893 lines
41 KiB
Plaintext
893 lines
41 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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" <script type=\"text/javascript\">\n",
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" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
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" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
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" if (typeof require !== 'undefined') {\n",
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" require.undef(\"plotly\");\n",
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" requirejs.config({\n",
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" paths: {\n",
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" 'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
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" }\n",
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" });\n",
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" require(['plotly'], function(Plotly) {\n",
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" window._Plotly = Plotly;\n",
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" });\n",
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" }\n",
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" </script>\n",
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" "
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import requests\n",
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"import json\n",
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"import xlrd\n",
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"import xlwt\n",
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"from datetime import datetime,timedelta\n",
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"import time\n",
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"# 变量定义\n",
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"login_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
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"search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos\"\n",
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"# search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos\" \n",
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"\n",
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"\n",
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"login_push_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
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"upload_url = \"http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList\"\n",
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"\n",
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"login_data = {\n",
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" \"data\": {\n",
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" \"account\": \"api_dev\",\n",
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" \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n",
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" \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n",
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" \"terminal\": \"API\"\n",
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" },\n",
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" \"funcModule\": \"API\",\n",
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" \"funcOperation\": \"获取token\"\n",
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"}\n",
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"\n",
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"login_push_data = {\n",
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" \"data\": {\n",
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" \"account\": \"api_dev\",\n",
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" \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n",
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" \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n",
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" \"terminal\": \"API\"\n",
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" },\n",
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" \"funcModule\": \"API\",\n",
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" \"funcOperation\": \"获取token\"\n",
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"}\n",
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"\n",
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"read_file_path_name = \"液化气数据.xls\"\n",
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"one_cols = []\n",
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"two_cols = []\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sn\n",
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"import random\n",
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"import time\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"from plotly import __version__\n",
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"from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
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"\n",
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"from sklearn import preprocessing\n",
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"\n",
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"from pandas import Series,DataFrame\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import sklearn.datasets as datasets\n",
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"\n",
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"#导入机器学习算法模型\n",
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"from sklearn.linear_model import Lasso\n",
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"from xgboost import XGBRegressor\n",
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"\n",
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"import statsmodels.api as sm\n",
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"try:\n",
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" from keras.preprocessing.sequence import TimeseriesGenerator\n",
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"except:\n",
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" from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
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"\n",
