, ax, **kwargs)\u001b[0m\n\u001b[0;32m 732\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatterplot\u001b[39m(\n\u001b[0;32m 733\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m 734\u001b[0m x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, style\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 738\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m 739\u001b[0m ):\n\u001b[0;32m 741\u001b[0m variables \u001b[38;5;241m=\u001b[39m _ScatterPlotter\u001b[38;5;241m.\u001b[39mget_semantics(\u001b[38;5;28mlocals\u001b[39m())\n\u001b[1;32m--> 742\u001b[0m p \u001b[38;5;241m=\u001b[39m _ScatterPlotter(data\u001b[38;5;241m=\u001b[39mdata, variables\u001b[38;5;241m=\u001b[39mvariables, legend\u001b[38;5;241m=\u001b[39mlegend)\n\u001b[0;32m 744\u001b[0m p\u001b[38;5;241m.\u001b[39mmap_hue(palette\u001b[38;5;241m=\u001b[39mpalette, order\u001b[38;5;241m=\u001b[39mhue_order, norm\u001b[38;5;241m=\u001b[39mhue_norm)\n\u001b[0;32m 745\u001b[0m p\u001b[38;5;241m.\u001b[39mmap_size(sizes\u001b[38;5;241m=\u001b[39msizes, order\u001b[38;5;241m=\u001b[39msize_order, norm\u001b[38;5;241m=\u001b[39msize_norm)\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\relational.py:538\u001b[0m, in \u001b[0;36m_ScatterPlotter.__init__\u001b[1;34m(self, data, variables, legend)\u001b[0m\n\u001b[0;32m 529\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, variables\u001b[38;5;241m=\u001b[39m{}, legend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 530\u001b[0m \n\u001b[0;32m 531\u001b[0m \u001b[38;5;66;03m# TODO this is messy, we want the mapping to be agnostic about\u001b[39;00m\n\u001b[0;32m 532\u001b[0m \u001b[38;5;66;03m# the kind of plot to draw, but for the time being we need to set\u001b[39;00m\n\u001b[0;32m 533\u001b[0m \u001b[38;5;66;03m# this information so the SizeMapping can use it\u001b[39;00m\n\u001b[0;32m 534\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_default_size_range \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 535\u001b[0m np\u001b[38;5;241m.\u001b[39mr_[\u001b[38;5;241m.5\u001b[39m, \u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39msquare(mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlines.markersize\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 536\u001b[0m )\n\u001b[1;32m--> 538\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(data\u001b[38;5;241m=\u001b[39mdata, variables\u001b[38;5;241m=\u001b[39mvariables)\n\u001b[0;32m 540\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegend \u001b[38;5;241m=\u001b[39m legend\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:640\u001b[0m, in \u001b[0;36mVectorPlotter.__init__\u001b[1;34m(self, data, variables)\u001b[0m\n\u001b[0;32m 635\u001b[0m \u001b[38;5;66;03m# var_ordered is relevant only for categorical axis variables, and may\u001b[39;00m\n\u001b[0;32m 636\u001b[0m \u001b[38;5;66;03m# be better handled by an internal axis information object that tracks\u001b[39;00m\n\u001b[0;32m 637\u001b[0m \u001b[38;5;66;03m# such information and is set up by the scale_* methods. The analogous\u001b[39;00m\n\u001b[0;32m 638\u001b[0m \u001b[38;5;66;03m# information for numeric axes would be information about log scales.\u001b[39;00m\n\u001b[0;32m 639\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_var_ordered \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m} \u001b[38;5;66;03m# alt., used DefaultDict\u001b[39;00m\n\u001b[1;32m--> 640\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39massign_variables(data, variables)\n\u001b[0;32m 642\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var, \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_semantic_mappings\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 643\u001b[0m \n\u001b[0;32m 644\u001b[0m \u001b[38;5;66;03m# Create the mapping function\u001b[39;00m\n\u001b[0;32m 645\u001b[0m map_func \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mmap, plotter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:701\u001b[0m, in \u001b[0;36mVectorPlotter.assign_variables\u001b[1;34m(self, data, variables)\u001b[0m\n\u001b[0;32m 699\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_format \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlong\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 701\u001b[0m plot_data, variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_assign_variables_longform(\n\u001b[0;32m 702\u001b[0m data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mvariables,\n\u001b[0;32m 703\u001b[0m )\n\u001b[0;32m 705\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot_data \u001b[38;5;241m=\u001b[39m plot_data\n\u001b[0;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables \u001b[38;5;241m=\u001b[39m variables\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:962\u001b[0m, in \u001b[0;36mVectorPlotter._assign_variables_longform\u001b[1;34m(self, data, **kwargs)\u001b[0m\n\u001b[0;32m 958\u001b[0m variables[key] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(val, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 960\u001b[0m \u001b[38;5;66;03m# Construct a tidy plot DataFrame. This will convert a number of\u001b[39;00m\n\u001b[0;32m 961\u001b[0m \u001b[38;5;66;03m# types automatically, aligning on index in case of pandas objects\u001b[39;00m\n\u001b[1;32m--> 962\u001b[0m plot_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(plot_data)\n\u001b[0;32m 964\u001b[0m \u001b[38;5;66;03m# Reduce the variables dictionary to fields with valid data\u001b[39;00m\n\u001b[0;32m 965\u001b[0m variables \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 966\u001b[0m var: name\n\u001b[0;32m 967\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var, name \u001b[38;5;129;01min\u001b[39;00m variables\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m 968\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m plot_data[var]\u001b[38;5;241m.\u001b[39mnotnull()\u001b[38;5;241m.\u001b[39many()\n\u001b[0;32m 969\u001b[0m }\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:733\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m 727\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_mgr(\n\u001b[0;32m 728\u001b[0m data, axes\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m: index, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: columns}, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy\n\u001b[0;32m 729\u001b[0m )\n\u001b[0;32m 731\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m 732\u001b[0m \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[1;32m--> 733\u001b[0m mgr \u001b[38;5;241m=\u001b[39m dict_to_mgr(data, index, columns, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, typ\u001b[38;5;241m=\u001b[39mmanager)\n\u001b[0;32m 734\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma\u001b[38;5;241m.\u001b[39mMaskedArray):\n\u001b[0;32m 735\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mma\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mrecords\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:503\u001b[0m, in \u001b[0;36mdict_to_mgr\u001b[1;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[0;32m 499\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 500\u001b[0m \u001b[38;5;66;03m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[0;32m 501\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [x\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m--> 503\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arrays_to_mgr(arrays, columns, index, dtype\u001b[38;5;241m=\u001b[39mdtype, typ\u001b[38;5;241m=\u001b[39mtyp, consolidate\u001b[38;5;241m=\u001b[39mcopy)\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:114\u001b[0m, in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[0;32m 111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_integrity:\n\u001b[0;32m 112\u001b[0m \u001b[38;5;66;03m# figure out the index, if necessary\u001b[39;00m\n\u001b[0;32m 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 114\u001b[0m index \u001b[38;5;241m=\u001b[39m _extract_index(arrays)\n\u001b[0;32m 115\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 116\u001b[0m index \u001b[38;5;241m=\u001b[39m ensure_index(index)\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:664\u001b[0m, in \u001b[0;36m_extract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m 662\u001b[0m raw_lengths\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mlen\u001b[39m(val))\n\u001b[0;32m 663\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;129;01mand\u001b[39;00m val\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 664\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPer-column arrays must each be 1-dimensional\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 666\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m indexes \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m raw_lengths:\n\u001b[0;32m 667\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf using all scalar values, you must pass an index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[1;31mValueError\u001b[0m: Per-column arrays must each be 1-dimensional" + "15 2024-10-11 77.558740 80.558740 78.04 78.04 2024-10-10\n", + "['2024-10-04', '2024-10-05', '2024-10-06', '2024-10-07', '2024-10-08', '2024-10-09', '2024-10-10', '2024-10-11']\n", + "(10, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "0 2024-10-08 78.172055 81.172055 76.36 76.36 2024-10-07\n", + "1 2024-10-09 78.017734 81.017734 75.15 75.15 2024-10-07\n", + "2 2024-10-10 78.196563 81.196563 76.72 76.72 2024-10-07\n", + "3 2024-10-11 78.244970 81.244970 78.04 78.04 2024-10-07\n", + "5 2024-10-09 75.442758 78.442758 75.15 75.15 2024-10-08\n", + "6 2024-10-10 75.684414 78.684414 76.72 76.72 2024-10-08\n", + "7 2024-10-11 75.675482 78.675482 78.04 78.04 2024-10-08\n", + "10 2024-10-10 75.439643 78.439643 76.72 76.72 2024-10-09\n", + "11 2024-10-11 