{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-29T10:09:22.171634Z",
     "start_time": "2026-01-29T08:27:03.148937Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import urllib.parse\n",
    "\n",
    "import pandas as pd\n",
    "import sqlalchemy\n",
    "from sqlalchemy.orm import DeclarativeBase, Session\n",
    "\n",
    "postgresql_engin = sqlalchemy.create_engine(\n",
    "    f\"postgresql://leopard:{urllib.parse.quote_plus(\"9NEzFzovnddf@PyEP?e*AYAWnCyd7UhYwQK$pJf>7?ccFiN^x4$eKEZ5~E<7<+~X\")}@81.71.3.24:6785/leopard\"\n",
    ")\n",
    "sqlite_engine = sqlalchemy.create_engine(f\"sqlite:////Users/lanyuanxiaoyao/Documents/leopard_data/leopard.sqlite\")\n",
    "\n",
    "\n",
    "class Base(DeclarativeBase):\n",
    "    pass\n",
    "\n",
    "\n",
    "class Daily(Base):\n",
    "    __tablename__ = 'daily'\n",
    "\n",
    "    code = sqlalchemy.Column(sqlalchemy.String, primary_key=True)\n",
    "    trade_date = sqlalchemy.Column(sqlalchemy.Date)\n",
    "    open = sqlalchemy.Column(sqlalchemy.Double)\n",
    "    close = sqlalchemy.Column(sqlalchemy.Double)\n",
    "    high = sqlalchemy.Column(sqlalchemy.Double)\n",
    "    low = sqlalchemy.Column(sqlalchemy.Double)\n",
    "    previous_close = sqlalchemy.Column(sqlalchemy.Double)\n",
    "    turnover = sqlalchemy.Column(sqlalchemy.Double)\n",
    "    volume = sqlalchemy.Column(sqlalchemy.Integer)\n",
    "    price_change_amount = sqlalchemy.Column(sqlalchemy.Double)\n",
    "    factor = sqlalchemy.Column(sqlalchemy.Double)\n",
    "\n",
    "\n",
    "try:\n",
    "    stock_df = pd.read_sql_table(\"stock\", sqlite_engine)\n",
    "    for index, code in enumerate(stock_df[\"code\"]):\n",
    "        print(code)\n",
    "        daily_df = pd.read_sql(\n",
    "            f\"\"\"\n",
    "               select code,\n",
    "                      trade_date,\n",
    "                      open,\n",
    "                      close,\n",
    "                      high,\n",
    "                      low,\n",
    "                      previous_close,\n",
    "                      turnover,\n",
    "                      volume,\n",
    "                      price_change_amount,\n",
    "                      factor\n",
    "               from leopard_daily d\n",
    "                        left join leopard_stock s on d.stock_id = s.id\n",
    "               where s.code = '{code}'\n",
    "           \"\"\",\n",
    "            postgresql_engin\n",
    "        )\n",
    "        with Session(sqlite_engine) as session:\n",
    "            for _, row in daily_df.iterrows():\n",
    "                session.add(\n",
    "                    Daily(\n",
    "                        code=row[\"code\"],\n",
    "                        trade_date=row[\"trade_date\"],\n",
    "                        open=row[\"open\"],\n",
    "                        close=row[\"close\"],\n",
    "                        high=row[\"high\"],\n",
    "                        low=row[\"low\"],\n",
    "                        previous_close=row[\"previous_close\"],\n",
    "                        turnover=row[\"turnover\"],\n",
    "                        volume=row[\"volume\"],\n",
    "                        price_change_amount=row[\"price_change_amount\"],\n",
    "                        factor=row[\"factor\"]\n",
    "                    )\n",
    "                )\n",
    "            session.flush()\n",
    "            session.commit()\n",
    "finally:\n",
    "    postgresql_engin.dispose()\n",
    "    sqlite_engine.dispose()"
   ],
   "id": "48821306efc640a1",
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
      "\u001B[31mKeyboardInterrupt\u001B[39m                         Traceback (most recent call last)",
      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[26]\u001B[39m\u001B[32m, line 37\u001B[39m\n\u001B[32m     35\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m index, code \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(stock_df[\u001B[33m\"\u001B[39m\u001B[33mcode\u001B[39m\u001B[33m\"\u001B[39m]):\n\u001B[32m     36\u001B[39m     \u001B[38;5;28mprint\u001B[39m(code)\n\u001B[32m---> \u001B[39m\u001B[32m37\u001B[39m     daily_df = \u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mread_sql\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m     38\u001B[39m \u001B[43m        \u001B[49m\u001B[33;43mf\u001B[39;49m\u001B[33;43m\"\"\"\u001B[39;49m\n\u001B[32m     