Python to SingleStore Connection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f86ffcfb-3f9c-4103-b626-ee000b4f044b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sqlalchemy\n",
    "import pymysql\n",
    "import plotly.express as px\n",
    "from pandas.io import sql\n",
    "from sqlalchemy import Column, VARCHAR\n",
    "from sqlalchemy.ext.declarative import declarative_base\n",
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "abfedb01-e894-47a7-8c9b-539a1899982c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I set the username, password and database name as variables\n",
    "UserName='Your user name'\n",
    "Password='Your Password'\n",
    "DatabaseName='Your database'\n",
    "URL='Your URL'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3bf45296-9c12-4973-86d7-c7f45082b0dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the database connection\n",
    "db_connection_str = \"mysql+pymysql://\"+UserName+ \":\" +Password +\"@\"+URL+\"/\"+ DatabaseName\n",
    "db_connection = create_engine(db_connection_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a5dca2d-6cdb-4eb5-bc11-a39124f71827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1952</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>2.449008e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1952</td>\n",
       "      <td>lifeExp</td>\n",
       "      <td>4.307700e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1952</td>\n",
       "      <td>pop</td>\n",
       "      <td>9.279525e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1957</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>3.013976e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1957</td>\n",
       "      <td>lifeExp</td>\n",
       "      <td>4.568500e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2002</td>\n",
       "      <td>lifeExp</td>\n",
       "      <td>7.911000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2002</td>\n",
       "      <td>pop</td>\n",
       "      <td>3.908037e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2007</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>2.518501e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2007</td>\n",
       "      <td>lifeExp</td>\n",
       "      <td>8.020400e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5111</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2007</td>\n",
       "      <td>pop</td>\n",
       "      <td>4.115771e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5112 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year     metric         value\n",
       "0         Algeria    Africa  1952  gdpPercap  2.449008e+03\n",
       "1         Algeria    Africa  1952    lifeExp  4.307700e+01\n",
       "2         Algeria    Africa  1952        pop  9.279525e+06\n",
       "3         Algeria    Africa  1957  gdpPercap  3.013976e+03\n",
       "4         Algeria    Africa  1957    lifeExp  4.568500e+01\n",
       "...           ...       ...   ...        ...           ...\n",
       "5107  New Zealand   Oceania  2002    lifeExp  7.911000e+01\n",
       "5108  New Zealand   Oceania  2002        pop  3.908037e+06\n",
       "5109  New Zealand   Oceania  2007  gdpPercap  2.518501e+04\n",
       "5110  New Zealand   Oceania  2007    lifeExp  8.020400e+01\n",
       "5111  New Zealand   Oceania  2007        pop  4.115771e+06\n",
       "\n",
       "[5112 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data = pd.read_csv(\"gapminder_tidy.csv\")\n",
    "df_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "07dbff68-ca1a-4eda-8372-c47500a785b7",
   "metadata": {},
   "outputs": [
    {
     "ename": "OperationalError",
     "evalue": "(pymysql.err.OperationalError) (2003, \"Can't connect to MySQL server on 'Your URL' ([Errno 8] nodename nor servname provided, or not known)\")\n(Background on this error at: https://sqlalche.me/e/14/e3q8)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/pymysql/connections.py:613\u001b[0m, in \u001b[0;36mConnection.connect\u001b[0;34m(self, sock)\u001b[0m\n\u001b[1;32m    612\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 613\u001b[0m     sock \u001b[39m=\u001b[39m socket\u001b[39m.\u001b[39;49mcreate_connection(\n\u001b[1;32m    614\u001b[0m         (\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhost, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mport), \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnect_timeout, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m    615\u001b[0m     )\n\u001b[1;32m    616\u001b[0m     \u001b[39mbreak\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/socket.py:824\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m    823\u001b[0m err \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m--> 824\u001b[0m \u001b[39mfor\u001b[39;00m res \u001b[39min\u001b[39;00m getaddrinfo(host, port, \u001b[39m0\u001b[39;49m, SOCK_STREAM):\n\u001b[1;32m    825\u001b[0m     af, socktype, proto, canonname, sa \u001b[39m=\u001b[39m res\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/socket.py:955\u001b[0m, in \u001b[0;36mgetaddrinfo\u001b[0;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[1;32m    954\u001b[0m addrlist \u001b[39m=\u001b[39m []\n\u001b[0;32m--> 955\u001b[0m \u001b[39mfor\u001b[39;00m res \u001b[39min\u001b[39;00m _socket\u001b[39m.\u001b[39;49mgetaddrinfo(host, port, family, \u001b[39mtype\u001b[39;49m, proto, flags):\n\u001b[1;32m    956\u001b[0m     af, socktype, proto, canonname, sa \u001b[39m=\u001b[39m res\n",