75.214194 78.214194 78.04 78.04 2024-10-09\n", + "15 2024-10-11 77.558740 80.558740 78.04 78.04 2024-10-10\n", + "['2024-10-04', '2024-10-05', '2024-10-06', '2024-10-07', '2024-10-08', '2024-10-09', '2024-10-10', '2024-10-11']\n", + "(10, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "0 2024-10-08 78.172055 81.172055 76.36 76.36 2024-10-07\n", + "1 2024-10-09 78.017734 81.017734 75.15 75.15 2024-10-07\n", + "2 2024-10-10 78.196563 81.196563 76.72 76.72 2024-10-07\n", + "3 2024-10-11 78.244970 81.244970 78.04 78.04 2024-10-07\n", + "5 2024-10-09 75.442758 78.442758 75.15 75.15 2024-10-08\n", + "6 2024-10-10 75.684414 78.684414 76.72 76.72 2024-10-08\n", + "7 2024-10-11 75.675482 78.675482 78.04 78.04 2024-10-08\n", + "10 2024-10-10 75.439643 78.439643 76.72 76.72 2024-10-09\n", + "11 2024-10-11 75.214194 78.214194 78.04 78.04 2024-10-09\n", + "15 2024-10-11 77.558740 80.558740 78.04 78.04 2024-10-10\n", + "['2024-10-04', '2024-10-05', '2024-10-06', '2024-10-07', '2024-10-08', '2024-10-09', '2024-10-10', '2024-10-11']\n", + "(10, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "0 2024-10-08 78.172055 81.172055 76.36 76.36 2024-10-07\n", + "1 2024-10-09 78.017734 81.017734 75.15 75.15 2024-10-07\n", + "2 2024-10-10 78.196563 81.196563 76.72 76.72 2024-10-07\n", + "3 2024-10-11 78.244970 81.244970 78.04 78.04 2024-10-07\n", + "5 2024-10-09 75.442758 78.442758 75.15 75.15 2024-10-08\n", + "6 2024-10-10 75.684414 78.684414 76.72 76.72 2024-10-08\n", + "7 2024-10-11 75.675482 78.675482 78.04 78.04 2024-10-08\n", + "10 2024-10-10 75.439643 78.439643 76.72 76.72 2024-10-09\n", + "11 2024-10-11 75.214194 78.214194 78.04 78.04 2024-10-09\n", + "15 2024-10-11 77.558740 80.558740 78.04 78.04 2024-10-10\n", + "['2024-10-04', '2024-10-05', '2024-10-06', '2024-10-07', '2024-10-08', '2024-10-09', '2024-10-10', '2024-10-11']\n", + "(10, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "0 2024-10-08 78.172055 81.172055 76.36 76.36 2024-10-07\n", + "1 2024-10-09 78.017734 81.017734 75.15 75.15 2024-10-07\n", + "2 2024-10-10 78.196563 81.196563 76.72 76.72 2024-10-07\n", + "3 2024-10-11 78.244970 81.244970 78.04 78.04 2024-10-07\n", + "5 2024-10-09 75.442758 78.442758 75.15 75.15 2024-10-08\n", + "6 2024-10-10 75.684414 78.684414 76.72 76.72 2024-10-08\n", + "7 2024-10-11 75.675482 78.675482 78.04 78.04 2024-10-08\n", + "10 2024-10-10 75.439643 78.439643 76.72 76.72 2024-10-09\n", + "11 2024-10-11 75.214194 78.214194 78.04 78.04 2024-10-09\n", + "15 2024-10-11 77.558740 80.558740 78.04 78.04 2024-10-10\n", + "['2024-10-11', '2024-10-12', '2024-10-13', '2024-10-14', '2024-10-15', '2024-10-16', '2024-10-17', '2024-10-18']\n", + "(29, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "3 2024-10-11 78.244970 81.244970 78.04 78.04 2024-10-07\n", + "4 2024-10-14 78.448240 81.448240 74.86 74.86 2024-10-07\n", + "7 2024-10-11 75.675482 78.675482 78.04 78.04 2024-10-08\n", + "8 2024-10-14 75.819379 78.819379 74.86 74.86 2024-10-08\n", + "9 2024-10-15 75.830953 78.830953 73.34 73.34 2024-10-08\n", + "11 2024-10-11 75.214194 78.214194 78.04 78.04 2024-10-09\n", + "12 2024-10-14 75.775363 78.775363 74.86 74.86 2024-10-09\n", + "13 2024-10-15 75.869936 78.869936 73.34 73.34 2024-10-09\n", + "14 2024-10-16 76.203067 79.203067 73.42 73.42 2024-10-09\n", + "15 2024-10-11 77.558740 80.558740 78.04 78.04 2024-10-10\n", + "16 2024-10-14 77.326639 80.326639 74.86 74.86 2024-10-10\n", + "17 2024-10-15 77.711800 80.711800 73.34 73.34 2024-10-10\n", + "18 2024-10-16 78.129062 81.129062 73.42 73.42 2024-10-10\n", + "19 2024-10-17 78.237173 81.237173 73.26 73.26 2024-10-10\n", + "20 2024-10-14 77.103294 80.103294 74.86 74.86 2024-10-11\n", + "21 2024-10-15 77.561999 80.561999 73.34 73.34 2024-10-11\n", + "22 2024-10-16 77.827442 80.827442 73.42 73.42 2024-10-11\n", + "23 2024-10-17 77.831231 80.831231 73.26 73.26 2024-10-11\n", + "24 2024-10-18 78.024951 81.024951 72.50 72.50 2024-10-11\n", + "25 2024-10-15 76.526668 79.526668 73.34 73.34 2024-10-14\n", + "['2024-10-11', '2024-10-12', '2024-10-13', '2024-10-14', '2024-10-15', '2024-10-16', '2024-10-17', '2024-10-18']\n", + "(29, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "3 2024-10-11 78.244970 81.244970 78.04 78.04 2024-10-07\n", + "4 2024-10-14 78.448240 81.448240 74.86 74.86 2024-10-07\n", + "7 2024-10-11 75.675482 78.675482 78.04 78.04 2024-10-08\n", + "8 2024-10-14 75.819379 78.819379 74.86 74.86 2024-10-08\n", + "9 2024-10-15 75.830953 78.830953 73.34 73.34 2024-10-08\n", + "11 2024-10-11 75.214194 78.214194 78.04 78.04 2024-10-09\n", + "12 2024-10-14 75.775363 78.775363 74.86 74.86 2024-10-09\n", + "13 2024-10-15 75.869936 78.869936 73.34 73.34 2024-10-09\n", + "14 2024-10-16 76.203067 79.203067 73.42 73.42 2024-10-09\n", + "15 2024-10-11 77.558740 80.558740 78.04 78.04 2024-10-10\n", + "16 2024-10-14 77.326639 80.326639 74.86 74.86 2024-10-10\n", + "17 2024-10-15 77.711800 80.711800 73.34 73.34 2024-10-10\n", + "18 2024-10-16 78.129062 81.129062 73.42 73.42 2024-10-10\n", + "19 2024-10-17 78.237173 81.237173 73.26 73.26 2024-10-10\n", + "20 2024-10-14 77.103294 80.103294 74.86 74.86 2024-10-11\n", + "21 2024-10-15 77.561999 80.561999 73.34 73.34 2024-10-11\n", + "22 2024-10-16 77.827442 80.827442 73.42 73.42 2024-10-11\n", + "23 2024-10-17 77.831231 80.831231 73.26 73.26 2024-10-11\n", + "24 2024-10-18 78.024951 81.024951 72.50 72.50 2024-10-11\n", + "25 2024-10-15 76.526668 79.526668 73.34 73.34 2024-10-14\n", + "['2024-10-11', '2024-10-12', '2024-10-13', '2024-10-14', '2024-10-15', '2024-10-16', '2024-10-17', '2024-10-18']\n", + "(29, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "3 2024-10-11 78.244970 81.244970 78.04 78.04 2024-10-07\n", + "4 2024-10-14 78.448240 81.448240 74.86 74.86 2024-10-07\n", + "7 2024-10-11 75.675482 78.675482 78.04 78.04 2024-10-08\n", + "8 2024-10-14 75.819379 78.819379 74.86 74.86 2024-10-08\n", + "9 2024-10-15 75.830953 78.830953 73.34 73.34 2024-10-08\n", + "11 2024-10-11 75.214194 78.214194 78.04 78.04 2024-10-09\n", + "12 2024-10-14 75.775363 78.775363 74.86 74.86 2024-10-09\n", + "13 2024-10-15 75.869936 78.869936 73.34 73.34 2024-10-09\n", + "14 2024-10-16 76.203067 79.203067 73.42 73.42 2024-10-09\n", + "15 2024-10-11 77.558740 80.558740 78.04 78.04 2024-10-10\n", + "16 2024-10-14 77.326639 80.326639 74.86 74.86 2024-10-10\n", + "17 2024-10-15 77.711800 80.711800 73.34 73.34 2024-10-10\n", + "18 2024-10-16 78.129062 81.129062 73.42 73.42 2024-10-10\n", + "19 2024-10-17 78.237173 81.237173 73.26 73.26 2024-10-10\n", + "20 2024-10-14 77.103294 80.103294 74.86 74.86 2024-10-11\n", + "21 2024-10-15 77.561999 80.561999 73.34 73.34 2024-10-11\n", + "22 2024-10-16 77.827442 80.827442 73.42 73.42 2024-10-11\n", + "23 2024-10-17 77.831231 80.831231 73.26 73.26 2024-10-11\n", + "24 2024-10-18 78.024951 81.024951 72.50 72.50 2024-10-11\n", + "25 2024-10-15 76.526668 79.526668 73.34 73.34 2024-10-14\n", + "['2024-10-11', '2024-10-12', '2024-10-13', '2024-10-14', '2024-10-15', '2024-10-16', '2024-10-17', '2024-10-18']\n", + "(29, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "3 2024-10-11 78.244970 81.244970 78.04 78.04 2024-10-07\n", + "4 2024-10-14 78.448240 81.448240 74.86 74.86 2024-10-07\n", + "7 2024-10-11 75.675482 78.675482 78.04 78.04 2024-10-08\n", + "8 2024-10-14 75.819379 78.819379 74.86 74.86 2024-10-08\n", + "9 2024-10-15 75.830953 78.830953 73.34 73.34 2024-10-08\n", + "11 2024-10-11 75.214194 78.214194 78.04 78.04 2024-10-09\n", + "12 2024-10-14 75.775363 78.775363 74.86 74.86 2024-10-09\n", + "13 2024-10-15 75.869936 78.869936 73.34 73.34 2024-10-09\n", + "14 2024-10-16 76.203067 79.203067 73.42 73.42 2024-10-09\n", + "15 2024-10-11 77.558740 80.558740 78.04 78.04 2024-10-10\n", + "16 2024-10-14 77.326639 80.326639 74.86 74.86 2024-10-10\n", + "17 2024-10-15 77.711800 80.711800 73.34 73.34 2024-10-10\n", + "18 2024-10-16 78.129062 81.129062 73.42 73.42 2024-10-10\n", + "19 2024-10-17 78.237173 81.237173 73.26 73.26 2024-10-10\n", + "20 2024-10-14 77.103294 80.103294 74.86 74.86 2024-10-11\n", + "21 2024-10-15 77.561999 80.561999 73.34 73.34 2024-10-11\n", + "22 2024-10-16 77.827442 80.827442 73.42 73.42 2024-10-11\n", + "23 2024-10-17 77.831231 80.831231 73.26 73.26 2024-10-11\n", + "24 2024-10-18 78.024951 81.024951 72.50 72.50 2024-10-11\n", + "25 2024-10-15 76.526668 79.526668 73.34 73.34 2024-10-14\n", + "['2024-10-11', '2024-10-12', '2024-10-13', '2024-10-14', '2024-10-15', '2024-10-16', '2024-10-17', '2024-10-18']\n", + "(29, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "3 2024-10-11 78.244970 81.244970 78.04 78.04 2024-10-07\n", + "4 2024-10-14 78.448240 81.448240 74.86 74.86 2024-10-07\n", + "7 2024-10-11 75.675482 78.675482 78.04 78.04 2024-10-08\n", + "8 2024-10-14 75.819379 78.819379 74.86 74.86 2024-10-08\n", + "9 2024-10-15 75.830953 78.830953 73.34 73.34 2024-10-08\n", + "11 2024-10-11 75.214194 78.214194 78.04 78.04 2024-10-09\n", + "12 2024-10-14 75.775363 78.775363 74.86 74.86 2024-10-09\n", + "13 2024-10-15 75.869936 78.869936 73.34 73.34 2024-10-09\n", + "14 2024-10-16 76.203067 79.203067 73.42 73.42 2024-10-09\n", + "15 2024-10-11 77.558740 80.558740 78.04 78.04 2024-10-10\n", + "16 2024-10-14 77.326639 80.326639 74.86 74.86 2024-10-10\n", + "17 2024-10-15 77.711800 80.711800 73.34 73.34 2024-10-10\n", + "18 2024-10-16 78.129062 81.129062 73.42 73.42 2024-10-10\n", + "19 2024-10-17 78.237173 81.237173 73.26 73.26 2024-10-10\n", + "20 2024-10-14 77.103294 80.103294 74.86 74.86 2024-10-11\n", + "21 2024-10-15 77.561999 80.561999 73.34 73.34 2024-10-11\n", + "22 2024-10-16 77.827442 80.827442 73.42 73.42 2024-10-11\n", + "23 2024-10-17 77.831231 80.831231 73.26 73.26 2024-10-11\n", + "24 2024-10-18 78.024951 81.024951 72.50 72.50 2024-10-11\n", + "25 2024-10-15 76.526668 79.526668 73.34 73.34 