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"import plotly.express as px\n",
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"import plotly.graph_objects as go\n",
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"\n",
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"import xgboost as xgb\n",
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"from xgboost import plot_importance, plot_tree\n",
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"from sklearn.metrics import mean_absolute_error\n",
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"from statsmodels.tools.eval_measures import mse,rmse\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"from xgboost import XGBRegressor\n",
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"import warnings\n",
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"import pickle\n",
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"\n",
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"from sklearn.metrics import mean_squared_error\n",
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"\n",
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"#切割训练数据和样本数据\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"#用于模型评分\n",
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"from sklearn.metrics import r2_score\n",
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"\n",
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"le = preprocessing.LabelEncoder()\n",
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"\n",
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"# print(__version__) # requires version >= 1.9.0\n",
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"\n",
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"\n",
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"import cufflinks as cf\n",
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"cf.go_offline()\n",
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"\n",
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"random.seed(100)\n",
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"\n",
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"%matplotlib inline\n",
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"\n",
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"# 数据获取\n",
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"\n",
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"def get_head_auth():\n",
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" login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))\n",
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" text = json.loads(login_res.text)\n",
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" if text[\"status\"]:\n",
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" token = text[\"data\"][\"accessToken\"]\n",
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" print('获取的token:',token)\n",
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" return token\n",
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" else:\n",
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" print(\"获取认证失败\")\n",
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" return None\n",
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"\n",
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"\n",
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"def get_data_value(token, dataItemNoList,date):\n",
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" search_data = {\n",
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" \"data\": {\n",
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" \"date\": date,\n",
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" \"dataItemNoList\": dataItemNoList\n",
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" },\n",
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" \"funcModule\": \"数据项\",\n",
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" \"funcOperation\": \"查询\"\n",
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" }\n",
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" \n",
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"# search_data = {\n",
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"# \"funcModule\": \"数据项\",\n",
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"# \"funcOperation\": \"查询\",\n",
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"# \"data\": {\n",
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"# \"dateStart\": date,\n",
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"# \"dateEnd\": date,\n",
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"# \"dataItemNoList\":dataItemNoList # 数据项编码,代表 brent最低价和最高价\n",
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"# }\n",
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"# }\n",
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" \n",
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" headers = {\"Authorization\": token}\n",
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" search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
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" print('数据项查询参数search_data：')\n",
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" print(search_data)\n",
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" print('数据项查询结果search_res：')\n",
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" print(search_res.text)\n",
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" \n",
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" try:\n",
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" search_value = json.loads(search_res.text)[\"data\"]\n",
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" \n",
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" print(\"数据项查询结果:\", search_value)\n",
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" except json.JSONDecodeError as e:\n",
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" print(f\"Error decoding JSON: {e}\")\n",
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" print(\"Response content:\", search_res.text)\n",
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" return None \n",
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" if search_value:\n",
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" return search_value\n",