39\u001B[39m \u001B[33;43m           select code,\u001B[39;49m\n\u001B[32m     40\u001B[39m \u001B[33;43m                  trade_date,\u001B[39;49m\n\u001B[32m     41\u001B[39m \u001B[33;43m                  open,\u001B[39;49m\n\u001B[32m     42\u001B[39m \u001B[33;43m                  close,\u001B[39;49m\n\u001B[32m     43\u001B[39m \u001B[33;43m                  high,\u001B[39;49m\n\u001B[32m     44\u001B[39m \u001B[33;43m                  low,\u001B[39;49m\n\u001B[32m     45\u001B[39m \u001B[33;43m                  previous_close,\u001B[39;49m\n\u001B[32m     46\u001B[39m \u001B[33;43m                  turnover,\u001B[39;49m\n\u001B[32m     47\u001B[39m \u001B[33;43m                  volume,\u001B[39;49m\n\u001B[32m     48\u001B[39m \u001B[33;43m                  price_change_amount,\u001B[39;49m\n\u001B[32m     49\u001B[39m \u001B[33;43m                  factor\u001B[39;49m\n\u001B[32m     50\u001B[39m \u001B[33;43m           from leopard_daily d\u001B[39;49m\n\u001B[32m     51\u001B[39m \u001B[33;43m                    left join leopard_stock s on d.stock_id = s.id\u001B[39;49m\n\u001B[32m     52\u001B[39m \u001B[33;43m           where s.code = \u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[38;5;132;43;01m{\u001B[39;49;00m\u001B[43mcode\u001B[49m\u001B[38;5;132;43;01m}\u001B[39;49;00m\u001B[33;43m'\u001B[39;49m\n\u001B[32m     53\u001B[39m \u001B[33;43m       \u001B[39;49m\u001B[33;43m\"\"\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[32m     54\u001B[39m \u001B[43m        \u001B[49m\u001B[43mpostgresql_engin\u001B[49m\n\u001B[32m     55\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     56\u001B[39m     \u001B[38;5;28;01mwith\u001B[39;00m Session(sqlite_engine) \u001B[38;5;28;01mas\u001B[39;00m session:\n\u001B[32m     57\u001B[39m         \u001B[38;5;28;01mfor\u001B[39;00m _, row \u001B[38;5;129;01min\u001B[39;00m daily_df.iterrows():\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/pandas/io/sql.py:736\u001B[39m, in \u001B[36mread_sql\u001B[39m\u001B[34m(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize, dtype_backend, dtype)\u001B[39m\n\u001B[32m    726\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m pandas_sql.read_table(\n\u001B[32m    727\u001B[39m         sql,\n\u001B[32m    728\u001B[39m         index_col=index_col,\n\u001B[32m   (...)\u001B[39m\u001B[32m    733\u001B[39m         dtype_backend=dtype_backend,\n\u001B[32m    734\u001B[39m     )\n\u001B[32m    735\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m736\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mpandas_sql\u001B[49m\u001B[43m.\u001B[49m\u001B[43mread_query\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m    737\u001B[39m \u001B[43m        \u001B[49m\u001B[43msql\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    738\u001B[39m \u001B[43m        \u001B[49m\u001B[43mindex_col\u001B[49m\u001B[43m=\u001B[49m\u001B[43mindex_col\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    739\u001B[39m \u001B[43m        \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m=\u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    740\u001B[39m \u001B[43m        \u001B[49m\u001B[43mcoerce_float\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcoerce_float\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    741\u001B[39m \u001B[43m        \u001B[49m\u001B[43mparse_dates\u001B[49m\u001B[43m=\u001B[49m\u001B[43mparse_dates\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    742\u001B[39m \u001B[43m        \u001B[49m\u001B[43mchunksize\u001B[49m\u001B[43m=\u001B[49m\u001B[43mchunksize\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    743\u001B[39m \u001B[43m        \u001B[49m\u001B[43mdtype_backend\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdtype_backend\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    744\u001B[39m \u001B[43m        \u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m    745\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/pandas/io/sql.py:1848\u001B[39m, in \u001B[36mSQLDatabase.read_query\u001B[39m\u001B[34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize, dtype, dtype_backend)\u001B[39m\n\u001B[32m   1791\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mread_query\u001B[39m(\n\u001B[32m   1792\u001B[39m     \u001B[38;5;28mself\u001B[39m,\n\u001B[32m   1793\u001B[39m     sql: \u001B[38;5;28mstr\u001B[39m,\n\u001B[32m   (...)