2024-10-14\n", + "['2024-10-18', '2024-10-19', '2024-10-20', '2024-10-21', '2024-10-22', '2024-10-23', '2024-10-24', '2024-10-25']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "24 2024-10-18 78.024951 81.024951 72.50 72.50 2024-10-11\n", + "28 2024-10-18 77.135056 80.135056 72.50 72.50 2024-10-14\n", + "29 2024-10-21 77.530344 80.530344 72.80 72.80 2024-10-14\n", + "32 2024-10-18 73.482573 76.482573 72.50 72.50 2024-10-15\n", + "33 2024-10-21 73.144924 76.144924 72.80 72.80 2024-10-15\n", + "34 2024-10-22 73.877058 76.877058 73.57 73.57 2024-10-15\n", + "36 2024-10-18 72.624883 75.624883 72.50 72.50 2024-10-16\n", + "37 2024-10-21 72.775239 75.775239 72.80 72.80 2024-10-16\n", + "38 2024-10-22 72.379754 75.379754 73.57 73.57 2024-10-16\n", + "39 2024-10-23 72.447762 75.447762 74.42 74.42 2024-10-16\n", + "40 2024-10-18 71.980603 74.980603 72.50 72.50 2024-10-17\n", + "41 2024-10-21 71.915474 74.915474 72.80 72.80 2024-10-17\n", + "42 2024-10-22 71.445161 74.445161 73.57 73.57 2024-10-17\n", + "43 2024-10-23 70.993233 73.993233 74.42 74.42 2024-10-17\n", + "44 2024-10-24 71.050194 74.050194 74.00 74.00 2024-10-17\n", + "45 2024-10-21 70.300023 73.300023 72.80 72.80 2024-10-18\n", + "46 2024-10-22 70.269711 73.269711 73.57 73.57 2024-10-18\n", + "47 2024-10-23 69.892138 72.892138 74.42 74.42 2024-10-18\n", + "48 2024-10-24 69.531043 72.531043 74.00 74.00 2024-10-18\n", + "49 2024-10-25 69.305193 72.305193 74.17 74.17 2024-10-18\n", + "['2024-10-18', '2024-10-19', '2024-10-20', '2024-10-21', '2024-10-22', '2024-10-23', '2024-10-24', '2024-10-25']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "24 2024-10-18 78.024951 81.024951 72.50 72.50 2024-10-11\n", + "28 2024-10-18 77.135056 80.135056 72.50 72.50 2024-10-14\n", + "29 2024-10-21 77.530344 80.530344 72.80 72.80 2024-10-14\n", + "32 2024-10-18 73.482573 76.482573 72.50 72.50 2024-10-15\n", + "33 2024-10-21 73.144924 76.144924 72.80 72.80 2024-10-15\n", + "34 2024-10-22 73.877058 76.877058 73.57 73.57 2024-10-15\n", + "36 2024-10-18 72.624883 75.624883 72.50 72.50 2024-10-16\n", + "37 2024-10-21 72.775239 75.775239 72.80 72.80 2024-10-16\n", + "38 2024-10-22 72.379754 75.379754 73.57 73.57 2024-10-16\n", + "39 2024-10-23 72.447762 75.447762 74.42 74.42 2024-10-16\n", + "40 2024-10-18 71.980603 74.980603 72.50 72.50 2024-10-17\n", + "41 2024-10-21 71.915474 74.915474 72.80 72.80 2024-10-17\n", + "42 2024-10-22 71.445161 74.445161 73.57 73.57 2024-10-17\n", + "43 2024-10-23 70.993233 73.993233 74.42 74.42 2024-10-17\n", + "44 2024-10-24 71.050194 74.050194 74.00 74.00 2024-10-17\n", + "45 2024-10-21 70.300023 73.300023 72.80 72.80 2024-10-18\n", + "46 2024-10-22 70.269711 73.269711 73.57 73.57 2024-10-18\n", + "47 2024-10-23 69.892138 72.892138 74.42 74.42 2024-10-18\n", + "48 2024-10-24 69.531043 72.531043 74.00 74.00 2024-10-18\n", + "49 2024-10-25 69.305193 72.305193 74.17 74.17 2024-10-18\n", + "['2024-10-18', '2024-10-19', '2024-10-20', '2024-10-21', '2024-10-22', '2024-10-23', '2024-10-24', '2024-10-25']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "24 2024-10-18 78.024951 81.024951 72.50 72.50 2024-10-11\n", + "28 2024-10-18 77.135056 80.135056 72.50 72.50 2024-10-14\n", + "29 2024-10-21 77.530344 80.530344 72.80 72.80 2024-10-14\n", + "32 2024-10-18 73.482573 76.482573 72.50 72.50 2024-10-15\n", + "33 2024-10-21 73.144924 76.144924 72.80 72.80 2024-10-15\n", + "34 2024-10-22 73.877058 76.877058 73.57 73.57 2024-10-15\n", + "36 2024-10-18 72.624883 75.624883 72.50 72.50 2024-10-16\n", + "37 2024-10-21 72.775239 75.775239 72.80 72.80 2024-10-16\n", + "38 2024-10-22 72.379754 75.379754 73.57 73.57 2024-10-16\n", + "39 2024-10-23 72.447762 75.447762 74.42 74.42 2024-10-16\n", + "40 2024-10-18 71.980603 74.980603 72.50 72.50 2024-10-17\n", + "41 2024-10-21 71.915474 74.915474 72.80 72.80 2024-10-17\n", + "42 2024-10-22 71.445161 74.445161 73.57 73.57 2024-10-17\n", + "43 2024-10-23 70.993233 73.993233 74.42 74.42 2024-10-17\n", + "44 2024-10-24 71.050194 74.050194 74.00 74.00 2024-10-17\n", + "45 2024-10-21 70.300023 73.300023 72.80 72.80 2024-10-18\n", + "46 2024-10-22 70.269711 73.269711 73.57 73.57 2024-10-18\n", + "47 2024-10-23 69.892138 72.892138 74.42 74.42 2024-10-18\n", + "48 2024-10-24 69.531043 72.531043 74.00 74.00 2024-10-18\n", + "49 2024-10-25 69.305193 72.305193 74.17 74.17 2024-10-18\n", + "['2024-10-18', '2024-10-19', '2024-10-20', '2024-10-21', '2024-10-22', '2024-10-23', '2024-10-24', '2024-10-25']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "24 2024-10-18 78.024951 81.024951 72.50 72.50 2024-10-11\n", + "28 2024-10-18 77.135056 80.135056 72.50 72.50 2024-10-14\n", + "29 2024-10-21 77.530344 80.530344 72.80 72.80 2024-10-14\n", + "32 2024-10-18 73.482573 76.482573 72.50 72.50 2024-10-15\n", + "33 2024-10-21 73.144924 76.144924 72.80 72.80 2024-10-15\n", + "34 2024-10-22 73.877058 76.877058 73.57 73.57 2024-10-15\n", + "36 2024-10-18 72.624883 75.624883 72.50 72.50 2024-10-16\n", + "37 2024-10-21 72.775239 75.775239 72.80 72.80 2024-10-16\n", + "38 2024-10-22 72.379754 75.379754 73.57 73.57 2024-10-16\n", + "39 2024-10-23 72.447762 75.447762 74.42 74.42 2024-10-16\n", + "40 2024-10-18 71.980603 74.980603 72.50 72.50 2024-10-17\n", + "41 2024-10-21 71.915474 74.915474 72.80 72.80 2024-10-17\n", + "42 2024-10-22 71.445161 74.445161 73.57 73.57 2024-10-17\n", + "43 2024-10-23 70.993233 73.993233 74.42 74.42 2024-10-17\n", + "44 2024-10-24 71.050194 74.050194 74.00 74.00 2024-10-17\n", + "45 2024-10-21 70.300023 73.300023 72.80 72.80 2024-10-18\n", + "46 2024-10-22 70.269711 73.269711 73.57 73.57 2024-10-18\n", + "47 2024-10-23 69.892138 72.892138 74.42 74.42 2024-10-18\n", + "48 2024-10-24 69.531043 72.531043 74.00 74.00 2024-10-18\n", + "49 2024-10-25 69.305193 72.305193 74.17 74.17 2024-10-18\n", + "['2024-10-18', '2024-10-19', '2024-10-20', '2024-10-21', '2024-10-22', '2024-10-23', '2024-10-24', '2024-10-25']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "24 2024-10-18 78.024951 81.024951 72.50 72.50 2024-10-11\n", + "28 2024-10-18 77.135056 80.135056 72.50 72.50 2024-10-14\n", + "29 2024-10-21 77.530344 80.530344 72.80 72.80 2024-10-14\n", + "32 2024-10-18 73.482573 76.482573 72.50 72.50 2024-10-15\n", + "33 2024-10-21 73.144924 76.144924 72.80 72.80 2024-10-15\n", + "34 2024-10-22 73.877058 76.877058 73.57 73.57 2024-10-15\n", + "36 2024-10-18 72.624883 75.624883 72.50 72.50 2024-10-16\n", + "37 2024-10-21 72.775239 75.775239 72.80 72.80 2024-10-16\n", + "38 2024-10-22 72.379754 75.379754 73.57 73.57 2024-10-16\n", + "39 2024-10-23 72.447762 75.447762 74.42 74.42 2024-10-16\n", + "40 2024-10-18 71.980603 74.980603 72.50 72.50 2024-10-17\n", + "41 2024-10-21 71.915474 74.915474 72.80 72.80 2024-10-17\n", + "42 2024-10-22 71.445161 74.445161 73.57 73.57 2024-10-17\n", + "43 2024-10-23 70.993233 73.993233 74.42 74.42 2024-10-17\n", + "44 2024-10-24 71.050194 74.050194 74.00 74.00 2024-10-17\n", + "45 2024-10-21 70.300023 73.300023 72.80 72.80 2024-10-18\n", + "46 2024-10-22 70.269711 73.269711 73.57 73.57 2024-10-18\n", + "47 2024-10-23 69.892138 72.892138 74.42 74.42 2024-10-18\n", + "48 2024-10-24 69.531043 72.531043 74.00 74.00 2024-10-18\n", + "49 2024-10-25 69.305193 72.305193 74.17 74.17 2024-10-18\n", + "['2024-10-25', '2024-10-26', '2024-10-27', '2024-10-28', '2024-10-29', '2024-10-30', '2024-10-31', '2024-11-01']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "49 2024-10-25 69.305193 72.305193 74.17 74.17 2024-10-18\n", + "53 2024-10-25 71.284130 74.284130 74.17 74.17 2024-10-21\n", + "54 2024-10-28 71.130199 74.130199 70.82 70.82 2024-10-21\n", + "57 2024-10-25 72.849289 75.849289 74.17 74.17 2024-10-22\n", + "58 2024-10-28 72.846420 75.846420 70.82 70.82 2024-10-22\n", + "59 2024-10-29 73.135881 76.135881 70.28 70.28 2024-10-22\n", + "61 2024-10-25 72.576628 75.576628 74.17 74.17 2024-10-23\n", + "62 2024-10-28 72.523148 75.523148 70.82 70.82 2024-10-23\n", + "63 2024-10-29 72.996282 75.996282 70.28 70.28 2024-10-23\n", + "64 2024-10-30 73.612846 76.612846 70.71 70.71 2024-10-23\n", + "65 2024-10-25 72.725321 75.725321 74.17 74.17 2024-10-24\n", + "66 2024-10-28 73.117595 76.117595 70.82 70.82 2024-10-24\n", + "67 2024-10-29 73.842996 76.842996 70.28 70.28 2024-10-24\n", + "68 2024-10-30 73.879968 76.879968 70.71 70.71 2024-10-24\n", + "69 2024-10-31 73.636574 76.636574 71.82 71.82 2024-10-24\n", + "70 2024-10-28 74.114556 77.114556 70.82 70.82 2024-10-25\n", + "71 2024-10-29 74.218034 77.218034 70.28 70.28 2024-10-25\n", + "72 2024-10-30 74.565620 77.565620 70.71 70.71 2024-10-25\n", + "73 2024-10-31 73.995926 76.995926 71.82 71.82 2024-10-25\n", + "74 2024-11-01 73.982739 76.982739 72.89 72.89 2024-10-25\n", + "['2024-10-25', '2024-10-26', '2024-10-27', '2024-10-28', '2024-10-29', '2024-10-30', '2024-10-31', '2024-11-01']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "49 2024-10-25 69.305193 72.305193 74.17 74.17 2024-10-18\n", + "53 2024-10-25 71.284130 74.284130 74.17 74.17 2024-10-21\n", + "54 2024-10-28 71.130199 74.130199 70.82 70.82 2024-10-21\n", + "57 2024-10-25 72.849289 75.849289 74.17 74.17 2024-10-22\n", + "58 2024-10-28 72.846420 75.846420 70.82 70.82 2024-10-22\n", + "59 2024-10-29 73.135881 76.135881 70.28 70.28 2024-10-22\n", + "61 2024-10-25 72.576628 75.576628 74.17 74.17 2024-10-23\n", + "62 2024-10-28 72.523148 75.523148 70.82 70.82 2024-10-23\n", + "63 2024-10-29 72.996282 75.996282 70.28 70.28 2024-10-23\n", + "64 2024-10-30 73.612846 76.612846 70.71 70.71 2024-10-23\n", + "65 2024-10-25 72.725321 75.725321 74.17 74.17 2024-10-24\n", + "66 2024-10-28 73.117595 76.117595 70.82 70.82 2024-10-24\n", + "67 2024-10-29 73.842996 76.842996 70.28 70.28 2024-10-24\n", + "68 2024-10-30 73.879968 76.879968 70.71 70.71 2024-10-24\n", + "69 2024-10-31 73.636574 76.636574 71.82 71.82 2024-10-24\n", + "70 2024-10-28 74.114556 77.114556 70.82 70.82 2024-10-25\n", + "71 2024-10-29 74.218034 77.218034 70.28 70.28 2024-10-25\n", + "72 2024-10-30 74.565620 