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" else:\n",
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" print(\"今天没有新数据\")\n",
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" return search_value\n",
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"\n",
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"\n",
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"\n",
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"# xls文件处理\n",
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"\n",
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"def write_xls(data):\n",
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" # 创建一个Workbook对象\n",
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" workbook = xlwt.Workbook()\n",
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"\n",
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" # 创建一个Sheet对象，可指定名称\n",
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" sheet = workbook.load('Sheet1')\n",
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"\n",
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" # 写入数据行\n",
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" for row_index, row_data in enumerate(data):\n",
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" for col_index, cell_data in enumerate(row_data):\n",
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" sheet.write(row_index, col_index, cell_data)\n",
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"\n",
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" # 保存Workbook到文件\n",
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" workbook.save(get_cur_time()[0] + '.xls')\n",
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"\n",
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"\n",
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"def get_cur_time(date = ''):\n",
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" if date == '':\n",
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" now = datetime.now()\n",
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" else:\n",
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" now = date\n",
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" year = now.year\n",
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" month = now.month\n",
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" day = now.day\n",
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"\n",
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" if month < 10:\n",
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" month = \"0\" + str(month)\n",
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" if day < 10:\n",
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" day = \"0\" + str(day)\n",
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" cur_time = str(year) + str(month) + str(day)\n",
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" cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
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"# cur_time = '20231011'\n",
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"# cur_time2 = '2023-10-11'\n",
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" return cur_time, cur_time2\n",
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"\n",
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"\n",
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"\n",
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"def get_head_push_auth():\n",
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" login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))\n",
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" text = json.loads(login_res.text)\n",
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" if text[\"status\"]:\n",
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" token = text[\"data\"][\"accessToken\"]\n",
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" return token\n",
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" else:\n",
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" print(\"获取认证失败\")\n",
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" return None\n",
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"\n",
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"\n",
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"\n",
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"def upload_data_to_system(token_push,date):\n",
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" data = {\n",
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" \"funcModule\": \"数据表信息列表\",\n",
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" \"funcOperation\": \"新增\",\n",
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" \"data\": [\n",
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" {\"dataItemNo\": \"250855713|Forecast_Price|ACN\",\n",
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" \"dataDate\": date,\n",
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" \"dataStatus\": \"add\",\n",
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" \"dataValue\": forecast_price()\n",
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" }\n",
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"\n",
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" ]\n",
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" }\n",
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" # headers = {\"Authorization\": token_push}\n",
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" # res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
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" # print(res.text)\n",
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" print('预测值：',data['data'][0]['dataValue'])\n",
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"\n",
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" \n",
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"# def upload_data_to_system(token):\n",
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"# data = {\n",
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"# \"funcModule\": \"数据表信息列表\",\n",
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"# \"funcOperation\": \"新增\",\n",
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"# \"data\": [\n",
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"# {\"dataItemNo\": \"C01100036|Forecast_ Price|ACN\",\n",