      "\u001b[0;31mgaierror\u001b[0m: [Errno 8] nodename nor servname provided, or not known",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/base.py:3280\u001b[0m, in \u001b[0;36mEngine._wrap_pool_connect\u001b[0;34m(self, fn, connection)\u001b[0m\n\u001b[1;32m   3279\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3280\u001b[0m     \u001b[39mreturn\u001b[39;00m fn()\n\u001b[1;32m   3281\u001b[0m \u001b[39mexcept\u001b[39;00m dialect\u001b[39m.\u001b[39mdbapi\u001b[39m.\u001b[39mError \u001b[39mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:310\u001b[0m, in \u001b[0;36mPool.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[39m\"\"\"Return a DBAPI connection from the pool.\u001b[39;00m\n\u001b[1;32m    304\u001b[0m \n\u001b[1;32m    305\u001b[0m \u001b[39mThe connection is instrumented such that when its\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    308\u001b[0m \n\u001b[1;32m    309\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 310\u001b[0m \u001b[39mreturn\u001b[39;00m _ConnectionFairy\u001b[39m.\u001b[39;49m_checkout(\u001b[39mself\u001b[39;49m)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:868\u001b[0m, in \u001b[0;36m_ConnectionFairy._checkout\u001b[0;34m(cls, pool, threadconns, fairy)\u001b[0m\n\u001b[1;32m    867\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m fairy:\n\u001b[0;32m--> 868\u001b[0m     fairy \u001b[39m=\u001b[39m _ConnectionRecord\u001b[39m.\u001b[39;49mcheckout(pool)\n\u001b[1;32m    870\u001b[0m     fairy\u001b[39m.\u001b[39m_pool \u001b[39m=\u001b[39m pool\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:476\u001b[0m, in \u001b[0;36m_ConnectionRecord.checkout\u001b[0;34m(cls, pool)\u001b[0m\n\u001b[1;32m    474\u001b[0m \u001b[39m@classmethod\u001b[39m\n\u001b[1;32m    475\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcheckout\u001b[39m(\u001b[39mcls\u001b[39m, pool):\n\u001b[0;32m--> 476\u001b[0m     rec \u001b[39m=\u001b[39m pool\u001b[39m.\u001b[39;49m_do_get()\n\u001b[1;32m    477\u001b[0m     \u001b[39mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/impl.py:145\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[39mexcept\u001b[39;00m:\n\u001b[0;32m--> 145\u001b[0m     \u001b[39mwith\u001b[39;00m util\u001b[39m.\u001b[39msafe_reraise():\n\u001b[1;32m    146\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dec_overflow()\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py:70\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwarn_only:\n\u001b[0;32m---> 70\u001b[0m         compat\u001b[39m.\u001b[39;49mraise_(\n\u001b[1;32m     71\u001b[0m             exc_value,\n\u001b[1;32m     72\u001b[0m             with_traceback\u001b[39m=\u001b[39;49mexc_tb,\n\u001b[1;32m     73\u001b[0m         )\n\u001b[1;32m     74\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[39mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[39m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[39m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[39m# as the __traceback__ object creates a cycle\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/impl.py:143\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 143\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_create_connection()\n\u001b[1;32m    144\u001b[0m \u001b[39mexcept\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:256\u001b[0m, in \u001b[0;36mPool._create_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[39m\"\"\"Called by subclasses to create a new ConnectionRecord.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 256\u001b[0m \u001b[39mreturn\u001b[39;00m _ConnectionRecord(\u001b[39mself\u001b[39;49m)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:371\u001b[0m, in \u001b[0;36m_ConnectionRecord.__init__\u001b[0;34m(self, pool, connect)\u001b[0m\n\u001b[1;32m    370\u001b[0m \u001b[39mif\u001b[39;00m connect:\n\u001b[0;32m--> 371\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m__connect()\n\u001b[1;32m    372\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfinalize_callback \u001b[39m=\u001b[39m deque()\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:665\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    664\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m--> 665\u001b[0m     \u001b[39mwith\u001b[39;00m util\u001b[39m.\u001b[39msafe_reraise():\n\u001b[1;32m    666\u001b[0m         pool\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mdebug(\u001b[39m\"\u001b[39m\u001b[39mError on connect(): \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m, e)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py:70\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwarn_only:\n\u001b[0;32m---> 70\u001b[0m         compat\u001b[39m.