77.565620 70.71 70.71 2024-10-25\n", + "73 2024-10-31 73.995926 76.995926 71.82 71.82 2024-10-25\n", + "74 2024-11-01 73.982739 76.982739 72.89 72.89 2024-10-25\n", + "['2024-10-25', '2024-10-26', '2024-10-27', '2024-10-28', '2024-10-29', '2024-10-30', '2024-10-31', '2024-11-01']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "49 2024-10-25 69.305193 72.305193 74.17 74.17 2024-10-18\n", + "53 2024-10-25 71.284130 74.284130 74.17 74.17 2024-10-21\n", + "54 2024-10-28 71.130199 74.130199 70.82 70.82 2024-10-21\n", + "57 2024-10-25 72.849289 75.849289 74.17 74.17 2024-10-22\n", + "58 2024-10-28 72.846420 75.846420 70.82 70.82 2024-10-22\n", + "59 2024-10-29 73.135881 76.135881 70.28 70.28 2024-10-22\n", + "61 2024-10-25 72.576628 75.576628 74.17 74.17 2024-10-23\n", + "62 2024-10-28 72.523148 75.523148 70.82 70.82 2024-10-23\n", + "63 2024-10-29 72.996282 75.996282 70.28 70.28 2024-10-23\n", + "64 2024-10-30 73.612846 76.612846 70.71 70.71 2024-10-23\n", + "65 2024-10-25 72.725321 75.725321 74.17 74.17 2024-10-24\n", + "66 2024-10-28 73.117595 76.117595 70.82 70.82 2024-10-24\n", + "67 2024-10-29 73.842996 76.842996 70.28 70.28 2024-10-24\n", + "68 2024-10-30 73.879968 76.879968 70.71 70.71 2024-10-24\n", + "69 2024-10-31 73.636574 76.636574 71.82 71.82 2024-10-24\n", + "70 2024-10-28 74.114556 77.114556 70.82 70.82 2024-10-25\n", + "71 2024-10-29 74.218034 77.218034 70.28 70.28 2024-10-25\n", + "72 2024-10-30 74.565620 77.565620 70.71 70.71 2024-10-25\n", + "73 2024-10-31 73.995926 76.995926 71.82 71.82 2024-10-25\n", + "74 2024-11-01 73.982739 76.982739 72.89 72.89 2024-10-25\n", + "['2024-10-25', '2024-10-26', '2024-10-27', '2024-10-28', '2024-10-29', '2024-10-30', '2024-10-31', '2024-11-01']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "49 2024-10-25 69.305193 72.305193 74.17 74.17 2024-10-18\n", + "53 2024-10-25 71.284130 74.284130 74.17 74.17 2024-10-21\n", + "54 2024-10-28 71.130199 74.130199 70.82 70.82 2024-10-21\n", + "57 2024-10-25 72.849289 75.849289 74.17 74.17 2024-10-22\n", + "58 2024-10-28 72.846420 75.846420 70.82 70.82 2024-10-22\n", + "59 2024-10-29 73.135881 76.135881 70.28 70.28 2024-10-22\n", + "61 2024-10-25 72.576628 75.576628 74.17 74.17 2024-10-23\n", + "62 2024-10-28 72.523148 75.523148 70.82 70.82 2024-10-23\n", + "63 2024-10-29 72.996282 75.996282 70.28 70.28 2024-10-23\n", + "64 2024-10-30 73.612846 76.612846 70.71 70.71 2024-10-23\n", + "65 2024-10-25 72.725321 75.725321 74.17 74.17 2024-10-24\n", + "66 2024-10-28 73.117595 76.117595 70.82 70.82 2024-10-24\n", + "67 2024-10-29 73.842996 76.842996 70.28 70.28 2024-10-24\n", + "68 2024-10-30 73.879968 76.879968 70.71 70.71 2024-10-24\n", + "69 2024-10-31 73.636574 76.636574 71.82 71.82 2024-10-24\n", + "70 2024-10-28 74.114556 77.114556 70.82 70.82 2024-10-25\n", + "71 2024-10-29 74.218034 77.218034 70.28 70.28 2024-10-25\n", + "72 2024-10-30 74.565620 77.565620 70.71 70.71 2024-10-25\n", + "73 2024-10-31 73.995926 76.995926 71.82 71.82 2024-10-25\n", + "74 2024-11-01 73.982739 76.982739 72.89 72.89 2024-10-25\n", + "['2024-10-25', '2024-10-26', '2024-10-27', '2024-10-28', '2024-10-29', '2024-10-30', '2024-10-31', '2024-11-01']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "49 2024-10-25 69.305193 72.305193 74.17 74.17 2024-10-18\n", + "53 2024-10-25 71.284130 74.284130 74.17 74.17 2024-10-21\n", + "54 2024-10-28 71.130199 74.130199 70.82 70.82 2024-10-21\n", + "57 2024-10-25 72.849289 75.849289 74.17 74.17 2024-10-22\n", + "58 2024-10-28 72.846420 75.846420 70.82 70.82 2024-10-22\n", + "59 2024-10-29 73.135881 76.135881 70.28 70.28 2024-10-22\n", + "61 2024-10-25 72.576628 75.576628 74.17 74.17 2024-10-23\n", + "62 2024-10-28 72.523148 75.523148 70.82 70.82 2024-10-23\n", + "63 2024-10-29 72.996282 75.996282 70.28 70.28 2024-10-23\n", + "64 2024-10-30 73.612846 76.612846 70.71 70.71 2024-10-23\n", + "65 2024-10-25 72.725321 75.725321 74.17 74.17 2024-10-24\n", + "66 2024-10-28 73.117595 76.117595 70.82 70.82 2024-10-24\n", + "67 2024-10-29 73.842996 76.842996 70.28 70.28 2024-10-24\n", + "68 2024-10-30 73.879968 76.879968 70.71 70.71 2024-10-24\n", + "69 2024-10-31 73.636574 76.636574 71.82 71.82 2024-10-24\n", + "70 2024-10-28 74.114556 77.114556 70.82 70.82 2024-10-25\n", + "71 2024-10-29 74.218034 77.218034 70.28 70.28 2024-10-25\n", + "72 2024-10-30 74.565620 77.565620 70.71 70.71 2024-10-25\n", + "73 2024-10-31 73.995926 76.995926 71.82 71.82 2024-10-25\n", + "74 2024-11-01 73.982739 76.982739 72.89 72.89 2024-10-25\n", + "['2024-11-01', '2024-11-02', '2024-11-03', '2024-11-04', '2024-11-05', '2024-11-06', '2024-11-07', '2024-11-08']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "74 2024-11-01 73.982739 76.982739 72.89 72.89 2024-10-25\n", + "78 2024-11-01 71.585668 74.585668 72.89 72.89 2024-10-28\n", + "79 2024-11-04 72.056366 75.056366 73.88 73.88 2024-10-28\n", + "82 2024-11-01 71.468451 74.468451 72.89 72.89 2024-10-29\n", + "83 2024-11-04 71.871874 74.871874 73.88 73.88 2024-10-29\n", + "84 2024-11-05 72.078893 75.078893 73.52 73.52 2024-10-29\n", + "86 2024-11-01 72.094280 75.094280 72.89 72.89 2024-10-30\n", + "87 2024-11-04 72.038187 75.038187 73.88 73.88 2024-10-30\n", + "88 2024-11-05 72.291321 75.291321 73.52 73.52 2024-10-30\n", + "89 2024-11-06 72.141546 75.141546 73.34 73.34 2024-10-30\n", + "90 2024-11-01 71.938575 74.938575 72.89 72.89 2024-10-31\n", + "91 2024-11-04 72.103529 75.103529 73.88 73.88 2024-10-31\n", + "92 2024-11-05 72.012638 75.012638 73.52 73.52 2024-10-31\n", + "93 2024-11-06 71.676658 74.676658 73.34 73.34 2024-10-31\n", + "94 2024-11-07 72.012166 75.012166 74.02 74.02 2024-10-31\n", + "95 2024-11-04 71.952961 74.952961 73.88 73.88 2024-11-01\n", + "96 2024-11-05 72.000069 75.000069 73.52 73.52 2024-11-01\n", + "97 2024-11-06 71.373926 74.373926 73.34 73.34 2024-11-01\n", + "98 2024-11-07 71.456712 74.456712 74.02 74.02 2024-11-01\n", + "99 2024-11-08 71.590162 74.590162 73.13 73.13 2024-11-01\n", + "['2024-11-01', '2024-11-02', '2024-11-03', '2024-11-04', '2024-11-05', '2024-11-06', '2024-11-07', '2024-11-08']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "74 2024-11-01 73.982739 76.982739 72.89 72.89 2024-10-25\n", + "78 2024-11-01 71.585668 74.585668 72.89 72.89 2024-10-28\n", + "79 2024-11-04 72.056366 75.056366 73.88 73.88 2024-10-28\n", + "82 2024-11-01 71.468451 74.468451 72.89 72.89 2024-10-29\n", + "83 2024-11-04 71.871874 74.871874 73.88 73.88 2024-10-29\n", + "84 2024-11-05 72.078893 75.078893 73.52 73.52 2024-10-29\n", + "86 2024-11-01 72.094280 75.094280 72.89 72.89 2024-10-30\n", + "87 2024-11-04 72.038187 75.038187 73.88 73.88 2024-10-30\n", + "88 2024-11-05 72.291321 75.291321 73.52 73.52 2024-10-30\n", + "89 2024-11-06 72.141546 75.141546 73.34 73.34 2024-10-30\n", + "90 2024-11-01 71.938575 74.938575 72.89 72.89 2024-10-31\n", + "91 2024-11-04 72.103529 75.103529 73.88 73.88 2024-10-31\n", + "92 2024-11-05 72.012638 75.012638 73.52 73.52 2024-10-31\n", + "93 2024-11-06 71.676658 74.676658 73.34 73.34 2024-10-31\n", + "94 2024-11-07 72.012166 75.012166 74.02 74.02 2024-10-31\n", + "95 2024-11-04 71.952961 74.952961 73.88 73.88 2024-11-01\n", + "96 2024-11-05 72.000069 75.000069 73.52 73.52 2024-11-01\n", + "97 2024-11-06 71.373926 74.373926 73.34 73.34 2024-11-01\n", + "98 2024-11-07 71.456712 74.456712 74.02 74.02 2024-11-01\n", + "99 2024-11-08 71.590162 74.590162 73.13 73.13 2024-11-01\n", + "['2024-11-01', '2024-11-02', '2024-11-03', '2024-11-04', '2024-11-05', '2024-11-06', '2024-11-07', '2024-11-08']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "74 2024-11-01 73.982739 76.982739 72.89 72.89 2024-10-25\n", + "78 2024-11-01 71.585668 74.585668 72.89 72.89 2024-10-28\n", + "79 2024-11-04 72.056366 75.056366 73.88 73.88 2024-10-28\n", + "82 2024-11-01 71.468451 74.468451 72.89 72.89 2024-10-29\n", + "83 2024-11-04 71.871874 74.871874 73.88 73.88 2024-10-29\n", + "84 2024-11-05 72.078893 75.078893 73.52 73.52 2024-10-29\n", + "86 2024-11-01 72.094280 75.094280 72.89 72.89 2024-10-30\n", + "87 2024-11-04 72.038187 75.038187 73.88 73.88 2024-10-30\n", + "88 2024-11-05 72.291321 75.291321 73.52 73.52 2024-10-30\n", + "89 2024-11-06 72.141546 75.141546 73.34 73.34 2024-10-30\n", + "90 2024-11-01 71.938575 74.938575 72.89 72.89 2024-10-31\n", + "91 2024-11-04 72.103529 75.103529 73.88 73.88 2024-10-31\n", + "92 2024-11-05 72.012638 75.012638 73.52 73.52 2024-10-31\n", + "93 2024-11-06 71.676658 74.676658 73.34 73.34 2024-10-31\n", + "94 2024-11-07 72.012166 75.012166 74.02 74.02 2024-10-31\n", + "95 2024-11-04 71.952961 74.952961 73.88 73.88 2024-11-01\n", + "96 2024-11-05 72.000069 75.000069 73.52 73.52 2024-11-01\n", + "97 2024-11-06 71.373926 74.373926 73.34 73.34 2024-11-01\n", + "98 2024-11-07 71.456712 74.456712 74.02 74.02 2024-11-01\n", + "99 2024-11-08 71.590162 74.590162 73.13 73.13 2024-11-01\n", + "['2024-11-01', '2024-11-02', '2024-11-03', '2024-11-04', '2024-11-05', '2024-11-06', '2024-11-07', '2024-11-08']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "74 2024-11-01 73.982739 76.982739 72.89 72.89 2024-10-25\n", + "78 2024-11-01 71.585668 74.585668 72.89 72.89 2024-10-28\n", + "79 2024-11-04 72.056366 75.056366 73.88 73.88 2024-10-28\n", + "82 2024-11-01 71.468451 74.468451 72.89 72.89 2024-10-29\n", + "83 2024-11-04 71.871874 74.871874 73.88 73.88 2024-10-29\n", + "84 2024-11-05 72.078893 75.078893 73.52 73.52 2024-10-29\n", + "86 2024-11-01 72.094280 75.094280 72.89 72.89 2024-10-30\n", + "87 2024-11-04 72.038187 75.038187 73.88 73.88 2024-10-30\n", + "88 2024-11-05 72.291321 75.291321 73.52 73.52 2024-10-30\n", + "89 2024-11-06 72.141546 75.141546 73.34 73.34 2024-10-30\n", + "90 2024-11-01 71.938575 74.938575 72.89 72.89 2024-10-31\n", + "91 2024-11-04 72.103529 75.103529 73.88 73.88 2024-10-31\n", + "92 2024-11-05 72.012638 75.012638 73.52 73.52 2024-10-31\n", + "93 2024-11-06 71.676658 74.676658 73.34 73.34 