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"# \"dataDate\": '20230706',\n",
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"# \"dataStatus\": \"add\",\n",
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"# \"dataValue\": 3780.0\n",
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"# }\n",
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"\n",
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"# ]\n",
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"# }\n",
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"# headers = {\"Authorization\": token}\n",
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"# res = requests.post(url=upload_url, headers=headers, json=data, timeout=(3, 5))\n",
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"# print(res.text)\n",
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"\n",
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"price_list = []\n",
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" \n",
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"def forecast_price():\n",
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" # df_test = pd.read_csv('定价模型数据收集0212.csv')\n",
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" df_test = pd.read_excel('液化气数据.xls')\n",
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" df_test.drop([0],inplace=True)\n",
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" try:\n",
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" df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)\n",
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" except:\n",
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" df_test['Date']=pd.to_datetime(df_test['Date'], format=r'%Y-%m-%d',infer_datetime_format=True)\n",
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"\n",
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"\n",
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" df_test_1 = df_test\n",
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" df_test_1=df_test_1.fillna(df_test.ffill())\n",
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" df_test_1=df_test_1.fillna(df_test_1.bfill())\n",
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"\n",
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" # 选择用于模型训练的列名称\n",
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" col_for_training = df_test_1.columns\n",
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"\n",
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"\n",
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"\n",
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"\n",
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" import joblib\n",
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" Best_model_DalyLGPrice = joblib.load(\"日度价格预测_液化气最佳模型.pkl\")\n",
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" # 最新的一天为最后一行的数据\n",
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" \n",
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" df_test_1_Day = df_test_1.tail(1)\n",
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" # 移除不需要的列\n",
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" df_test_1_Day.index = df_test_1_Day[\"Date\"]\n",
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" df_test_1_Day = df_test_1_Day.drop([\"Date\"], axis= 1)\n",
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" df_test_1_Day=df_test_1_Day.drop('Price',axis=1)\n",
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" df_test_1_Day=df_test_1_Day.dropna()\n",
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"\n",
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" for col in df_test_1_Day.columns:\n",
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" df_test_1_Day[col] = pd.to_numeric(df_test_1_Day[col],errors='coerce')\n",
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" #预测今日价格，显示至小数点后两位\n",
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" Ypredict_Today=Best_model_DalyLGPrice.predict(df_test_1_Day)\n",
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"\n",
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" df_test_1_Day['日度预测价格']=Ypredict_Today\n",
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" print(df_test_1_Day['日度预测价格'])\n",
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" a = df_test_1_Day['日度预测价格']\n",
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" a = a[0]\n",
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" a = float(a)\n",
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" a = round(a,2)\n",
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" price_list.append(a)\n",
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" return a\n",
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"def optimize_Model():\n",
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" from sklearn.model_selection import train_test_split\n",
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" from sklearn.impute import SimpleImputer\n",
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" from sklearn.preprocessing import OrdinalEncoder\n",
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" from sklearn.feature_selection import SelectFromModel\n",
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" from sklearn.metrics import mean_squared_error, r2_score\n",
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" import pandas as pd\n",
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"\n",
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" pd.set_option('display.max_rows',40) \n",
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" pd.set_option('display.max_columns',40) \n",
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" df_test = pd.read_excel('液化气数据.xls')\n",
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" df_test.drop([0],inplace=True)\n",
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" try:\n",
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" df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)\n",
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" except:\n",
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" df_test['Date']=pd.to_datetime(df_test['Date'], format=r'%Y-%m-%d',infer_datetime_format=True)\n",
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"\n",