\u001b[39;49mraise_(\n\u001b[1;32m     71\u001b[0m             exc_value,\n\u001b[1;32m     72\u001b[0m             with_traceback\u001b[39m=\u001b[39;49mexc_tb,\n\u001b[1;32m     73\u001b[0m         )\n\u001b[1;32m     74\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[39mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[39m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[39m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[39m# as the __traceback__ object creates a cycle\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:661\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    660\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstarttime \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime()\n\u001b[0;32m--> 661\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdbapi_connection \u001b[39m=\u001b[39m connection \u001b[39m=\u001b[39m pool\u001b[39m.\u001b[39;49m_invoke_creator(\u001b[39mself\u001b[39;49m)\n\u001b[1;32m    662\u001b[0m pool\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mdebug(\u001b[39m\"\u001b[39m\u001b[39mCreated new connection \u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m\"\u001b[39m, connection)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/create.py:590\u001b[0m, in \u001b[0;36mcreate_engine.<locals>.connect\u001b[0;34m(connection_record)\u001b[0m\n\u001b[1;32m    589\u001b[0m             \u001b[39mreturn\u001b[39;00m connection\n\u001b[0;32m--> 590\u001b[0m \u001b[39mreturn\u001b[39;00m dialect\u001b[39m.\u001b[39;49mconnect(\u001b[39m*\u001b[39;49mcargs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcparams)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/default.py:597\u001b[0m, in \u001b[0;36mDefaultDialect.connect\u001b[0;34m(self, *cargs, **cparams)\u001b[0m\n\u001b[1;32m    595\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mconnect\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39mcargs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcparams):\n\u001b[1;32m    596\u001b[0m     \u001b[39m# inherits the docstring from interfaces.Dialect.connect\u001b[39;00m\n\u001b[0;32m--> 597\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdbapi\u001b[39m.\u001b[39;49mconnect(\u001b[39m*\u001b[39;49mcargs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcparams)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/pymysql/connections.py:353\u001b[0m, in \u001b[0;36mConnection.__init__\u001b[0;34m(self, user, password, host, database, unix_socket, port, charset, sql_mode, read_default_file, conv, use_unicode, client_flag, cursorclass, init_command, connect_timeout, read_default_group, autocommit, local_infile, max_allowed_packet, defer_connect, auth_plugin_map, read_timeout, write_timeout, bind_address, binary_prefix, program_name, server_public_key, ssl, ssl_ca, ssl_cert, ssl_disabled, ssl_key, ssl_verify_cert, ssl_verify_identity, compress, named_pipe, passwd, db)\u001b[0m\n\u001b[1;32m    352\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 353\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnect()\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/pymysql/connections.py:664\u001b[0m, in \u001b[0;36mConnection.connect\u001b[0;34m(self, sock)\u001b[0m\n\u001b[1;32m    663\u001b[0m         \u001b[39mprint\u001b[39m(exc\u001b[39m.\u001b[39mtraceback)\n\u001b[0;32m--> 664\u001b[0m     \u001b[39mraise\u001b[39;00m exc\n\u001b[1;32m    666\u001b[0m \u001b[39m# If e is neither DatabaseError or IOError, It's a bug.\u001b[39;00m\n\u001b[1;32m    667\u001b[0m \u001b[39m# But raising AssertionError hides original error.\u001b[39;00m\n\u001b[1;32m    668\u001b[0m \u001b[39m# So just reraise it.\u001b[39;00m\n",
      "\u001b[0;31mOperationalError\u001b[0m: (2003, \"Can't connect to MySQL server on 'Your URL' ([Errno 8] nodename nor servname provided, or not known)\")",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[1;32m/Users/arnaudcomet/Desktop/data_viz_workshop/Data/Python to SingleStore Connection.ipynb Cell 5'\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/arnaudcomet/Desktop/data_viz_workshop/Data/Python%20to%20SingleStore%20Connection.ipynb#ch0000004?line=0'>1</a>\u001b[0m \u001b[39m# Insert whole DataFrame into MySQL\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/arnaudcomet/Desktop/data_viz_workshop/Data/Python%20to%20SingleStore%20Connection.ipynb#ch0000004?line=1'>2</a>\u001b[0m df_data\u001b[39m.\u001b[39;49mto_sql(\u001b[39m'\u001b[39;49m\u001b[39mgapminder_tidy\u001b[39;49m\u001b[39m'\u001b[39;49m, con \u001b[39m=\u001b[39;49m db_connection, if_exists \u001b[39m=\u001b[39;49m \u001b[39m'\u001b[39;49m\u001b[39mappend\u001b[39;49m\u001b[39m'\u001b[39;49m, index \u001b[39m=\u001b[39;49m \u001b[39mFalse\u001b[39;49;00m, chunksize \u001b[39m=\u001b[39;49m \u001b[39m1000\u001b[39;49m, dtype \u001b[39m=\u001b[39;49m{\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/arnaudcomet/Desktop/data_viz_workshop/Data/Python%20to%20SingleStore%20Connection.ipynb#ch0000004?line=2'>3</a>\u001b[0m     \u001b[39m'\u001b[39;49m\u001b[39mcountry\u001b[39;49m\u001b[39m'\u001b[39;49m: sqlalchemy\u001b[39m.\u001b[39;49mtypes\u001b[39m.\u001b[39;49mNVARCHAR(length\u001b[39m=\u001b[39;49m\u001b[39m255\u001b[39;49m),\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/arnaudcomet/Desktop/data_viz_workshop/Data/Python%20to%20SingleStore%20Connection.ipynb#ch0000004?line=3'>4</a>\u001b[0m     \u001b[39m'\u001b[39;49m\u001b[39mcontinent\u001b[39;49m\u001b[39m'\u001b[39;49m: sqlalchemy\u001b[39m.\u001b[39;49mtypes\u001b[39m.