2024-10-31\n", + "94 2024-11-07 72.012166 75.012166 74.02 74.02 2024-10-31\n", + "95 2024-11-04 71.952961 74.952961 73.88 73.88 2024-11-01\n", + "96 2024-11-05 72.000069 75.000069 73.52 73.52 2024-11-01\n", + "97 2024-11-06 71.373926 74.373926 73.34 73.34 2024-11-01\n", + "98 2024-11-07 71.456712 74.456712 74.02 74.02 2024-11-01\n", + "99 2024-11-08 71.590162 74.590162 73.13 73.13 2024-11-01\n", + "['2024-11-01', '2024-11-02', '2024-11-03', '2024-11-04', '2024-11-05', '2024-11-06', '2024-11-07', '2024-11-08']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "74 2024-11-01 73.982739 76.982739 72.89 72.89 2024-10-25\n", + "78 2024-11-01 71.585668 74.585668 72.89 72.89 2024-10-28\n", + "79 2024-11-04 72.056366 75.056366 73.88 73.88 2024-10-28\n", + "82 2024-11-01 71.468451 74.468451 72.89 72.89 2024-10-29\n", + "83 2024-11-04 71.871874 74.871874 73.88 73.88 2024-10-29\n", + "84 2024-11-05 72.078893 75.078893 73.52 73.52 2024-10-29\n", + "86 2024-11-01 72.094280 75.094280 72.89 72.89 2024-10-30\n", + "87 2024-11-04 72.038187 75.038187 73.88 73.88 2024-10-30\n", + "88 2024-11-05 72.291321 75.291321 73.52 73.52 2024-10-30\n", + "89 2024-11-06 72.141546 75.141546 73.34 73.34 2024-10-30\n", + "90 2024-11-01 71.938575 74.938575 72.89 72.89 2024-10-31\n", + "91 2024-11-04 72.103529 75.103529 73.88 73.88 2024-10-31\n", + "92 2024-11-05 72.012638 75.012638 73.52 73.52 2024-10-31\n", + "93 2024-11-06 71.676658 74.676658 73.34 73.34 2024-10-31\n", + "94 2024-11-07 72.012166 75.012166 74.02 74.02 2024-10-31\n", + "95 2024-11-04 71.952961 74.952961 73.88 73.88 2024-11-01\n", + "96 2024-11-05 72.000069 75.000069 73.52 73.52 2024-11-01\n", + "97 2024-11-06 71.373926 74.373926 73.34 73.34 2024-11-01\n", + "98 2024-11-07 71.456712 74.456712 74.02 74.02 2024-11-01\n", + "99 2024-11-08 71.590162 74.590162 73.13 73.13 2024-11-01\n", + "['2024-11-08', '2024-11-09', '2024-11-10', '2024-11-11', '2024-11-12', '2024-11-13', '2024-11-14', '2024-11-15']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "99 2024-11-08 71.590162 74.590162 73.13 73.13 2024-11-01\n", + "103 2024-11-08 74.519317 77.519317 73.13 73.13 2024-11-04\n", + "104 2024-11-11 74.569906 77.569906 71.57 71.57 2024-11-04\n", + "107 2024-11-08 74.694136 77.694136 73.13 73.13 2024-11-05\n", + "108 2024-11-11 74.762773 77.762773 71.57 71.57 2024-11-05\n", + "109 2024-11-12 74.566135 77.566135 71.55 71.55 2024-11-05\n", + "111 2024-11-08 73.830986 76.830986 73.13 73.13 2024-11-06\n", + "112 2024-11-11 73.992468 76.992468 71.57 71.57 2024-11-06\n", + "113 2024-11-12 73.151260 76.151260 71.55 71.55 2024-11-06\n", + "114 2024-11-13 72.774085 75.774085 70.72 70.72 2024-11-06\n", + "115 2024-11-08 73.928146 76.928146 73.13 73.13 2024-11-07\n", + "116 2024-11-11 73.889942 76.889942 71.57 71.57 2024-11-07\n", + "117 2024-11-12 74.026519 77.026519 71.55 71.55 2024-11-07\n", + "118 2024-11-13 73.767944 76.767944 70.72 70.72 2024-11-07\n", + "119 2024-11-14 73.642370 76.642370 71.79 71.79 2024-11-07\n", + "120 2024-11-11 72.042299 75.042299 71.57 71.57 2024-11-08\n", + "121 2024-11-12 72.238258 75.238258 71.55 71.55 2024-11-08\n", + "122 2024-11-13 71.867976 74.867976 70.72 70.72 2024-11-08\n", + "123 2024-11-14 71.615019 74.615019 71.79 71.79 2024-11-08\n", + "124 2024-11-15 71.822586 74.822586 70.83 70.83 2024-11-08\n", + "['2024-11-08', '2024-11-09', '2024-11-10', '2024-11-11', '2024-11-12', '2024-11-13', '2024-11-14', '2024-11-15']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "99 2024-11-08 71.590162 74.590162 73.13 73.13 2024-11-01\n", + "103 2024-11-08 74.519317 77.519317 73.13 73.13 2024-11-04\n", + "104 2024-11-11 74.569906 77.569906 71.57 71.57 2024-11-04\n", + "107 2024-11-08 74.694136 77.694136 73.13 73.13 2024-11-05\n", + "108 2024-11-11 74.762773 77.762773 71.57 71.57 2024-11-05\n", + "109 2024-11-12 74.566135 77.566135 71.55 71.55 2024-11-05\n", + "111 2024-11-08 73.830986 76.830986 73.13 73.13 2024-11-06\n", + "112 2024-11-11 73.992468 76.992468 71.57 71.57 2024-11-06\n", + "113 2024-11-12 73.151260 76.151260 71.55 71.55 2024-11-06\n", + "114 2024-11-13 72.774085 75.774085 70.72 70.72 2024-11-06\n", + "115 2024-11-08 73.928146 76.928146 73.13 73.13 2024-11-07\n", + "116 2024-11-11 73.889942 76.889942 71.57 71.57 2024-11-07\n", + "117 2024-11-12 74.026519 77.026519 71.55 71.55 2024-11-07\n", + "118 2024-11-13 73.767944 76.767944 70.72 70.72 2024-11-07\n", + "119 2024-11-14 73.642370 76.642370 71.79 71.79 2024-11-07\n", + "120 2024-11-11 72.042299 75.042299 71.57 71.57 2024-11-08\n", + "121 2024-11-12 72.238258 75.238258 71.55 71.55 2024-11-08\n", + "122 2024-11-13 71.867976 74.867976 70.72 70.72 2024-11-08\n", + "123 2024-11-14 71.615019 74.615019 71.79 71.79 2024-11-08\n", + "124 2024-11-15 71.822586 74.822586 70.83 70.83 2024-11-08\n", + "['2024-11-08', '2024-11-09', '2024-11-10', '2024-11-11', '2024-11-12', '2024-11-13', '2024-11-14', '2024-11-15']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "99 2024-11-08 71.590162 74.590162 73.13 73.13 2024-11-01\n", + "103 2024-11-08 74.519317 77.519317 73.13 73.13 2024-11-04\n", + "104 2024-11-11 74.569906 77.569906 71.57 71.57 2024-11-04\n", + "107 2024-11-08 74.694136 77.694136 73.13 73.13 2024-11-05\n", + "108 2024-11-11 74.762773 77.762773 71.57 71.57 2024-11-05\n", + "109 2024-11-12 74.566135 77.566135 71.55 71.55 2024-11-05\n", + "111 2024-11-08 73.830986 76.830986 73.13 73.13 2024-11-06\n", + "112 2024-11-11 73.992468 76.992468 71.57 71.57 2024-11-06\n", + "113 2024-11-12 73.151260 76.151260 71.55 71.55 2024-11-06\n", + "114 2024-11-13 72.774085 75.774085 70.72 70.72 2024-11-06\n", + "115 2024-11-08 73.928146 76.928146 73.13 73.13 2024-11-07\n", + "116 2024-11-11 73.889942 76.889942 71.57 71.57 2024-11-07\n", + "117 2024-11-12 74.026519 77.026519 71.55 71.55 2024-11-07\n", + "118 2024-11-13 73.767944 76.767944 70.72 70.72 2024-11-07\n", + "119 2024-11-14 73.642370 76.642370 71.79 71.79 2024-11-07\n", + "120 2024-11-11 72.042299 75.042299 71.57 71.57 2024-11-08\n", + "121 2024-11-12 72.238258 75.238258 71.55 71.55 2024-11-08\n", + "122 2024-11-13 71.867976 74.867976 70.72 70.72 2024-11-08\n", + "123 2024-11-14 71.615019 74.615019 71.79 71.79 2024-11-08\n", + "124 2024-11-15 71.822586 74.822586 70.83 70.83 2024-11-08\n", + "['2024-11-08', '2024-11-09', '2024-11-10', '2024-11-11', '2024-11-12', '2024-11-13', '2024-11-14', '2024-11-15']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "99 2024-11-08 71.590162 74.590162 73.13 73.13 2024-11-01\n", + "103 2024-11-08 74.519317 77.519317 73.13 73.13 2024-11-04\n", + "104 2024-11-11 74.569906 77.569906 71.57 71.57 2024-11-04\n", + "107 2024-11-08 74.694136 77.694136 73.13 73.13 2024-11-05\n", + "108 2024-11-11 74.762773 77.762773 71.57 71.57 2024-11-05\n", + "109 2024-11-12 74.566135 77.566135 71.55 71.55 2024-11-05\n", + "111 2024-11-08 73.830986 76.830986 73.13 73.13 2024-11-06\n", + "112 2024-11-11 73.992468 76.992468 71.57 71.57 2024-11-06\n", + "113 2024-11-12 73.151260 76.151260 71.55 71.55 2024-11-06\n", + "114 2024-11-13 72.774085 75.774085 70.72 70.72 2024-11-06\n", + "115 2024-11-08 73.928146 76.928146 73.13 73.13 2024-11-07\n", + "116 2024-11-11 73.889942 76.889942 71.57 71.57 2024-11-07\n", + "117 2024-11-12 74.026519 77.026519 71.55 71.55 2024-11-07\n", + "118 2024-11-13 73.767944 76.767944 70.72 70.72 2024-11-07\n", + "119 2024-11-14 73.642370 76.642370 71.79 71.79 2024-11-07\n", + "120 2024-11-11 72.042299 75.042299 71.57 71.57 2024-11-08\n", + "121 2024-11-12 72.238258 75.238258 71.55 71.55 2024-11-08\n", + "122 2024-11-13 71.867976 74.867976 70.72 70.72 2024-11-08\n", + "123 2024-11-14 71.615019 74.615019 71.79 71.79 2024-11-08\n", + "124 2024-11-15 71.822586 74.822586 70.83 70.83 2024-11-08\n", + "['2024-11-08', '2024-11-09', '2024-11-10', '2024-11-11', '2024-11-12', '2024-11-13', '2024-11-14', '2024-11-15']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "99 2024-11-08 71.590162 74.590162 73.13 73.13 2024-11-01\n", + "103 2024-11-08 74.519317 77.519317 73.13 73.13 2024-11-04\n", + "104 2024-11-11 74.569906 77.569906 71.57 71.57 2024-11-04\n", + "107 2024-11-08 74.694136 77.694136 73.13 73.13 2024-11-05\n", + "108 2024-11-11 74.762773 77.762773 71.57 71.57 2024-11-05\n", + "109 2024-11-12 74.566135 77.566135 71.55 71.55 2024-11-05\n", + "111 2024-11-08 73.830986 76.830986 73.13 73.13 2024-11-06\n", + "112 2024-11-11 73.992468 76.992468 71.57 71.57 2024-11-06\n", + "113 2024-11-12 73.151260 76.151260 71.55 71.55 2024-11-06\n", + "114 2024-11-13 72.774085 75.774085 70.72 70.72 2024-11-06\n", + "115 2024-11-08 73.928146 76.928146 73.13 73.13 2024-11-07\n", + "116 2024-11-11 73.889942 76.889942 71.57 71.57 2024-11-07\n", + "117 2024-11-12 74.026519 77.026519 71.55 71.55 2024-11-07\n", + "118 2024-11-13 73.767944 76.767944 70.72 70.72 2024-11-07\n", + "119 2024-11-14 73.642370 76.642370 71.79 71.79 2024-11-07\n", + "120 2024-11-11 72.042299 75.042299 71.57 71.57 2024-11-08\n", + "121 2024-11-12 72.238258 75.238258 71.55 71.55 2024-11-08\n", + "122 2024-11-13 71.867976 74.867976 70.72 70.72 2024-11-08\n", + "123 2024-11-14 71.615019 74.615019 71.79 71.79 2024-11-08\n", + "124 2024-11-15 71.822586 74.822586 70.83 70.83 2024-11-08\n", + "['2024-11-15', '2024-11-16', '2024-11-17', '2024-11-18', '2024-11-19', '2024-11-20', '2024-11-21', '2024-11-22']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "124 2024-11-15 71.822586 74.822586 70.83 70.83 2024-11-08\n", + "128 2024-11-15 71.255264 74.255264 70.83 70.83 2024-11-11\n", + "129 2024-11-18 71.421794 74.421794 70.70 70.70 2024-11-11\n", + "132 2024-11-15 70.607036 73.607036 70.83 70.83 2024-11-12\n", + "133 2024-11-18 70.520069 73.520069 70.70 70.70 2024-11-12\n", + "134 2024-11-19 70.747882 73.747882 72.61 72.61 2024-11-12\n", + "136 2024-11-15 71.227759 74.227759 70.83 70.83 2024-11-13\n", + "137 2024-11-18 71.786118 74.786118 70.70 70.70 2024-11-13\n", + "138 2024-11-19 71.775379 