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" \n",
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" #将缺失值补为前一个或者后一个数值\n",
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" df_test_1 = df_test\n",
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" df_test_1=df_test_1.fillna(df_test.ffill())\n",
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" df_test_1=df_test_1.fillna(df_test_1.bfill())\n",
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" df_test_1[\"Date\"] = pd.to_datetime(df_test_1[\"Date\"])\n",
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" df_test_1.index = df_test_1[\"Date\"]\n",
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" df_test_1 = df_test_1.drop([\"Date\"], axis= 1)\n",
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" df_test_1 = df_test_1.astype('float')\n",
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" \n",
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" \n",
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" import numpy as np\n",
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" import pandas as pd\n",
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" from pandas import Series,DataFrame\n",
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"\n",
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" import matplotlib.pyplot as plt\n",
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"\n",
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" import sklearn.datasets as datasets\n",
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"\n",
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" #导入机器学习算法模型\n",
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" from sklearn.linear_model import Lasso\n",
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" from xgboost import XGBRegressor\n",
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"\n",
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" import statsmodels.api as sm\n",
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" try:\n",
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" from keras.preprocessing.sequence import TimeseriesGenerator\n",
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" except:\n",
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" from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
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"\n",
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" import plotly.express as px\n",
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" import plotly.graph_objects as go\n",
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"\n",
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" import xgboost as xgb\n",
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" from xgboost import plot_importance, plot_tree\n",
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" from sklearn.metrics import mean_absolute_error\n",
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" from statsmodels.tools.eval_measures import mse,rmse\n",
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" from sklearn.model_selection import GridSearchCV\n",
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" from xgboost import XGBRegressor\n",
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" import warnings\n",
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" import pickle\n",
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"\n",
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" from sklearn.metrics import mean_squared_error\n",
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"\n",
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" #切割训练数据和样本数据\n",
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" from sklearn.model_selection import train_test_split\n",
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"\n",
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" #用于模型评分\n",
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" from sklearn.metrics import r2_score\n",
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"\n",
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" dataset1=df_test_1.drop('Price',axis=1)#.astype(float)\n",
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"\n",
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" y=df_test_1['Price']\n",
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"\n",
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" x=dataset1 \n",
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"\n",
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" train = x\n",
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" target = y\n",
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"\n",
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" #切割数据样本集合测试集\n",
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" X_train,x_test,y_train,y_true = train_test_split(train,target,test_size=0.2,random_state=0)\n",
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"\n",
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" #模型缩写\n",
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" Lasso = Lasso(random_state=0)\n",
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" XGBR = XGBRegressor(random_state=0)\n",
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" #训练模型\n",
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" Lasso.fit(X_train,y_train)\n",
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" XGBR.fit(X_train,y_train)\n",
|
||
" #模型拟合\n",
|
||
" y_pre_Lasso = Lasso.predict(x_test)\n",
|
||
" y_pre_XGBR = XGBR.predict(x_test)\n",
|
||
"\n",
|
||
" #计算Lasso、XGBR、RandomForestR、AdaBoostR、GradientBoostingR、BaggingRegressor各模型的R²\n",
|
||
" Lasso_score = r2_score(y_true,y_pre_Lasso)\n",
|
||
" XGBR_score=r2_score(y_true,y_pre_XGBR)\n",
|
||
"\n",
|
||
" #计算Lasso、XGBR的MSE和RMSE\n",
|
||
" Lasso_MSE=mean_squared_error(y_true, y_pre_Lasso)\n",
|
||
" XGBR_MSE=mean_squared_error(y_true, y_pre_XGBR)\n",
|
||
"\n",
|
||
" Lasso_RMSE=np.sqrt(Lasso_MSE)\n",
|
||
" XGBR_RMSE=np.sqrt(XGBR_MSE)\n",
|
||
" # 将不同模型的不同误差值整合成一个表格\n",
|
||
" model_results = pd.DataFrame([['Lasso', Lasso_RMSE, Lasso_score],\n",
|
||
" ['XgBoost', XGBR_RMSE, XGBR_score]],\n",
|
||
" columns = ['模型(Model)','均方根误差(RMSE)', 'R^2 score'])\n",
|
||
" #将模型名称(Model)列设置为索引\n",
|
||
" model_results1=model_results.set_index('模型(Model)')\n",
|
||