\u001b[39;49mNVARCHAR(length\u001b[39m=\u001b[39;49m\u001b[39m255\u001b[39;49m),\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/arnaudcomet/Desktop/data_viz_workshop/Data/Python%20to%20SingleStore%20Connection.ipynb#ch0000004?line=4'>5</a>\u001b[0m     \u001b[39m'\u001b[39;49m\u001b[39myear\u001b[39;49m\u001b[39m'\u001b[39;49m: sqlalchemy\u001b[39m.\u001b[39;49mtypes\u001b[39m.\u001b[39;49mINTEGER(),\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/arnaudcomet/Desktop/data_viz_workshop/Data/Python%20to%20SingleStore%20Connection.ipynb#ch0000004?line=5'>6</a>\u001b[0m     \u001b[39m'\u001b[39;49m\u001b[39mmetric\u001b[39;49m\u001b[39m'\u001b[39;49m: sqlalchemy\u001b[39m.\u001b[39;49mtypes\u001b[39m.\u001b[39;49mNVARCHAR(length\u001b[39m=\u001b[39;49m\u001b[39m255\u001b[39;49m),\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/arnaudcomet/Desktop/data_viz_workshop/Data/Python%20to%20SingleStore%20Connection.ipynb#ch0000004?line=6'>7</a>\u001b[0m     \u001b[39m'\u001b[39;49m\u001b[39mvalue\u001b[39;49m\u001b[39m'\u001b[39;49m: sqlalchemy\u001b[39m.\u001b[39;49mtypes\u001b[39m.\u001b[39;49mFloat(precision\u001b[39m=\u001b[39;49m\u001b[39m3\u001b[39;49m, asdecimal\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)})\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/pandas/core/generic.py:2951\u001b[0m, in \u001b[0;36mNDFrame.to_sql\u001b[0;34m(self, name, con, schema, if_exists, index, index_label, chunksize, dtype, method)\u001b[0m\n\u001b[1;32m   2794\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   2795\u001b[0m \u001b[39mWrite records stored in a DataFrame to a SQL database.\u001b[39;00m\n\u001b[1;32m   2796\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2947\u001b[0m \u001b[39m[(1,), (None,), (2,)]\u001b[39;00m\n\u001b[1;32m   2948\u001b[0m \u001b[39m\"\"\"\u001b[39;00m  \u001b[39m# noqa:E501\u001b[39;00m\n\u001b[1;32m   2949\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mpandas\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mio\u001b[39;00m \u001b[39mimport\u001b[39;00m sql\n\u001b[0;32m-> 2951\u001b[0m \u001b[39mreturn\u001b[39;00m sql\u001b[39m.\u001b[39;49mto_sql(\n\u001b[1;32m   2952\u001b[0m     \u001b[39mself\u001b[39;49m,\n\u001b[1;32m   2953\u001b[0m     name,\n\u001b[1;32m   2954\u001b[0m     con,\n\u001b[1;32m   2955\u001b[0m     schema\u001b[39m=\u001b[39;49mschema,\n\u001b[1;32m   2956\u001b[0m     if_exists\u001b[39m=\u001b[39;49mif_exists,\n\u001b[1;32m   2957\u001b[0m     index\u001b[39m=\u001b[39;49mindex,\n\u001b[1;32m   2958\u001b[0m     index_label\u001b[39m=\u001b[39;49mindex_label,\n\u001b[1;32m   2959\u001b[0m     chunksize\u001b[39m=\u001b[39;49mchunksize,\n\u001b[1;32m   2960\u001b[0m     dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[1;32m   2961\u001b[0m     method\u001b[39m=\u001b[39;49mmethod,\n\u001b[1;32m   2962\u001b[0m )\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/pandas/io/sql.py:697\u001b[0m, in \u001b[0;36mto_sql\u001b[0;34m(frame, name, con, schema, if_exists, index, index_label, chunksize, dtype, method, engine, **engine_kwargs)\u001b[0m\n\u001b[1;32m    692\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(frame, DataFrame):\n\u001b[1;32m    693\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mNotImplementedError\u001b[39;00m(\n\u001b[1;32m    694\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39mframe\u001b[39m\u001b[39m'\u001b[39m\u001b[39m argument should be either a Series or a DataFrame\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    695\u001b[0m     )\n\u001b[0;32m--> 697\u001b[0m \u001b[39mreturn\u001b[39;00m pandas_sql\u001b[39m.\u001b[39;49mto_sql(\n\u001b[1;32m    698\u001b[0m     frame,\n\u001b[1;32m    699\u001b[0m     name,\n\u001b[1;32m    700\u001b[0m     if_exists\u001b[39m=\u001b[39;49mif_exists,\n\u001b[1;32m    701\u001b[0m     index\u001b[39m=\u001b[39;49mindex,\n\u001b[1;32m    702\u001b[0m     index_label\u001b[39m=\u001b[39;49mindex_label,\n\u001b[1;32m    703\u001b[0m     schema\u001b[39m=\u001b[39;49mschema,\n\u001b[1;32m    704\u001b[0m     chunksize\u001b[39m=\u001b[39;49mchunksize,\n\u001b[1;32m    705\u001b[0m     dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[1;32m    706\u001b[0m     method\u001b[39m=\u001b[39;49mmethod,\n\u001b[1;32m    707\u001b[0m     engine\u001b[39m=\u001b[39;49mengine,\n\u001b[1;32m    708\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mengine_kwargs,\n\u001b[1;32m    709\u001b[0m )\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/pandas/io/sql.py:1729\u001b[0m, in \u001b[0;36mSQLDatabase.to_sql\u001b[0;34m(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype, method, engine, **engine_kwargs)\u001b[0m\n\u001b[1;32m   1679\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1680\u001b[0m \u001b[39mWrite records stored in a DataFrame to a SQL database.\u001b[39;00m\n\u001b[1;32m   1681\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1725\u001b[0m \u001b[39m    Any additional kwargs are passed to the engine.