74.775379 72.61 72.61 2024-11-13\n", + "139 2024-11-20 72.188313 75.188313 72.70 72.70 2024-11-13\n", + "140 2024-11-15 70.857173 73.857173 70.83 70.83 2024-11-14\n", + "141 2024-11-18 70.920734 73.920734 70.70 70.70 2024-11-14\n", + "142 2024-11-19 71.358757 74.358757 72.61 72.61 2024-11-14\n", + "143 2024-11-20 71.586167 74.586167 72.70 72.70 2024-11-14\n", + "144 2024-11-21 71.787274 74.787274 71.79 71.79 2024-11-14\n", + "145 2024-11-18 69.781442 72.781442 70.70 70.70 2024-11-15\n", + "146 2024-11-19 70.118789 73.118789 72.61 72.61 2024-11-15\n", + "147 2024-11-20 70.319890 73.319890 72.70 72.70 2024-11-15\n", + "148 2024-11-21 70.470192 73.470192 71.79 71.79 2024-11-15\n", + "149 2024-11-22 70.457320 73.457320 73.43 73.43 2024-11-15\n", + "['2024-11-15', '2024-11-16', '2024-11-17', '2024-11-18', '2024-11-19', '2024-11-20', '2024-11-21', '2024-11-22']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "124 2024-11-15 71.822586 74.822586 70.83 70.83 2024-11-08\n", + "128 2024-11-15 71.255264 74.255264 70.83 70.83 2024-11-11\n", + "129 2024-11-18 71.421794 74.421794 70.70 70.70 2024-11-11\n", + "132 2024-11-15 70.607036 73.607036 70.83 70.83 2024-11-12\n", + "133 2024-11-18 70.520069 73.520069 70.70 70.70 2024-11-12\n", + "134 2024-11-19 70.747882 73.747882 72.61 72.61 2024-11-12\n", + "136 2024-11-15 71.227759 74.227759 70.83 70.83 2024-11-13\n", + "137 2024-11-18 71.786118 74.786118 70.70 70.70 2024-11-13\n", + "138 2024-11-19 71.775379 74.775379 72.61 72.61 2024-11-13\n", + "139 2024-11-20 72.188313 75.188313 72.70 72.70 2024-11-13\n", + "140 2024-11-15 70.857173 73.857173 70.83 70.83 2024-11-14\n", + "141 2024-11-18 70.920734 73.920734 70.70 70.70 2024-11-14\n", + "142 2024-11-19 71.358757 74.358757 72.61 72.61 2024-11-14\n", + "143 2024-11-20 71.586167 74.586167 72.70 72.70 2024-11-14\n", + "144 2024-11-21 71.787274 74.787274 71.79 71.79 2024-11-14\n", + "145 2024-11-18 69.781442 72.781442 70.70 70.70 2024-11-15\n", + "146 2024-11-19 70.118789 73.118789 72.61 72.61 2024-11-15\n", + "147 2024-11-20 70.319890 73.319890 72.70 72.70 2024-11-15\n", + "148 2024-11-21 70.470192 73.470192 71.79 71.79 2024-11-15\n", + "149 2024-11-22 70.457320 73.457320 73.43 73.43 2024-11-15\n", + "['2024-11-15', '2024-11-16', '2024-11-17', '2024-11-18', '2024-11-19', '2024-11-20', '2024-11-21', '2024-11-22']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "124 2024-11-15 71.822586 74.822586 70.83 70.83 2024-11-08\n", + "128 2024-11-15 71.255264 74.255264 70.83 70.83 2024-11-11\n", + "129 2024-11-18 71.421794 74.421794 70.70 70.70 2024-11-11\n", + "132 2024-11-15 70.607036 73.607036 70.83 70.83 2024-11-12\n", + "133 2024-11-18 70.520069 73.520069 70.70 70.70 2024-11-12\n", + "134 2024-11-19 70.747882 73.747882 72.61 72.61 2024-11-12\n", + "136 2024-11-15 71.227759 74.227759 70.83 70.83 2024-11-13\n", + "137 2024-11-18 71.786118 74.786118 70.70 70.70 2024-11-13\n", + "138 2024-11-19 71.775379 74.775379 72.61 72.61 2024-11-13\n", + "139 2024-11-20 72.188313 75.188313 72.70 72.70 2024-11-13\n", + "140 2024-11-15 70.857173 73.857173 70.83 70.83 2024-11-14\n", + "141 2024-11-18 70.920734 73.920734 70.70 70.70 2024-11-14\n", + "142 2024-11-19 71.358757 74.358757 72.61 72.61 2024-11-14\n", + "143 2024-11-20 71.586167 74.586167 72.70 72.70 2024-11-14\n", + "144 2024-11-21 71.787274 74.787274 71.79 71.79 2024-11-14\n", + "145 2024-11-18 69.781442 72.781442 70.70 70.70 2024-11-15\n", + "146 2024-11-19 70.118789 73.118789 72.61 72.61 2024-11-15\n", + "147 2024-11-20 70.319890 73.319890 72.70 72.70 2024-11-15\n", + "148 2024-11-21 70.470192 73.470192 71.79 71.79 2024-11-15\n", + "149 2024-11-22 70.457320 73.457320 73.43 73.43 2024-11-15\n", + "['2024-11-15', '2024-11-16', '2024-11-17', '2024-11-18', '2024-11-19', '2024-11-20', '2024-11-21', '2024-11-22']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "124 2024-11-15 71.822586 74.822586 70.83 70.83 2024-11-08\n", + "128 2024-11-15 71.255264 74.255264 70.83 70.83 2024-11-11\n", + "129 2024-11-18 71.421794 74.421794 70.70 70.70 2024-11-11\n", + "132 2024-11-15 70.607036 73.607036 70.83 70.83 2024-11-12\n", + "133 2024-11-18 70.520069 73.520069 70.70 70.70 2024-11-12\n", + "134 2024-11-19 70.747882 73.747882 72.61 72.61 2024-11-12\n", + "136 2024-11-15 71.227759 74.227759 70.83 70.83 2024-11-13\n", + "137 2024-11-18 71.786118 74.786118 70.70 70.70 2024-11-13\n", + "138 2024-11-19 71.775379 74.775379 72.61 72.61 2024-11-13\n", + "139 2024-11-20 72.188313 75.188313 72.70 72.70 2024-11-13\n", + "140 2024-11-15 70.857173 73.857173 70.83 70.83 2024-11-14\n", + "141 2024-11-18 70.920734 73.920734 70.70 70.70 2024-11-14\n", + "142 2024-11-19 71.358757 74.358757 72.61 72.61 2024-11-14\n", + "143 2024-11-20 71.586167 74.586167 72.70 72.70 2024-11-14\n", + "144 2024-11-21 71.787274 74.787274 71.79 71.79 2024-11-14\n", + "145 2024-11-18 69.781442 72.781442 70.70 70.70 2024-11-15\n", + "146 2024-11-19 70.118789 73.118789 72.61 72.61 2024-11-15\n", + "147 2024-11-20 70.319890 73.319890 72.70 72.70 2024-11-15\n", + "148 2024-11-21 70.470192 73.470192 71.79 71.79 2024-11-15\n", + "149 2024-11-22 70.457320 73.457320 73.43 73.43 2024-11-15\n", + "['2024-11-15', '2024-11-16', '2024-11-17', '2024-11-18', '2024-11-19', '2024-11-20', '2024-11-21', '2024-11-22']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "124 2024-11-15 71.822586 74.822586 70.83 70.83 2024-11-08\n", + "128 2024-11-15 71.255264 74.255264 70.83 70.83 2024-11-11\n", + "129 2024-11-18 71.421794 74.421794 70.70 70.70 2024-11-11\n", + "132 2024-11-15 70.607036 73.607036 70.83 70.83 2024-11-12\n", + "133 2024-11-18 70.520069 73.520069 70.70 70.70 2024-11-12\n", + "134 2024-11-19 70.747882 73.747882 72.61 72.61 2024-11-12\n", + "136 2024-11-15 71.227759 74.227759 70.83 70.83 2024-11-13\n", + "137 2024-11-18 71.786118 74.786118 70.70 70.70 2024-11-13\n", + "138 2024-11-19 71.775379 74.775379 72.61 72.61 2024-11-13\n", + "139 2024-11-20 72.188313 75.188313 72.70 72.70 2024-11-13\n", + "140 2024-11-15 70.857173 73.857173 70.83 70.83 2024-11-14\n", + "141 2024-11-18 70.920734 73.920734 70.70 70.70 2024-11-14\n", + "142 2024-11-19 71.358757 74.358757 72.61 72.61 2024-11-14\n", + "143 2024-11-20 71.586167 74.586167 72.70 72.70 2024-11-14\n", + "144 2024-11-21 71.787274 74.787274 71.79 71.79 2024-11-14\n", + "145 2024-11-18 69.781442 72.781442 70.70 70.70 2024-11-15\n", + "146 2024-11-19 70.118789 73.118789 72.61 72.61 2024-11-15\n", + "147 2024-11-20 70.319890 73.319890 72.70 72.70 2024-11-15\n", + "148 2024-11-21 70.470192 73.470192 71.79 71.79 2024-11-15\n", + "149 2024-11-22 70.457320 73.457320 73.43 73.43 2024-11-15\n", + "['2024-11-22', '2024-11-23', '2024-11-24', '2024-11-25', '2024-11-26', '2024-11-27', '2024-11-28', '2024-11-29']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "149 2024-11-22 70.457320 73.457320 73.43 73.43 2024-11-15\n", + "153 2024-11-22 72.701802 75.701802 73.43 73.43 2024-11-18\n", + "154 2024-11-25 72.861929 75.861929 72.30 72.30 2024-11-18\n", + "157 2024-11-22 72.350601 75.350601 73.43 73.43 2024-11-19\n", + "158 2024-11-25 72.403636 75.403636 72.30 72.30 2024-11-19\n", + "159 2024-11-26 72.408611 75.408611 71.63 71.63 2024-11-19\n", + "161 2024-11-22 71.625726 74.625726 73.43 73.43 2024-11-20\n", + "162 2024-11-25 71.746366 74.746366 72.30 72.30 2024-11-20\n", + "163 2024-11-26 71.802204 74.802204 71.63 71.63 2024-11-20\n", + "164 2024-11-27 71.483702 74.483702 71.71 71.71 2024-11-20\n", + "165 2024-11-22 73.542202 76.542202 73.43 73.43 2024-11-21\n", + "166 2024-11-25 73.833649 76.833649 72.30 72.30 2024-11-21\n", + "167 2024-11-26 73.836256 76.836256 71.63 71.63 2024-11-21\n", + "168 2024-11-27 74.010098 77.010098 71.71 71.71 2024-11-21\n", + "169 2024-11-28 74.079505 77.079505 71.85 71.85 2024-11-21\n", + "170 2024-11-25 74.152900 77.152900 72.30 72.30 2024-11-22\n", + "171 2024-11-26 74.268427 77.268427 71.63 71.63 2024-11-22\n", + "172 2024-11-27 74.185521 77.185521 71.71 71.71 2024-11-22\n", + "173 2024-11-28 74.473107 77.473107 71.85 71.85 2024-11-22\n", + "174 2024-11-29 74.723802 77.723802 71.75 71.75 2024-11-22\n", + "['2024-11-22', '2024-11-23', '2024-11-24', '2024-11-25', '2024-11-26', '2024-11-27', '2024-11-28', '2024-11-29']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "149 2024-11-22 70.457320 73.457320 73.43 73.43 2024-11-15\n", + "153 2024-11-22 72.701802 75.701802 73.43 73.43 2024-11-18\n", + "154 2024-11-25 72.861929 75.861929 72.30 72.30 2024-11-18\n", + "157 2024-11-22 72.350601 75.350601 73.43 73.43 2024-11-19\n", + "158 2024-11-25 72.403636 75.403636 72.30 72.30 2024-11-19\n", + "159 2024-11-26 72.408611 75.408611 71.63 71.63 2024-11-19\n", + "161 2024-11-22 71.625726 74.625726 73.43 73.43 2024-11-20\n", + "162 2024-11-25 71.746366 74.746366 72.30 72.30 2024-11-20\n", + "163 2024-11-26 71.802204 74.802204 71.63 71.63 2024-11-20\n", + "164 2024-11-27 71.483702 74.483702 71.71 71.71 2024-11-20\n", + "165 2024-11-22 73.542202 76.542202 73.43 73.43 2024-11-21\n", + "166 2024-11-25 73.833649 76.833649 72.30 72.30 2024-11-21\n", + "167 2024-11-26 73.836256 76.836256 71.63 71.63 2024-11-21\n", + "168 2024-11-27 74.010098 77.010098 71.71 71.71 2024-11-21\n", + "169 2024-11-28 74.079505 77.079505 71.85 71.85 2024-11-21\n", + "170 2024-11-25 74.152900 77.152900 72.30 72.30 2024-11-22\n", + "171 2024-11-26 74.268427 77.268427 71.63 71.63 2024-11-22\n", + "172 2024-11-27 74.185521 77.185521 71.71 71.71 2024-11-22\n", + "173 2024-11-28 74.473107 77.473107 71.85 71.85 2024-11-22\n", + "174 2024-11-29 74.723802 77.723802 71.75 71.75 2024-11-22\n", + "['2024-11-22', '2024-11-23', '2024-11-24', '2024-11-25', '2024-11-26', '2024-11-27', '2024-11-28', '2024-11-29']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "149 2024-11-22 70.457320 73.457320 73.43 73.43 2024-11-15\n", + "153 2024-11-22 72.701802 75.701802 73.43 73.43 2024-11-18\n", + "154 2024-11-25 72.861929 75.861929 