"\n",
|
||
" model_results1\n",
|
||
" #定义plot_feature_importance函数，该函数用于计算特征重要性。此部分代码无需调整\n",
|
||
" def plot_feature_importance(importance,names,model_type):\n",
|
||
" feature_importance = np.array(importance)\n",
|
||
" feature_names = np.array(names)\n",
|
||
"\n",
|
||
" data={'feature_names':feature_names,'feature_importance':feature_importance}\n",
|
||
" fi_df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
" fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)\n",
|
||
"\n",
|
||
" plt.figure(figsize=(10,8))\n",
|
||
" sn.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])\n",
|
||
"\n",
|
||
" plt.title(model_type + \" \"+'FEATURE IMPORTANCE')\n",
|
||
" plt.xlabel('FEATURE IMPORTANCE')\n",
|
||
" plt.ylabel('FEATURE NAMES')\n",
|
||
" from pylab import mpl\n",
|
||
" %pylab\n",
|
||
" mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
|
||
" ## Xgboost 模型参数优化-初步\n",
|
||
" #参考: https://juejin.im/post/6844903661013827598 \n",
|
||
" #每次调参时，备选参数数值以同数量级的1、3、10设置即可（比如设置1、3、10，或0.1、0.3、1.0，或0.01,0.03,0.10即可）\n",
|
||
"\n",
|
||
" from xgboost import XGBRegressor\n",
|
||
" from sklearn.model_selection import GridSearchCV\n",
|
||
"\n",
|
||
" estimator = XGBRegressor(random_state=0,\n",
|
||
" nthread=4,\n",
|
||
" seed=0\n",
|
||
" )\n",
|
||
" parameters = {\n",
|
||
" 'max_depth': range (2, 11, 2), # 树的最大深度\n",
|
||
" 'n_estimators': range (50, 101, 10), # 迭代次数\n",
|
||
" 'learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]\n",
|
||
" }\n",
|
||
"\n",
|
||
" grid_search_XGB = GridSearchCV(\n",
|
||
" estimator=estimator,\n",
|
||
" param_grid=parameters,\n",
|
||
" # n_jobs = 10,\n",
|
||
" cv = 3,\n",
|
||
" verbose=True\n",
|
||
" )\n",
|
||
"\n",
|
||
" grid_search_XGB.fit(X_train, y_train)\n",
|
||
" #如果电脑在此步骤报错，可能是因为计算量太大，超过硬件可支持程度，可注释掉“n_jobs=10”一行\n",
|
||
"\n",
|
||
" best_parameters = grid_search_XGB.best_estimator_.get_params()\n",
|
||
" y_pred = grid_search_XGB.predict(x_test)\n",
|
||
"\n",
|
||
" op_XGBR_score = r2_score(y_true,y_pred)\n",
|
||
" op_XGBR_MSE= mean_squared_error(y_true, y_pred)\n",
|
||
" op_XGBR_RMSE= np.sqrt(op_XGBR_MSE)\n",
|
||
"\n",
|
||
" model_results2 = pd.DataFrame([['Optimized_Xgboost', op_XGBR_RMSE, op_XGBR_score]],\n",
|
||
" columns = ['模型(Model)', '均方根误差(RMSE)', 'R^2 score'])\n",
|
||
" model_results2=model_results2.set_index('模型(Model)')\n",
|
||
"\n",
|
||
" try:\n",
|
||
" results = model_results1.append(model_results2, ignore_index = False)\n",
|
||
" except:\n",
|
||
" results = pd.concat([model_results1,model_results2],ignore_index=True)\n",
|
||
" import pickle\n",
|
||
"\n",
|
||
" Pkl_Filename = \"日度价格预测_液化气最佳模型.pkl\" \n",
|
||
"\n",
|
||
" with open(Pkl_Filename, 'wb') as file: \n",
|
||
" pickle.dump(grid_search_XGB, file)\n",
|
||
"\n",
|
||
"\n",
|
||
" \n",
|
||
"\n",
|
||
"def read_xls_data_bak():\n",
|
||
" global one_cols, two_cols\n",
|
||
" # 打开 XLS 文件\n",
|
||
" workbook = xlrd.open_workbook(read_file_path_name)\n",
|
||
"\n",
|
||
" # 获取所有表格名称\n",
|
||
" # sheet_names = workbook.sheet_names()\n",
|
||
"\n",
|
||
" # 选择第一个表格\n",
|
||
" sheet = workbook.sheet_by_index(0)\n",
|
||
"\n",
|
||
" # 获取行数和列数\n",
|
||
" num_rows = sheet.nrows\n",
|
||
" # num_cols = sheet.ncols\n",
|
||
"\n",
|
||
" # 遍历每一行，获取单元格数据\n",
|
||
" # for i in range(num_rows):\n",
|
||
" # row_data = sheet.row_values(i)\n",
|
||
" # one_cols.append(row_data)\n",
|
||
" # two_cols.append(row_data[1])\n",
|
||
"\n",
|
||
" row_data = sheet.row_values(1)\n",
|
||
" one_cols = row_data\n",
|
||
"\n",
|
||
" # 关闭 XLS 文件\n",
|
||
" # workbook.close()\n",
|
||
"\n",
|
||
"def read_xls_data():\n",
|
||
" global one_cols, two_cols\n",
|
||
" # 使用pandas读取Excel文件\n",
|
||
" df = pd.read_excel(read_file_path_name, header=None) # 不自动识别列名\n",
|
||
" # 获取第二行数据（索引为1）\n",
|
||
" one_cols = df.iloc[1].tolist()[1:]\n",
|
||
" print(f'获取到的数据项ID{one_cols}')\n",
|
||
"\n",
|
||
"def start(date=''):\n",
|
||
" read_xls_data()\n",
|
||
"\n",
|
||
" token = get_head_auth()\n",
|
||
" if not token:\n",
|
||
" return\n",
|
||
" token_push = get_head_push_auth()\n",
|
||
" if not token_push:\n",
|
||
" return\n",
|
||
" cur_time,cur_time2 = getNow(date)\n",
|
||
" datas = get_data_value(token, one_cols[1:],cur_time)\n",
|
||
"# if not datas:\n",
|
||
"# return\n",
|
||
"\n",
|
||
" # data_list = [two_cols, one_cols]\n",
|
||
" append_rows = [cur_time2]\n",
|
||
" dataItemNo_dataValue = {}\n",
|
||
" for data_value in datas:\n",
|
||
" if \"dataValue\" not in data_value:\n",
|
||
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
|
||
" else:\n",
|
||
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
|
||
" \n",
|
||
" for value in one_cols[1:]:\n",
|
||
" if value in dataItemNo_dataValue:\n",
|
||
" append_rows.append(dataItemNo_dataValue[value])\n",
|
||
" else:\n",
|
||
" append_rows.append(\"\")\n",
|
||
" save_xls(append_rows)\n",
|
||
" # optimize_Model()\n",
|
||
" # upload_data_to_system(token_push,cur_time)\n",
|
||
" \n",
|
||
" \n",
|
||
" \n",
|
||
" # data_list.append(three_cols)\n",
|
||
" # write_xls(data_list)\n",
|
||
"\n",
|
||
"def getNow(date='',offset=0):\n",
|
||
" \n",
|
||
" if date == '':\n",
|
||
" now = datetime.now() - timedelta(days=offset)\n",
|
||
" else:\n",
|
||
" if isinstance(date, datetime):\n",
|
||
" now = date\n",
|
||
" else:\n",
|
||
" try:\n",
|
||
" # 先尝试常见日期格式解析\n",
|
||
" now = datetime.strptime(str(date), \"%Y-%m-%d\")\n",
|
||
" except ValueError:\n",
|
||
" # 失败后尝试无分隔符格式\n",
|
||
" now = datetime.strptime(str(date), \"%Y%m%d\")\n",
|
||
" print(now,type(now))\n",
|
||
" now = now - timedelta(days=offset)\n",
|
||
" year = now.year\n",
|
||
" month = now.month\n",
|
||
" day = now.day\n",
|
||
"\n",
|
||
" if month < 10:\n",
|
||
" month = \"0\" + str(month)\n",
|
||
" if day < 10:\n",
|
||
" day = \"0\" + str(day)\n",
|
||
" cur_time = str(year) + str(month) + str(day)\n",
|
||
" cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
|
||
" return cur_time,cur_time2\n",
|
||
" \n",
|
||
"def start_1(date=''):\n",
|
||
" read_xls_data()\n",
|
||
" token = get_head_auth()\n",
|
||
" if not token:\n",
|
||
" return\n",
|
||
" \n",
|
||
" cur_time,cur_time2 = getNow(date,offset=1)\n",
|
||
" print(f\"补充{cur_time}数据\")\n",
|
||
" datas = get_data_value(token, one_cols[1:],date=cur_time)\n",
|
||
"# if not datas:\n",
|
||
"# return\n",
|
||
"\n",
|
||