\u001b[39;00m\n\u001b[1;32m   1726\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1727\u001b[0m sql_engine \u001b[39m=\u001b[39m get_engine(engine)\n\u001b[0;32m-> 1729\u001b[0m table \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprep_table(\n\u001b[1;32m   1730\u001b[0m     frame\u001b[39m=\u001b[39;49mframe,\n\u001b[1;32m   1731\u001b[0m     name\u001b[39m=\u001b[39;49mname,\n\u001b[1;32m   1732\u001b[0m     if_exists\u001b[39m=\u001b[39;49mif_exists,\n\u001b[1;32m   1733\u001b[0m     index\u001b[39m=\u001b[39;49mindex,\n\u001b[1;32m   1734\u001b[0m     index_label\u001b[39m=\u001b[39;49mindex_label,\n\u001b[1;32m   1735\u001b[0m     schema\u001b[39m=\u001b[39;49mschema,\n\u001b[1;32m   1736\u001b[0m     dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[1;32m   1737\u001b[0m )\n\u001b[1;32m   1739\u001b[0m total_inserted \u001b[39m=\u001b[39m sql_engine\u001b[39m.\u001b[39minsert_records(\n\u001b[1;32m   1740\u001b[0m     table\u001b[39m=\u001b[39mtable,\n\u001b[1;32m   1741\u001b[0m     con\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconnectable,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1748\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mengine_kwargs,\n\u001b[1;32m   1749\u001b[0m )\n\u001b[1;32m   1751\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcheck_case_sensitive(name\u001b[39m=\u001b[39mname, schema\u001b[39m=\u001b[39mschema)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/pandas/io/sql.py:1628\u001b[0m, in \u001b[0;36mSQLDatabase.prep_table\u001b[0;34m(self, frame, name, if_exists, index, index_label, schema, dtype)\u001b[0m\n\u001b[1;32m   1616\u001b[0m             \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mThe type of \u001b[39m\u001b[39m{\u001b[39;00mcol\u001b[39m}\u001b[39;00m\u001b[39m is not a SQLAlchemy type\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   1618\u001b[0m table \u001b[39m=\u001b[39m SQLTable(\n\u001b[1;32m   1619\u001b[0m     name,\n\u001b[1;32m   1620\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1626\u001b[0m     dtype\u001b[39m=\u001b[39mdtype,\n\u001b[1;32m   1627\u001b[0m )\n\u001b[0;32m-> 1628\u001b[0m table\u001b[39m.\u001b[39;49mcreate()\n\u001b[1;32m   1629\u001b[0m \u001b[39mreturn\u001b[39;00m table\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/pandas/io/sql.py:831\u001b[0m, in \u001b[0;36mSQLTable.create\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    830\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcreate\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m--> 831\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mexists():\n\u001b[1;32m    832\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mif_exists \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mfail\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    833\u001b[0m             \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mTable \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m already exists.\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/pandas/io/sql.py:815\u001b[0m, in \u001b[0;36mSQLTable.exists\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    814\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mexists\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m--> 815\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpd_sql\u001b[39m.\u001b[39;49mhas_table(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mname, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mschema)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/pandas/io/sql.py:1762\u001b[0m, in \u001b[0;36mSQLDatabase.has_table\u001b[0;34m(self, name, schema)\u001b[0m\n\u001b[1;32m   1759\u001b[0m \u001b[39mif\u001b[39;00m _gt14():\n\u001b[1;32m   1760\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39msqlalchemy\u001b[39;00m \u001b[39mimport\u001b[39;00m inspect\n\u001b[0;32m-> 1762\u001b[0m     insp \u001b[39m=\u001b[39m inspect(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnectable)\n\u001b[1;32m   1763\u001b[0m     \u001b[39mreturn\u001b[39;00m insp\u001b[39m.\u001b[39mhas_table(name, schema \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmeta\u001b[39m.\u001b[39mschema)\n\u001b[1;32m   1764\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/inspection.py:64\u001b[0m, in \u001b[0;36minspect\u001b[0;34m(subject, raiseerr)\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[39mif\u001b[39;00m reg \u001b[39mis\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m     63\u001b[0m     \u001b[39mreturn\u001b[39;00m subject\n\u001b[0;32m---> 64\u001b[0m ret \u001b[39m=\u001b[39m reg(subject)\n\u001b[1;32m     65\u001b[0m \u001b[39mif\u001b[39;00m ret \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m     66\u001b[0m     \u001b[39mbreak\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/reflection.py:182\u001b[0m, in \u001b[0;36mInspector._engine_insp\u001b[0;34m(bind)\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[39m@inspection\u001b[39m\u001b[39m.\u001b[39m_inspects(Engine)\n\u001b[1;32m    181\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_engine_insp\u001b[39m(bind):\n\u001b[0;32m--> 182\u001b[0m     \u001b[39mreturn\u001b[39;00m Inspector\u001b[39m.\u001b[39;49m_construct(Inspector\u001b[39m.\u001b[39;49m_init_engine, bind)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/reflection.py:117\u001b[0m, in \u001b[0;36mInspector._construct\u001b[0;34m(cls, init, bind)\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[39mcls\u001b[39m \u001b[39m=\u001b[39m bind\u001b[39m.\u001b[39mdialect\u001b[39m.