72.30 72.30 2024-11-18\n", + "157 2024-11-22 72.350601 75.350601 73.43 73.43 2024-11-19\n", + "158 2024-11-25 72.403636 75.403636 72.30 72.30 2024-11-19\n", + "159 2024-11-26 72.408611 75.408611 71.63 71.63 2024-11-19\n", + "161 2024-11-22 71.625726 74.625726 73.43 73.43 2024-11-20\n", + "162 2024-11-25 71.746366 74.746366 72.30 72.30 2024-11-20\n", + "163 2024-11-26 71.802204 74.802204 71.63 71.63 2024-11-20\n", + "164 2024-11-27 71.483702 74.483702 71.71 71.71 2024-11-20\n", + "165 2024-11-22 73.542202 76.542202 73.43 73.43 2024-11-21\n", + "166 2024-11-25 73.833649 76.833649 72.30 72.30 2024-11-21\n", + "167 2024-11-26 73.836256 76.836256 71.63 71.63 2024-11-21\n", + "168 2024-11-27 74.010098 77.010098 71.71 71.71 2024-11-21\n", + "169 2024-11-28 74.079505 77.079505 71.85 71.85 2024-11-21\n", + "170 2024-11-25 74.152900 77.152900 72.30 72.30 2024-11-22\n", + "171 2024-11-26 74.268427 77.268427 71.63 71.63 2024-11-22\n", + "172 2024-11-27 74.185521 77.185521 71.71 71.71 2024-11-22\n", + "173 2024-11-28 74.473107 77.473107 71.85 71.85 2024-11-22\n", + "174 2024-11-29 74.723802 77.723802 71.75 71.75 2024-11-22\n", + "['2024-11-22', '2024-11-23', '2024-11-24', '2024-11-25', '2024-11-26', '2024-11-27', '2024-11-28', '2024-11-29']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "149 2024-11-22 70.457320 73.457320 73.43 73.43 2024-11-15\n", + "153 2024-11-22 72.701802 75.701802 73.43 73.43 2024-11-18\n", + "154 2024-11-25 72.861929 75.861929 72.30 72.30 2024-11-18\n", + "157 2024-11-22 72.350601 75.350601 73.43 73.43 2024-11-19\n", + "158 2024-11-25 72.403636 75.403636 72.30 72.30 2024-11-19\n", + "159 2024-11-26 72.408611 75.408611 71.63 71.63 2024-11-19\n", + "161 2024-11-22 71.625726 74.625726 73.43 73.43 2024-11-20\n", + "162 2024-11-25 71.746366 74.746366 72.30 72.30 2024-11-20\n", + "163 2024-11-26 71.802204 74.802204 71.63 71.63 2024-11-20\n", + "164 2024-11-27 71.483702 74.483702 71.71 71.71 2024-11-20\n", + "165 2024-11-22 73.542202 76.542202 73.43 73.43 2024-11-21\n", + "166 2024-11-25 73.833649 76.833649 72.30 72.30 2024-11-21\n", + "167 2024-11-26 73.836256 76.836256 71.63 71.63 2024-11-21\n", + "168 2024-11-27 74.010098 77.010098 71.71 71.71 2024-11-21\n", + "169 2024-11-28 74.079505 77.079505 71.85 71.85 2024-11-21\n", + "170 2024-11-25 74.152900 77.152900 72.30 72.30 2024-11-22\n", + "171 2024-11-26 74.268427 77.268427 71.63 71.63 2024-11-22\n", + "172 2024-11-27 74.185521 77.185521 71.71 71.71 2024-11-22\n", + "173 2024-11-28 74.473107 77.473107 71.85 71.85 2024-11-22\n", + "174 2024-11-29 74.723802 77.723802 71.75 71.75 2024-11-22\n", + "['2024-11-22', '2024-11-23', '2024-11-24', '2024-11-25', '2024-11-26', '2024-11-27', '2024-11-28', '2024-11-29']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "149 2024-11-22 70.457320 73.457320 73.43 73.43 2024-11-15\n", + "153 2024-11-22 72.701802 75.701802 73.43 73.43 2024-11-18\n", + "154 2024-11-25 72.861929 75.861929 72.30 72.30 2024-11-18\n", + "157 2024-11-22 72.350601 75.350601 73.43 73.43 2024-11-19\n", + "158 2024-11-25 72.403636 75.403636 72.30 72.30 2024-11-19\n", + "159 2024-11-26 72.408611 75.408611 71.63 71.63 2024-11-19\n", + "161 2024-11-22 71.625726 74.625726 73.43 73.43 2024-11-20\n", + "162 2024-11-25 71.746366 74.746366 72.30 72.30 2024-11-20\n", + "163 2024-11-26 71.802204 74.802204 71.63 71.63 2024-11-20\n", + "164 2024-11-27 71.483702 74.483702 71.71 71.71 2024-11-20\n", + "165 2024-11-22 73.542202 76.542202 73.43 73.43 2024-11-21\n", + "166 2024-11-25 73.833649 76.833649 72.30 72.30 2024-11-21\n", + "167 2024-11-26 73.836256 76.836256 71.63 71.63 2024-11-21\n", + "168 2024-11-27 74.010098 77.010098 71.71 71.71 2024-11-21\n", + "169 2024-11-28 74.079505 77.079505 71.85 71.85 2024-11-21\n", + "170 2024-11-25 74.152900 77.152900 72.30 72.30 2024-11-22\n", + "171 2024-11-26 74.268427 77.268427 71.63 71.63 2024-11-22\n", + "172 2024-11-27 74.185521 77.185521 71.71 71.71 2024-11-22\n", + "173 2024-11-28 74.473107 77.473107 71.85 71.85 2024-11-22\n", + "174 2024-11-29 74.723802 77.723802 71.75 71.75 2024-11-22\n", + "['2024-11-29', '2024-11-30', '2024-12-01', '2024-12-02', '2024-12-03', '2024-12-04', '2024-12-05', '2024-12-06']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "174 2024-11-29 74.723802 77.723802 71.75 71.75 2024-11-22\n", + "178 2024-11-29 72.833511 75.833511 71.75 71.75 2024-11-25\n", + "179 2024-12-02 72.992029 75.992029 71.52 71.52 2024-11-25\n", + "182 2024-11-29 72.366704 75.366704 71.75 71.75 2024-11-26\n", + "183 2024-12-02 72.674196 75.674196 71.52 71.52 2024-11-26\n", + "184 2024-12-03 72.549176 75.549176 71.68 71.68 2024-11-26\n", + "186 2024-11-29 72.706763 75.706763 71.75 71.75 2024-11-27\n", + "187 2024-12-02 72.719974 75.719974 71.52 71.52 2024-11-27\n", + "188 2024-12-03 73.250393 76.250393 71.68 71.68 2024-11-27\n", + "189 2024-12-04 73.279968 76.279968 72.25 72.25 2024-11-27\n", + "190 2024-11-29 72.307409 75.307409 71.75 71.75 2024-11-28\n", + "191 2024-12-02 72.440306 75.440306 71.52 71.52 2024-11-28\n", + "192 2024-12-03 72.684748 75.684748 71.68 71.68 2024-11-28\n", + "193 2024-12-04 72.904345 75.904345 72.25 72.25 2024-11-28\n", + "194 2024-12-05 73.119072 76.119072 71.80 71.80 2024-11-28\n", + "195 2024-12-02 71.978429 74.978429 71.52 71.52 2024-11-29\n", + "196 2024-12-03 72.177397 75.177397 71.68 71.68 2024-11-29\n", + "197 2024-12-04 72.111895 75.111895 72.25 72.25 2024-11-29\n", + "198 2024-12-05 71.917178 74.917178 71.80 71.80 2024-11-29\n", + "199 2024-12-06 71.973777 74.973777 70.85 70.85 2024-11-29\n", + "['2024-11-29', '2024-11-30', '2024-12-01', '2024-12-02', '2024-12-03', '2024-12-04', '2024-12-05', '2024-12-06']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "174 2024-11-29 74.723802 77.723802 71.75 71.75 2024-11-22\n", + "178 2024-11-29 72.833511 75.833511 71.75 71.75 2024-11-25\n", + "179 2024-12-02 72.992029 75.992029 71.52 71.52 2024-11-25\n", + "182 2024-11-29 72.366704 75.366704 71.75 71.75 2024-11-26\n", + "183 2024-12-02 72.674196 75.674196 71.52 71.52 2024-11-26\n", + "184 2024-12-03 72.549176 75.549176 71.68 71.68 2024-11-26\n", + "186 2024-11-29 72.706763 75.706763 71.75 71.75 2024-11-27\n", + "187 2024-12-02 72.719974 75.719974 71.52 71.52 2024-11-27\n", + "188 2024-12-03 73.250393 76.250393 71.68 71.68 2024-11-27\n", + "189 2024-12-04 73.279968 76.279968 72.25 72.25 2024-11-27\n", + "190 2024-11-29 72.307409 75.307409 71.75 71.75 2024-11-28\n", + "191 2024-12-02 72.440306 75.440306 71.52 71.52 2024-11-28\n", + "192 2024-12-03 72.684748 75.684748 71.68 71.68 2024-11-28\n", + "193 2024-12-04 72.904345 75.904345 72.25 72.25 2024-11-28\n", + "194 2024-12-05 73.119072 76.119072 71.80 71.80 2024-11-28\n", + "195 2024-12-02 71.978429 74.978429 71.52 71.52 2024-11-29\n", + "196 2024-12-03 72.177397 75.177397 71.68 71.68 2024-11-29\n", + "197 2024-12-04 72.111895 75.111895 72.25 72.25 2024-11-29\n", + "198 2024-12-05 71.917178 74.917178 71.80 71.80 2024-11-29\n", + "199 2024-12-06 71.973777 74.973777 70.85 70.85 2024-11-29\n", + "['2024-11-29', '2024-11-30', '2024-12-01', '2024-12-02', '2024-12-03', '2024-12-04', '2024-12-05', '2024-12-06']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "174 2024-11-29 74.723802 77.723802 71.75 71.75 2024-11-22\n", + "178 2024-11-29 72.833511 75.833511 71.75 71.75 2024-11-25\n", + "179 2024-12-02 72.992029 75.992029 71.52 71.52 2024-11-25\n", + "182 2024-11-29 72.366704 75.366704 71.75 71.75 2024-11-26\n", + "183 2024-12-02 72.674196 75.674196 71.52 71.52 2024-11-26\n", + "184 2024-12-03 72.549176 75.549176 71.68 71.68 2024-11-26\n", + "186 2024-11-29 72.706763 75.706763 71.75 71.75 2024-11-27\n", + "187 2024-12-02 72.719974 75.719974 71.52 71.52 2024-11-27\n", + "188 2024-12-03 73.250393 76.250393 71.68 71.68 2024-11-27\n", + "189 2024-12-04 73.279968 76.279968 72.25 72.25 2024-11-27\n", + "190 2024-11-29 72.307409 75.307409 71.75 71.75 2024-11-28\n", + "191 2024-12-02 72.440306 75.440306 71.52 71.52 2024-11-28\n", + "192 2024-12-03 72.684748 75.684748 71.68 71.68 2024-11-28\n", + "193 2024-12-04 72.904345 75.904345 72.25 72.25 2024-11-28\n", + "194 2024-12-05 73.119072 76.119072 71.80 71.80 2024-11-28\n", + "195 2024-12-02 71.978429 74.978429 71.52 71.52 2024-11-29\n", + "196 2024-12-03 72.177397 75.177397 71.68 71.68 2024-11-29\n", + "197 2024-12-04 72.111895 75.111895 72.25 72.25 2024-11-29\n", + "198 2024-12-05 71.917178 74.917178 71.80 71.80 2024-11-29\n", + "199 2024-12-06 71.973777 74.973777 70.85 70.85 2024-11-29\n", + "['2024-11-29', '2024-11-30', '2024-12-01', '2024-12-02', '2024-12-03', '2024-12-04', '2024-12-05', '2024-12-06']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "174 2024-11-29 74.723802 77.723802 71.75 71.75 2024-11-22\n", + "178 2024-11-29 72.833511 75.833511 71.75 71.75 2024-11-25\n", + "179 2024-12-02 72.992029 75.992029 71.52 71.52 2024-11-25\n", + "182 2024-11-29 72.366704 75.366704 71.75 71.75 2024-11-26\n", + "183 2024-12-02 72.674196 75.674196 71.52 71.52 2024-11-26\n", + "184 2024-12-03 72.549176 75.549176 71.68 71.68 2024-11-26\n", + "186 2024-11-29 72.706763 75.706763 71.75 71.75 2024-11-27\n", + "187 2024-12-02 72.719974 75.719974 71.52 71.52 2024-11-27\n", + "188 2024-12-03 73.250393 76.250393 71.68 71.68 2024-11-27\n", + "189 2024-12-04 73.279968 76.279968 72.25 72.25 2024-11-27\n", + "190 2024-11-29 72.307409 75.307409 71.75 71.75 2024-11-28\n", + "191 2024-12-02 72.440306 75.440306 71.52 71.52 2024-11-28\n", + "192 2024-12-03 72.684748 75.684748 71.68 71.68 2024-11-28\n", + "193 2024-12-04 72.904345 75.904345 72.25 72.25 2024-11-28\n", + "194 2024-12-05 73.119072 76.119072 71.80 71.80 2024-11-28\n", + "195 2024-12-02 71.978429 74.978429 71.52 71.52 2024-11-29\n", + "196 2024-12-03 72.177397 75.177397 71.68 71.68 2024-11-29\n", + "197 2024-12-04 72.111895 75.111895 72.25 72.25 2024-11-29\n", + "198 2024-12-05 71.917178 74.917178 71.80 71.80 2024-11-29\n", + "199 2024-12-06 