" # data_list = [two_cols, one_cols]\n",
|
||
" append_rows = [cur_time]\n",
|
||
" dataItemNo_dataValue = {}\n",
|
||
" for data_value in datas:\n",
|
||
" if \"dataValue\" not in data_value:\n",
|
||
" print(data_value)\n",
|
||
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
|
||
" else:\n",
|
||
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
|
||
" \n",
|
||
" for value in one_cols:\n",
|
||
" if value in dataItemNo_dataValue:\n",
|
||
" append_rows.append(dataItemNo_dataValue[value])\n",
|
||
" else:\n",
|
||
" append_rows.append(\"\")\n",
|
||
" print('添加的行：',append_rows)\n",
|
||
" save_xls_2(append_rows)\n",
|
||
"\n",
|
||
" \n",
|
||
" # data_list.append(three_cols)\n",
|
||
" # write_xls(data_list)\n",
|
||
" \n",
|
||
"def save_xls_1(append_rows):\n",
|
||
"\n",
|
||
" # 打开xls文件\n",
|
||
" workbook = xlrd.open_workbook('液化气数据.xls')\n",
|
||
"\n",
|
||
" # 获取所有sheet的个数\n",
|
||
" sheet_count = len(workbook.sheet_names())\n",
|
||
"\n",
|
||
" # 获取所有sheet的名称\n",
|
||
" sheet_names = workbook.sheet_names()\n",
|
||
"\n",
|
||
" new_workbook = xlwt.Workbook()\n",
|
||
" for i in range(sheet_count):\n",
|
||
" # 获取当前sheet\n",
|
||
" sheet = workbook.sheet_by_index(i)\n",
|
||
"\n",
|
||
" # 获取sheet的行数和列数\n",
|
||
" row_count = sheet.nrows - 1\n",
|
||
" col_count = sheet.ncols\n",
|
||
" # 获取原有数据\n",
|
||
" data = []\n",
|
||
" for row in range(row_count):\n",
|
||
" row_data = []\n",
|
||
" for col in range(col_count):\n",
|
||
" row_data.append(sheet.cell_value(row, col))\n",
|
||
" data.append(row_data)\n",
|
||
" # 创建xlwt的Workbook对象\n",
|
||
" # 创建sheet\n",
|
||
" new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
|
||
"\n",
|
||
" # 将原有的数据写入新的sheet\n",
|
||
" for row in range(row_count):\n",
|
||
" for col in range(col_count):\n",
|
||
" new_sheet.write(row, col, data[row][col])\n",
|
||
"\n",
|
||
" if i == 0:\n",
|
||
" # 在新的sheet中添加数据\n",
|
||
" for col in range(col_count):\n",
|
||
" new_sheet.write(row_count, col, append_rows[col])\n",
|
||
"\n",
|
||
" # 保存新的xls文件\n",
|
||
" new_workbook.save(\"液化气数据.xls\") \n",
|
||
"\n",
|
||
"def save_xls_2(append_rows):\n",
|
||
" try:\n",
|
||
" # 读取现有数据（假设第一行为列名）\n",
|
||
" df = pd.read_excel('液化气数据.xls', sheet_name=0)\n",
|
||
" print(df.columns)\n",
|
||
" append_rows = pd.DataFrame(append_rows, index=df.columns)\n",
|
||
" # 创建新数据行\n",
|
||
" new_date = append_rows['Date'].values\n",
|
||
" \n",
|
||
" dates = df['Date'].to_list()\n",
|
||
" \n",
|
||
" # 判断日期是否存在\n",
|
||
" if new_date in dates:\n",
|
||
" # 找到日期所在行的索引\n",
|
||
" date_mask = df['Date'] == new_date\n",
|
||
" # 存在则更新数据\n",
|
||
" df.loc[date_mask] = append_rows\n",
|
||
" print(f\"更新 {new_date} 数据\")\n",
|
||
" else:\n",
|
||
" # 不存在则追加数据\n",
|
||
" df = pd.concat([df, append_rows], ignore_index=True)\n",
|
||
" print(df.head())\n",
|
||
" print(df.tail())\n",
|
||
" print(f\"插入 {new_date} 新数据\")\n",
|
||
" \n",
|
||
" # 保存更新后的数据\n",
|
||
" df.to_excel('液化气数据.xls', index=False, engine='openpyxl')\n",
|
||
" \n",
|
||
" except FileNotFoundError:\n",
|
||
" # 如果文件不存在则创建新文件\n",
|
||
" pd.DataFrame([append_rows]).to_excel('液化气数据.xls', index=False, engine='openpyxl')\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"保存数据时发生错误: {str(e)}\")\n",
|
||
" \n",
|
||
" \n",
|
||
"def check_data(dataItemNo):\n",
|
||
" token = get_head_auth()\n",
|
||
" if not token:\n",
|
||
" return\n",
|
||
"\n",
|
||
" datas = get_data_value(token, dataItemNo)\n",
|
||
" if not datas:\n",
|
||
" return\n",
|
||
"\n",
|
||
"\n",
|
||
"def save_xls(append_rows):\n",
|
||
"\n",
|
||
" # 打开xls文件\n",
|
||
" workbook = xlrd.open_workbook('液化气数据.xls')\n",
|
||
"\n",
|
||
" # 获取所有sheet的个数\n",
|
||
" sheet_count = len(workbook.sheet_names())\n",
|
||
"\n",
|
||
" # 获取所有sheet的名称\n",
|
||
" sheet_names = workbook.sheet_names()\n",
|
||
"\n",
|
||
" new_workbook = xlwt.Workbook()\n",
|
||
" for i in range(sheet_count):\n",
|
||
" # 获取当前sheet\n",
|
||
" sheet = workbook.sheet_by_index(i)\n",
|
||
"\n",
|
||
" # 获取sheet的行数和列数\n",
|
||
" row_count = sheet.nrows\n",
|
||
" col_count = sheet.ncols\n",
|
||
" # 获取原有数据\n",
|
||
" data = []\n",
|
||
" for row in range(row_count):\n",
|
||
" row_data = []\n",
|
||
" for col in range(col_count):\n",
|
||
" row_data.append(sheet.cell_value(row, col))\n",
|
||
" data.append(row_data)\n",
|
||
" # 创建xlwt的Workbook对象\n",
|
||
" # 创建sheet\n",
|
||
" new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
|
||
"\n",
|
||
" # 将原有的数据写入新的sheet\n",
|
||
" for row in range(row_count):\n",
|
||
" for col in range(col_count):\n",
|
||
" new_sheet.write(row, col, data[row][col])\n",
|
||
"\n",
|
||
" if i == 0:\n",
|
||
" # 在新的sheet中添加数据\n",
|
||
" for col in range(col_count):\n",
|
||
" new_sheet.write(row_count, col, append_rows[col])\n",
|
||
"\n",
|
||
" # 保存新的xls文件\n",
|
||
" new_workbook.save(\"液化气数据.xls\")\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"# if __name__ == \"__main__\":\n",
|
||
"# print('运行中')\n",
|
||
"# # 需要单独运行放开\n",
|
||
"# # start()\n",
|
||
"# # start_1(date='2025-01-22')\n",
|
||
"# # start_1()\n",
|
||
"\n",
|
||
"# # 每天定时12点运行\n",
|
||
"# while True:\n",
|
||
"# try:\n",
|
||
"# # 获取当前时间\n",
|
||
"# current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
|
||
"# current_time_1 = time.strftime(\"%H:%M:%S\", time.localtime())\n",
|
||
"# # print(current_time_1)\n",
|
||
"\n",
|
||
"# # 判断当前时间是否为执行任务的时间点\n",
|
||
"# if current_time == \"09:15:00\":\n",
|
||
"# print(\"执行定时任务\")\n",
|
||
"# start()\n",
|
||
"\n",
|
||
"# # 休眠1秒钟，避免过多占用CPU资源\n",
|
||
"# time.sleep(1)\n",
|
||
"\n",
|
||
"# elif current_time_1 == \"20:00:00\":\n",
|
||
"# print(\"更新数据\")\n",
|
||
"# start_1()\n",
|
||
"# time.sleep(1)\n",
|
||
"# except:\n",
|
||
"# print('执行错误')\n",
|
||
"# time.sleep(1)\n",
|
||
"\n",
|
||
"\n",
|
||
"# # 检测数据准确性, 需要检测放开\n",
|
||
"# # check_data(\"100028098|LISTING_PRICE\")\n",
|
||
"# # check_data(\"9137070016544622XB|DAY_Yield\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"获取到的数据项ID['YHQMXBB|C01100008|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']\n",
|
||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0MjMzMDEyMSwianRpIjoiMmI5ZmUzNTA5YjNmNGU4OTkzMjRiNzU1MzQ4ODlkNTQifQ.nezcKMQq4GnNoHKwvIOEe-1pK0Oz3LliiM8yYjOMG8c\n",
|
||
"补充20250228数据\n",
|
||
"数据项查询参数search_data：\n",
|
||