\u001b[39minspector\n\u001b[1;32m    116\u001b[0m \u001b[39mself\u001b[39m \u001b[39m=\u001b[39m \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__new__\u001b[39m(\u001b[39mcls\u001b[39m)\n\u001b[0;32m--> 117\u001b[0m init(\u001b[39mself\u001b[39;49m, bind)\n\u001b[1;32m    118\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/reflection.py:128\u001b[0m, in \u001b[0;36mInspector._init_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_init_engine\u001b[39m(\u001b[39mself\u001b[39m, engine):\n\u001b[1;32m    127\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbind \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mengine \u001b[39m=\u001b[39m engine\n\u001b[0;32m--> 128\u001b[0m     engine\u001b[39m.\u001b[39;49mconnect()\u001b[39m.\u001b[39mclose()\n\u001b[1;32m    129\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_op_context_requires_connect \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    130\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdialect \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mengine\u001b[39m.\u001b[39mdialect\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/base.py:3234\u001b[0m, in \u001b[0;36mEngine.connect\u001b[0;34m(self, close_with_result)\u001b[0m\n\u001b[1;32m   3219\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mconnect\u001b[39m(\u001b[39mself\u001b[39m, close_with_result\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[1;32m   3220\u001b[0m     \u001b[39m\"\"\"Return a new :class:`_engine.Connection` object.\u001b[39;00m\n\u001b[1;32m   3221\u001b[0m \n\u001b[1;32m   3222\u001b[0m \u001b[39m    The :class:`_engine.Connection` object is a facade that uses a DBAPI\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3231\u001b[0m \n\u001b[1;32m   3232\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3234\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_connection_cls(\u001b[39mself\u001b[39;49m, close_with_result\u001b[39m=\u001b[39;49mclose_with_result)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/base.py:96\u001b[0m, in \u001b[0;36mConnection.__init__\u001b[0;34m(self, engine, connection, close_with_result, _branch_from, _execution_options, _dispatch, _has_events, _allow_revalidate)\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_has_events \u001b[39m=\u001b[39m _branch_from\u001b[39m.\u001b[39m_has_events\n\u001b[1;32m     92\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     93\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dbapi_connection \u001b[39m=\u001b[39m (\n\u001b[1;32m     94\u001b[0m         connection\n\u001b[1;32m     95\u001b[0m         \u001b[39mif\u001b[39;00m connection \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m---> 96\u001b[0m         \u001b[39melse\u001b[39;00m engine\u001b[39m.\u001b[39;49mraw_connection()\n\u001b[1;32m     97\u001b[0m     )\n\u001b[1;32m     99\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_transaction \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_nested_transaction \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    100\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__savepoint_seq \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/base.py:3313\u001b[0m, in \u001b[0;36mEngine.raw_connection\u001b[0;34m(self, _connection)\u001b[0m\n\u001b[1;32m   3291\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mraw_connection\u001b[39m(\u001b[39mself\u001b[39m, _connection\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m   3292\u001b[0m     \u001b[39m\"\"\"Return a \"raw\" DBAPI connection from the connection pool.\u001b[39;00m\n\u001b[1;32m   3293\u001b[0m \n\u001b[1;32m   3294\u001b[0m \u001b[39m    The returned object is a proxied version of the DBAPI\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3311\u001b[0m \n\u001b[1;32m   3312\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3313\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_wrap_pool_connect(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpool\u001b[39m.\u001b[39;49mconnect, _connection)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/base.py:3283\u001b[0m, in \u001b[0;36mEngine._wrap_pool_connect\u001b[0;34m(self, fn, connection)\u001b[0m\n\u001b[1;32m   3281\u001b[0m \u001b[39mexcept\u001b[39;00m dialect\u001b[39m.\u001b[39mdbapi\u001b[39m.\u001b[39mError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m   3282\u001b[0m     \u001b[39mif\u001b[39;00m connection \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 3283\u001b[0m         Connection\u001b[39m.\u001b[39;49m_handle_dbapi_exception_noconnection(\n\u001b[1;32m   3284\u001b[0m             e, dialect, \u001b[39mself\u001b[39;49m\n\u001b[1;32m   3285\u001b[0m         )\n\u001b[1;32m   3286\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   3287\u001b[0m         util\u001b[39m.\u001b[39mraise_(\n\u001b[1;32m   3288\u001b[0m             sys\u001b[39m.\u001b[39mexc_info()[\u001b[39m1\u001b[39m], with_traceback\u001b[39m=\u001b[39msys\u001b[39m.\u001b[39mexc_info()[\u001b[39m2\u001b[39m]\n\u001b[1;32m   3289\u001b[0m         )\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/base.py:2117\u001b[0m, in \u001b[0;36mConnection._handle_dbapi_exception_noconnection\u001b[0;34m(cls, e, dialect, engine)\u001b[0m\n\u001b[1;32m   2115\u001b[0m     util\u001b[39m.