71.973777 74.973777 70.85 70.85 2024-11-29\n", + "['2024-11-29', '2024-11-30', '2024-12-01', '2024-12-02', '2024-12-03', '2024-12-04', '2024-12-05', '2024-12-06']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "174 2024-11-29 74.723802 77.723802 71.75 71.75 2024-11-22\n", + "178 2024-11-29 72.833511 75.833511 71.75 71.75 2024-11-25\n", + "179 2024-12-02 72.992029 75.992029 71.52 71.52 2024-11-25\n", + "182 2024-11-29 72.366704 75.366704 71.75 71.75 2024-11-26\n", + "183 2024-12-02 72.674196 75.674196 71.52 71.52 2024-11-26\n", + "184 2024-12-03 72.549176 75.549176 71.68 71.68 2024-11-26\n", + "186 2024-11-29 72.706763 75.706763 71.75 71.75 2024-11-27\n", + "187 2024-12-02 72.719974 75.719974 71.52 71.52 2024-11-27\n", + "188 2024-12-03 73.250393 76.250393 71.68 71.68 2024-11-27\n", + "189 2024-12-04 73.279968 76.279968 72.25 72.25 2024-11-27\n", + 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71.344403 74.344403 70.92 70.92 2024-12-02\n", + "207 2024-12-06 72.125730 75.125730 70.85 70.85 2024-12-03\n", + "208 2024-12-09 72.220322 75.220322 70.92 70.92 2024-12-03\n", + "209 2024-12-10 72.069719 75.069719 70.73 70.73 2024-12-03\n", + "211 2024-12-06 70.098060 73.098060 70.85 70.85 2024-12-04\n", + "212 2024-12-09 70.214520 73.214520 70.92 70.92 2024-12-04\n", + "213 2024-12-10 70.193227 73.193227 70.73 70.73 2024-12-04\n", + "214 2024-12-11 70.352228 73.352228 72.15 72.15 2024-12-04\n", + "215 2024-12-06 69.842873 72.842873 70.85 70.85 2024-12-05\n", + "216 2024-12-09 70.212922 73.212922 70.92 70.92 2024-12-05\n", + "217 2024-12-10 70.203988 73.203988 70.73 70.73 2024-12-05\n", + "218 2024-12-11 70.258084 73.258084 72.15 72.15 2024-12-05\n", + "219 2024-12-12 70.810124 73.810124 72.42 72.42 2024-12-05\n", + "220 2024-12-09 69.482207 72.482207 70.92 70.92 2024-12-06\n", + "221 2024-12-10 69.475770 72.475770 70.73 70.73 2024-12-06\n", + "222 2024-12-11 69.875355 72.875355 72.15 72.15 2024-12-06\n", + "223 2024-12-12 70.133042 73.133042 72.42 72.42 2024-12-06\n", + "224 2024-12-13 70.209316 73.209316 73.30 73.30 2024-12-06\n", + "['2024-12-06', '2024-12-07', '2024-12-08', '2024-12-09', '2024-12-10', '2024-12-11', '2024-12-12', '2024-12-13']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "199 2024-12-06 71.973777 74.973777 70.85 70.85 2024-11-29\n", + "203 2024-12-06 71.060380 74.060380 70.85 70.85 2024-12-02\n", + "204 2024-12-09 71.344403 74.344403 70.92 70.92 2024-12-02\n", + "207 2024-12-06 72.125730 75.125730 70.85 70.85 2024-12-03\n", + "208 2024-12-09 72.220322 75.220322 70.92 70.92 2024-12-03\n", + "209 2024-12-10 72.069719 75.069719 70.73 70.73 2024-12-03\n", + "211 2024-12-06 70.098060 73.098060 70.85 70.85 2024-12-04\n", + "212 2024-12-09 70.214520 73.214520 70.92 70.92 2024-12-04\n", + "213 2024-12-10 70.193227 73.193227 70.73 70.73 2024-12-04\n", + "214 2024-12-11 70.352228 73.352228 72.15 72.15 2024-12-04\n", + "215 2024-12-06 69.842873 72.842873 70.85 70.85 2024-12-05\n", + "216 2024-12-09 70.212922 73.212922 70.92 70.92 2024-12-05\n", + "217 2024-12-10 70.203988 73.203988 70.73 70.73 2024-12-05\n", + "218 2024-12-11 70.258084 73.258084 72.15 72.15 2024-12-05\n", + "219 2024-12-12 70.810124 73.810124 72.42 72.42 2024-12-05\n", + "220 2024-12-09 69.482207 72.482207 70.92 70.92 2024-12-06\n", + "221 2024-12-10 69.475770 72.475770 70.73 70.73 2024-12-06\n", + "222 2024-12-11 69.875355 72.875355 72.15 72.15 2024-12-06\n", + "223 2024-12-12 70.133042 73.133042 72.42 72.42 2024-12-06\n", + "224 2024-12-13 70.209316 73.209316 73.30 73.30 2024-12-06\n", + "['2024-12-06', '2024-12-07', '2024-12-08', '2024-12-09', '2024-12-10', '2024-12-11', '2024-12-12', '2024-12-13']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "199 2024-12-06 71.973777 74.973777 70.85 70.85 2024-11-29\n", + "203 2024-12-06 71.060380 74.060380 70.85 70.85 2024-12-02\n", + "204 2024-12-09 71.344403 74.344403 70.92 70.92 2024-12-02\n", + "207 2024-12-06 72.125730 75.125730 70.85 70.85 2024-12-03\n", + "208 2024-12-09 72.220322 75.220322 70.92 70.92 2024-12-03\n", + "209 2024-12-10 72.069719 75.069719 70.73 70.73 2024-12-03\n", + "211 2024-12-06 70.098060 73.098060 70.85 70.85 2024-12-04\n", + "212 2024-12-09 70.214520 73.214520 70.92 70.92 2024-12-04\n", + "213 2024-12-10 70.193227 73.193227 70.73 70.73 2024-12-04\n", + "214 2024-12-11 70.352228 73.352228 72.15 72.15 2024-12-04\n", + "215 2024-12-06 69.842873 72.842873 70.85 70.85 2024-12-05\n", + "216 2024-12-09 70.212922 73.212922 70.92 70.92 2024-12-05\n", + "217 2024-12-10 70.203988 73.203988 70.73 70.73 2024-12-05\n", + "218 2024-12-11 70.258084 73.258084 72.15 72.15 2024-12-05\n", + "219 2024-12-12 70.810124 73.810124 72.42 72.42 2024-12-05\n", + "220 2024-12-09 69.482207 72.482207 70.92 70.92 2024-12-06\n", + "221 2024-12-10 69.475770 72.475770 70.73 70.73 2024-12-06\n", + "222 2024-12-11 69.875355 72.875355 72.15 72.15 2024-12-06\n", + "223 2024-12-12 70.133042 73.133042 72.42 72.42 2024-12-06\n", + "224 2024-12-13 70.209316 73.209316 73.30 73.30 2024-12-06\n", + "['2024-12-06', '2024-12-07', '2024-12-08', '2024-12-09', '2024-12-10', '2024-12-11', '2024-12-12', '2024-12-13']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "199 2024-12-06 71.973777 74.973777 70.85 70.85 2024-11-29\n", + "203 2024-12-06 71.060380 74.060380 70.85 70.85 2024-12-02\n", + "204 2024-12-09 71.344403 74.344403 70.92 70.92 2024-12-02\n", + "207 2024-12-06 72.125730 75.125730 70.85 70.85 2024-12-03\n", + "208 2024-12-09 72.220322 75.220322 70.92 70.92 2024-12-03\n", + "209 2024-12-10 72.069719 75.069719 70.73 70.73 2024-12-03\n", + "211 2024-12-06 70.098060 73.098060 70.85 70.85 2024-12-04\n", + "212 2024-12-09 70.214520 73.214520 70.92 70.92 2024-12-04\n", + "213 2024-12-10 70.193227 73.193227 70.73 70.73 2024-12-04\n", + "214 2024-12-11 70.352228 73.352228 72.15 72.15 2024-12-04\n", + "215 2024-12-06 69.842873 72.842873 70.85 70.85 2024-12-05\n", + "216 2024-12-09 70.212922 73.212922 70.92 70.92 2024-12-05\n", + "217 2024-12-10 70.203988 73.203988 70.73 70.73 2024-12-05\n", + "218 2024-12-11 70.258084 73.258084 72.15 72.15 2024-12-05\n", + "219 2024-12-12 70.810124 73.810124 72.42 72.42 2024-12-05\n", + "220 2024-12-09 69.482207 72.482207 70.92 70.92 2024-12-06\n", + "221 2024-12-10 69.475770 72.475770 70.73 70.73 2024-12-06\n", + "222 2024-12-11 69.875355 72.875355 72.15 72.15 2024-12-06\n", + "223 2024-12-12 70.133042 73.133042 72.42 72.42 2024-12-06\n", + "224 2024-12-13 70.209316 73.209316 73.30 73.30 2024-12-06\n", + "['2024-12-06', '2024-12-07', '2024-12-08', '2024-12-09', '2024-12-10', '2024-12-11', '2024-12-12', '2024-12-13']\n", + "(30, 6)\n", + " ds min_price max_price LOW_PRICE LOW_PRICE CREAT_DATE\n", + "199 2024-12-06 71.973777 74.973777 70.85 70.85 2024-11-29\n", + "203 2024-12-06 71.060380 74.060380 70.85 70.85 2024-12-02\n", + "204 2024-12-09 71.344403 74.344403 70.92 70.92 2024-12-02\n", + "207 2024-12-06 72.125730 75.125730 70.85 70.85 2024-12-03\n", + "208 2024-12-09 72.220322 75.220322 70.92 70.92 2024-12-03\n", + "209 2024-12-10 72.069719 75.069719 70.73 70.73 2024-12-03\n", + "211 2024-12-06 70.098060 73.098060 70.85 70.85 2024-12-04\n", + "212 2024-12-09 70.214520 73.214520 70.92 70.92 2024-12-04\n", + "213 2024-12-10 70.193227 73.193227 70.73 70.73 2024-12-04\n", + "214 2024-12-11 70.352228 73.352228 72.15 72.15 2024-12-04\n", + "215 2024-12-06 69.842873 72.842873 70.85 70.85 2024-12-05\n", + "216 2024-12-09 70.212922 73.212922 70.92 70.92 2024-12-05\n", + "217 2024-12-10 70.203988 73.203988 70.73 70.73 2024-12-05\n", + "218 2024-12-11 70.258084 73.258084 72.15 72.15 2024-12-05\n", + "219 2024-12-12 70.810124 73.810124 72.42 72.42 2024-12-05\n", + "220 2024-12-09 69.482207 72.482207 70.92 70.92 2024-12-06\n", + "221 2024-12-10 69.475770 72.475770 70.73 70.73 2024-12-06\n", + "222 2024-12-11 69.875355 72.875355 72.15 72.15 2024-12-06\n", + "223 2024-12-12 70.133042 73.133042 72.42 72.42 2024-12-06\n", + "224 2024-12-13 70.209316 73.209316 73.30 73.30 2024-12-06\n" ] }, { "data": { - "image/png": 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" + "
" ] }, "metadata": {}, @@ -396,7 +1406,7 @@ "df5 = df5[['ds','min_price','max_price','LOW_PRICE','LOW_PRICE','CREAT_DATE']]\n", "\n", "print(df5.shape)\n", - "print(df5.head(10))\n", + "print(df5.head(20))\n", "# 画图配置\n", "plt.figure(figsize=(16,10))\n", "\n", @@ -404,11 +1414,10 @@ " endtime = end_time\n", " # endtimeweek = datetime.datetime.strptime(endtime, '%Y-%m-%d')\n", " endtimeweek = endtime\n", - " up_week = endtimeweek - datetime.timedelta(days=endtimeweek.weekday() )\n", - " up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(7)]\n", + " up_week = endtimeweek - datetime.timedelta(days=endtimeweek.weekday() + 14)\n", + " up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(14)][4:-2]\n", " up_week_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates]\n", " return up_week_dates\n", - "\n", "# ds分组\n", "end_times = df['ds'].unique()\n", "for endtime in end_times:\n", @@ -416,13 +1425,13 @@ " print(up_week_dates)\n", " df6 = df5[df5['ds'].isin(up_week_dates)]\n", " print(df6.shape)\n", - " print(df6.head(10))\n", + " print(df6.head(20))\n", " # sns画散点图\n", - " sns.scatterplot(x=df6.index,y=df6['min_price'].values,data=df6)\n", - " sns.scatterplot(x=df6.index,y=df6['max_price'].values,data=df6)\n", - " sns.scatterplot(x=df6.index,y=df6['LOW_PRICE'].values,data=df6)\n", - " sns.scatterplot(x=df6.index,y=df6['HIGH_PRICE'].values,data=df6)\n", - " sns.scatterplot(x=df6.index,y=df6['HIGH_PRICE'].values,data=df6)" + " # sns.scatterplot(x=df6.index,y=df6['min_price'].values,data=df6)\n", + " # sns.scatterplot(x=df6.index,y=df6['max_price'].values,data=df6)\n", + " # sns.scatterplot(x=df6.index,y=df6['LOW_PRICE'].values,data=df6)\n", + " # sns.scatterplot(x=df6.index,y=df6['HIGH_PRICE'].values,data=df6)\n", + " # sns.scatterplot(x=df6.index,y=df6['HIGH_PRICE'].values,data=df6)" ] }, {