"{'data': {'date': '20250228', 'dataItemNoList': ['C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|AMOUNT', 'ICE_CL0_LAST_YEDAY_PRICE', '100028046|LISTING_PRICE', 'C01100008|PLAN_SALE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
|
||
"数据项查询结果search_res：\n",
|
||
"{\"confirmFlg\":false,\"data\":[{\"dataDate\":\"20250228\",\"dataItemNo\":\"100028046|LISTING_PRICE\",\"dataValue\":8441.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"370500|ISOBUTANE|LIST_PRICE\",\"dataValue\":5380.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91110000710926094P|C01100008|SUPPLY_MERE\",\"dataValue\":1300.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91110000710932515R|C01100008|SUPPLY_MERE\"},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91110304102767480H|C01100008|STRIKE_PRICE\",\"dataValue\":5150.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91130193670310403L|C01100008|STRIKE_PRICE\",\"dataValue\":5150.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"912102117169477344|C01100008|STRIKE_PRICE\",\"dataValue\":4670.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370200163576944B|C01100008|STRIKE_PRICE\",\"dataValue\":5300.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370305773165341A|C01100008|STRIKE_PRICE\",\"dataValue\":5600.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370321164425136B|C01100008|STRIKE_PRICE\",\"dataValue\":5500.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370321164425136B|C01100008|SUPPLY_MERE\",\"dataValue\":200.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370500674526498A|C01100008|STRIKE_PRICE\",\"dataValue\":5488.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370500674526498A|C01100008|SUPPLY_MERE\",\"dataValue\":175.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"91370521164880008P|C01100008|STRIKE_PRICE\",\"dataValue\":5455.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|AMOUNT\",\"dataValue\":342.72000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|AUCTION_MAX_PRICE\",\"dataValue\":5500.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|CORTED_VALUE\",\"dataValue\":5500.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|EFFECTIVE_STOCK\",\"dataValue\":-550.20000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|OTHER|SUPPLY_MERE\",\"dataValue\":5000.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|PLAN_SALE\",\"dataValue\":500.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"C01100008|SUY_DED_DAP\",\"dataValue\":-50.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"HD|LPG|IMPORT_PRICE\",\"dataValue\":5400.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"ICE_CL0_LAST_YEDAY_PRICE\",\"dataValue\":73.35000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"SD|GC|ZDW|LIST_PRICE\",\"dataValue\":5250.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"SD|WJH|DEMANDS\",\"dataValue\":8500.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"SD|WJH|SALES_PRICE\",\"dataValue\":8400.00000000},{\"dataDate\":\"20250228\",\"dataItemNo\":\"SD|YT|SG|LIST_PRICE\",\"dataValue\":6050.00000000}],\"status\":true}\n",
|
||
"数据项查询结果: [{'dataDate': '20250228', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8441.0}, {'dataDate': '20250228', 'dataItemNo': '370500|ISOBUTANE|LIST_PRICE', 'dataValue': 5380.0}, {'dataDate': '20250228', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250228', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250228', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5150.0}, {'dataDate': '20250228', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 5150.0}, {'dataDate': '20250228', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4670.0}, {'dataDate': '20250228', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5300.0}, {'dataDate': '20250228', 'dataItemNo': '91370305773165341A|C01100008|STRIKE_PRICE', 'dataValue': 5600.0}, {'dataDate': '20250228', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250228', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 200.0}, {'dataDate': '20250228', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5488.0}, {'dataDate': '20250228', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250228', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5455.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|AMOUNT', 'dataValue': 342.72}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5500.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5500.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': -550.2}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|PLAN_SALE', 'dataValue': 500.0}, {'dataDate': '20250228', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250228', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5400.0}, {'dataDate': '20250228', 'dataItemNo': 'ICE_CL0_LAST_YEDAY_PRICE', 'dataValue': 73.35}, {'dataDate': '20250228', 'dataItemNo': 'SD|GC|ZDW|LIST_PRICE', 'dataValue': 5250.0}, {'dataDate': '20250228', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250228', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 8400.0}, {'dataDate': '20250228', 'dataItemNo': 'SD|YT|SG|LIST_PRICE', 'dataValue': 6050.0}]\n",
|
||
"{'dataDate': '20250228', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
|
||
"添加的行： ['20250228', '', 5500.0, 5500.0, 342.72, 73.35, 8441.0, 500.0, 5300.0, '', 5488.0, 5600.0, 5455.0, 5500.0, 5250.0, 5380.0, 6050.0, 1300.0, '', 175.0, 200.0, 5000.0, 8500.0, -50.0, -550.2, 4670.0, 5150.0, 5150.0, 5400.0, 8400.0]\n",
|
||
"Index(['Date', 'Price', '修正价', '竞拍最高价', '液化石油气|发货量', '昨日布伦特价格', '昨日92#汽油价格',\n",
|
||
" '计划出货量', '青岛石化', '中化工-昌邑', '海科瑞林', '鑫泰石化|液化石油气|成交价', '垦利价格', '汇丰价格',\n",
|
||
" '正丁烷', '异丁烷价格', '顺酐', '中石化供应量', '中化工供应量', '海科供应量', '汇丰供应量', '京博和其他供应量',\n",
|
||
" '烷基化需求量', '昨日烷基化价差', '我司库存', '东北-大连石化', '华北-燕山石化', '华北-石家庄炼化',\n",
|
||
" '昨日原料气价格', '烷基化油销售价格'],\n",
|
||
" dtype='object')\n",
|
||
"保存数据时发生错误: 'Date'\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"start_date = datetime(2025, 3, 1)\n",
|
||
"end_date = datetime(2025, 3, 2)\n",
|
||
"\n",
|
||
"while start_date < end_date:\n",
|
||
" date = start_date.strftime('%Y%m%d')\n",
|
||
" # start(date)\n",
|
||
" # time.sleep(1)\n",
|
||
" start_1(start_date)\n",
|
||
" start_date += timedelta(days=1)\n",
|
||
" time.sleep(5)\n",
|
||
"\n",
|
||
"# print(price_list)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "base",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.11.7"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|