\u001B[39m\u001B[32m   1800\u001B[39m     dtype_backend: DtypeBackend | Literal[\u001B[33m\"\u001B[39m\u001B[33mnumpy\u001B[39m\u001B[33m\"\u001B[39m] = \u001B[33m\"\u001B[39m\u001B[33mnumpy\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m   1801\u001B[39m ) -> DataFrame | Iterator[DataFrame]:\n\u001B[32m   1802\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   1803\u001B[39m \u001B[33;03m    Read SQL query into a DataFrame.\u001B[39;00m\n\u001B[32m   1804\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m   1846\u001B[39m \n\u001B[32m   1847\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1848\u001B[39m     result = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43msql\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1849\u001B[39m     columns = result.keys()\n\u001B[32m   1851\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m chunksize \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/pandas/io/sql.py:1671\u001B[39m, in \u001B[36mSQLDatabase.execute\u001B[39m\u001B[34m(self, sql, params)\u001B[39m\n\u001B[32m   1669\u001B[39m args = [] \u001B[38;5;28;01mif\u001B[39;00m params \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m [params]\n\u001B[32m   1670\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(sql, \u001B[38;5;28mstr\u001B[39m):\n\u001B[32m-> \u001B[39m\u001B[32m1671\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mcon\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexec_driver_sql\u001B[49m\u001B[43m(\u001B[49m\u001B[43msql\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1672\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.con.execute(sql, *args)\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/sqlalchemy/engine/base.py:1779\u001B[39m, in \u001B[36mConnection.exec_driver_sql\u001B[39m\u001B[34m(self, statement, parameters, execution_options)\u001B[39m\n\u001B[32m   1774\u001B[39m execution_options = \u001B[38;5;28mself\u001B[39m._execution_options.merge_with(\n\u001B[32m   1775\u001B[39m     execution_options\n\u001B[32m   1776\u001B[39m )\n\u001B[32m   1778\u001B[39m dialect = \u001B[38;5;28mself\u001B[39m.dialect\n\u001B[32m-> \u001B[39m\u001B[32m1779\u001B[39m ret = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_execute_context\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   1780\u001B[39m \u001B[43m    \u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1781\u001B[39m \u001B[43m    \u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecution_ctx_cls\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_init_statement\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1782\u001B[39m \u001B[43m    \u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1783\u001B[39m \u001B[43m    \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[32m   1784\u001B[39m \u001B[43m    \u001B[49m\u001B[43mexecution_options\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1785\u001B[39m \u001B[43m    \u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1786\u001B[39m \u001B[43m    \u001B[49m\u001B[43mdistilled_parameters\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   1787\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1789\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m ret\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/sqlalchemy/engine/base.py:1846\u001B[39m, in \u001B[36mConnection._execute_context\u001B[39m\u001B[34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001B[39m\n\u001B[32m   1844\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._exec_insertmany_context(dialect, context)\n\u001B[32m   1845\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1846\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_exec_single_context\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   1847\u001B[39m \u001B[43m        \u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\n\u001B[32m   1848\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/sqlalchemy/engine/base.py:1986\u001B[39m, in \u001B[36mConnection._exec_single_context\u001B[39m\u001B[34m(self, dialect, context, statement, parameters)\u001B[39m\n\u001B[32m   1983\u001B[39m     result = context._setup_result_proxy()\n\u001B[32m   1985\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[32m-> \u001B[39m\u001B[32m1986\u001B[39m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_handle_dbapi_exception\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   1987\u001B[39m \u001B[43m        \u001B[49m\u001B[43me\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstr_statement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43meffective_parameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcursor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\n\u001B[32m   1988\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1990\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m result\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/sqlalchemy/engine/base.py:2366\u001B[39m, in \u001B[36mConnection._handle_dbapi_exception\u001B[39m\u001B[34m(self, e, statement, parameters, cursor, context, is_sub_exec)\u001B[39m\n\u001B[32m   2364\u001B[39m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m   2365\u001B[39m         \u001B[38;5;28;01massert\u001B[39;00m exc_info[\u001B[32m1\u001B[39m] \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m2366\u001B[39m         \u001B[38;5;28;01mraise\u001B[39;00m exc_info[\u001B[32m1\u001B[39m].with_traceback(exc_info[\u001B[32m2\u001B[39m])\n\u001B[32m   2367\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m   2368\u001B[39m     \u001B[38;5;28;01mdel\u001B[39;00m \u001B[38;5;28mself\u001B[39m._reentrant_error\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/sqlalchemy/engine/base.py:1967\u001B[39m, in \u001B[36mConnection._exec_single_context\u001B[39m\u001B[34m(self, dialect, context, statement, parameters)\u001B[39m\n\u001B[32m   1965\u001B[39m                 \u001B[38;5;28;01mbreak\u001B[39;00m\n\u001B[32m   1966\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m evt_handled:\n\u001B[32m-> \u001B[39m\u001B[32m1967\u001B[39m         \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mdialect\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdo_execute\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   1968\u001B[39m \u001B[43m            \u001B[49m\u001B[43mcursor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstr_statement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43meffective_parameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcontext\u001B[49m\n\u001B[32m   1969\u001B[39m \u001B[43m        \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1971\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m._has_events \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m.engine._has_events:\n\u001B[32m   1972\u001B[39m     \u001B[38;5;28mself\u001B[39m.dispatch.after_cursor_execute(\n\u001B[32m   1973\u001B[39m         \u001B[38;5;28mself\u001B[39m,\n\u001B[32m   1974\u001B[39m         cursor,\n\u001B[32m   (...)\u001B[39m\u001B[32m   1978\u001B[39m         context.executemany,\n\u001B[32m   1979\u001B[39m     )\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~/Project/leopard_analysis/.venv/lib/python3.14/site-packages/sqlalchemy/engine/default.py:952\u001B[39m, in \u001B[36mDefaultDialect.do_execute\u001B[39m\u001B[34m(self, cursor, statement, parameters, context)\u001B[39m\n\u001B[32m    951\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mdo_execute\u001B[39m(\u001B[38;5;28mself\u001B[39m, cursor, statement, parameters, context=\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[32m--> \u001B[39m\u001B[32m952\u001B[39m     \u001B[43mcursor\u001B[49m\u001B[43m.\u001B[49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstatement\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\n",
      "\u001B[36mFile \u001B[39m\u001B[32m~/.local/share/uv/python/cpython-3.14.2-macos-x86_64-none/lib/python3.14/encodings/utf_8.py:15\u001B[39m, in \u001B[36mdecode\u001B[39m\u001B[34m(input, errors)\u001B[39m\n\u001B[32m     11\u001B[39m \u001B[38;5;66;03m### Codec APIs\u001B[39;00m\n\u001B[32m     13\u001B[39m encode = codecs.utf_8_encode\n\u001B[32m---> \u001B[39m\u001B[32m15\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mdecode\u001B[39m(\u001B[38;5;28minput\u001B[39m, errors=\u001B[33m'\u001B[39m\u001B[33mstrict\u001B[39m\u001B[33m'\u001B[39m):\n\u001B[32m     16\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m codecs.utf_8_decode(\u001B[38;5;28minput\u001B[39m, errors, \u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m     18\u001B[39m \u001B[38;5;28;01mclass\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mIncrementalEncoder\u001B[39;00m(codecs.IncrementalEncoder):\n",
      "\u001B[31mKeyboardInterrupt\u001B[39m: "
     ]
    }
   ],
   "execution_count": 26
  }
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