\u001b[39mraise_(newraise, with_traceback\u001b[39m=\u001b[39mexc_info[\u001b[39m2\u001b[39m], from_\u001b[39m=\u001b[39me)\n\u001b[1;32m   2116\u001b[0m \u001b[39melif\u001b[39;00m should_wrap:\n\u001b[0;32m-> 2117\u001b[0m     util\u001b[39m.\u001b[39;49mraise_(\n\u001b[1;32m   2118\u001b[0m         sqlalchemy_exception, with_traceback\u001b[39m=\u001b[39;49mexc_info[\u001b[39m2\u001b[39;49m], from_\u001b[39m=\u001b[39;49me\n\u001b[1;32m   2119\u001b[0m     )\n\u001b[1;32m   2120\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   2121\u001b[0m     util\u001b[39m.\u001b[39mraise_(exc_info[\u001b[39m1\u001b[39m], with_traceback\u001b[39m=\u001b[39mexc_info[\u001b[39m2\u001b[39m])\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    205\u001b[0m     exception\u001b[39m.\u001b[39m__cause__ \u001b[39m=\u001b[39m replace_context\n\u001b[1;32m    207\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[39mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[39m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[39m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[39m# as the __traceback__ object creates a cycle\u001b[39;00m\n\u001b[1;32m    213\u001b[0m     \u001b[39mdel\u001b[39;00m exception, replace_context, from_, with_traceback\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/base.py:3280\u001b[0m, in \u001b[0;36mEngine._wrap_pool_connect\u001b[0;34m(self, fn, connection)\u001b[0m\n\u001b[1;32m   3278\u001b[0m dialect \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdialect\n\u001b[1;32m   3279\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3280\u001b[0m     \u001b[39mreturn\u001b[39;00m fn()\n\u001b[1;32m   3281\u001b[0m \u001b[39mexcept\u001b[39;00m dialect\u001b[39m.\u001b[39mdbapi\u001b[39m.\u001b[39mError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m   3282\u001b[0m     \u001b[39mif\u001b[39;00m connection \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:310\u001b[0m, in \u001b[0;36mPool.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    302\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mconnect\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    303\u001b[0m     \u001b[39m\"\"\"Return a DBAPI connection from the pool.\u001b[39;00m\n\u001b[1;32m    304\u001b[0m \n\u001b[1;32m    305\u001b[0m \u001b[39m    The connection is instrumented such that when its\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    308\u001b[0m \n\u001b[1;32m    309\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 310\u001b[0m     \u001b[39mreturn\u001b[39;00m _ConnectionFairy\u001b[39m.\u001b[39;49m_checkout(\u001b[39mself\u001b[39;49m)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:868\u001b[0m, in \u001b[0;36m_ConnectionFairy._checkout\u001b[0;34m(cls, pool, threadconns, fairy)\u001b[0m\n\u001b[1;32m    865\u001b[0m \u001b[39m@classmethod\u001b[39m\n\u001b[1;32m    866\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_checkout\u001b[39m(\u001b[39mcls\u001b[39m, pool, threadconns\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, fairy\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m    867\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m fairy:\n\u001b[0;32m--> 868\u001b[0m         fairy \u001b[39m=\u001b[39m _ConnectionRecord\u001b[39m.\u001b[39;49mcheckout(pool)\n\u001b[1;32m    870\u001b[0m         fairy\u001b[39m.\u001b[39m_pool \u001b[39m=\u001b[39m pool\n\u001b[1;32m    871\u001b[0m         fairy\u001b[39m.\u001b[39m_counter \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:476\u001b[0m, in \u001b[0;36m_ConnectionRecord.checkout\u001b[0;34m(cls, pool)\u001b[0m\n\u001b[1;32m    474\u001b[0m \u001b[39m@classmethod\u001b[39m\n\u001b[1;32m    475\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcheckout\u001b[39m(\u001b[39mcls\u001b[39m, pool):\n\u001b[0;32m--> 476\u001b[0m     rec \u001b[39m=\u001b[39m pool\u001b[39m.\u001b[39;49m_do_get()\n\u001b[1;32m    477\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    478\u001b[0m         dbapi_connection \u001b[39m=\u001b[39m rec\u001b[39m.\u001b[39mget_connection()\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/impl.py:145\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_create_connection()\n\u001b[1;32m    144\u001b[0m     \u001b[39mexcept\u001b[39;00m:\n\u001b[0;32m--> 145\u001b[0m         \u001b[39mwith\u001b[39;00m util\u001b[39m.\u001b[39msafe_reraise():\n\u001b[1;32m    146\u001b[0m             \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dec_overflow()\n\u001b[1;32m    147\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py:70\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exc_info \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m  \u001b[39m# remove potential circular references\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwarn_only:\n\u001b[0;32m---> 70\u001b[0m         compat\u001b[39m.\u001b[39;49mraise_(\n\u001b[1;32m     71\u001b[0m             exc_value,\n\u001b[1;32m     72\u001b[0m             with_traceback\u001b[39m=\u001b[39;49mexc_tb,\n\u001b[1;32m     73\u001b[0m         )\n\u001b[1;32m     74\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     75\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m compat\u001b[39m.\u001b[39mpy3k \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exc_info \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exc_info[\u001b[39m1\u001b[39m]:\n\u001b[1;32m     76\u001b[0m         \u001b[39m# emulate Py3K's behavior of telling us when an exception\u001b[39;00m\n\u001b[1;32m     77\u001b[0m         \u001b[39m# occurs in an exception handler.\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    205\u001b[0m     exception\u001b[39m.\u001b[39m__cause__ \u001b[39m=\u001b[39m replace_context\n\u001b[1;32m    207\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[39mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[39m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[39m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[39m# as the __traceback__ object creates a cycle\u001b[39;00m\n\u001b[1;32m    213\u001b[0m     \u001b[39mdel\u001b[39;00m exception, replace_context, from_, with_traceback\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/impl.py:143\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_inc_overflow():\n\u001b[1;32m    142\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 143\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_create_connection()\n\u001b[1;32m    144\u001b[0m     \u001b[39mexcept\u001b[39;00m:\n\u001b[1;32m    145\u001b[0m         \u001b[39mwith\u001b[39;00m util\u001b[39m.\u001b[39msafe_reraise():\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:256\u001b[0m, in \u001b[0;36mPool._create_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_create_connection\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    254\u001b[0m     \u001b[39m\"\"\"Called by subclasses to create a new ConnectionRecord.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 256\u001b[0m     \u001b[39mreturn\u001b[39;00m _ConnectionRecord(\u001b[39mself\u001b[39;49m)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:371\u001b[0m, in \u001b[0;36m_ConnectionRecord.__init__\u001b[0;34m(self, pool, connect)\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__pool \u001b[39m=\u001b[39m pool\n\u001b[1;32m    370\u001b[0m \u001b[39mif\u001b[39;00m connect:\n\u001b[0;32m--> 371\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m__connect()\n\u001b[1;32m    372\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfinalize_callback \u001b[39m=\u001b[39m deque()\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:665\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    663\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfresh \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    664\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m--> 665\u001b[0m     \u001b[39mwith\u001b[39;00m util\u001b[39m.\u001b[39msafe_reraise():\n\u001b[1;32m    666\u001b[0m         pool\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mdebug(\u001b[39m\"\u001b[39m\u001b[39mError on connect(): \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m, e)\n\u001b[1;32m    667\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    668\u001b[0m     \u001b[39m# in SQLAlchemy 1.4 the first_connect event is not used by\u001b[39;00m\n\u001b[1;32m    669\u001b[0m     \u001b[39m# the engine, so this will usually not be set\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py:70\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exc_info \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m  \u001b[39m# remove potential circular references\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwarn_only:\n\u001b[0;32m---> 70\u001b[0m         compat\u001b[39m.\u001b[39;49mraise_(\n\u001b[1;32m     71\u001b[0m             exc_value,\n\u001b[1;32m     72\u001b[0m             with_traceback\u001b[39m=\u001b[39;49mexc_tb,\n\u001b[1;32m     73\u001b[0m         )\n\u001b[1;32m     74\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     75\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m compat\u001b[39m.\u001b[39mpy3k \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exc_info \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exc_info[\u001b[39m1\u001b[39m]:\n\u001b[1;32m     76\u001b[0m         \u001b[39m# emulate Py3K's behavior of telling us when an exception\u001b[39;00m\n\u001b[1;32m     77\u001b[0m         \u001b[39m# occurs in an exception handler.\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    205\u001b[0m     exception\u001b[39m.\u001b[39m__cause__ \u001b[39m=\u001b[39m replace_context\n\u001b[1;32m    207\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[39mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[39m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[39m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[39m# as the __traceback__ object creates a cycle\u001b[39;00m\n\u001b[1;32m    213\u001b[0m     \u001b[39mdel\u001b[39;00m exception, replace_context, from_, with_traceback\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/pool/base.py:661\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    659\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    660\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstarttime \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime()\n\u001b[0;32m--> 661\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdbapi_connection \u001b[39m=\u001b[39m connection \u001b[39m=\u001b[39m pool\u001b[39m.\u001b[39;49m_invoke_creator(\u001b[39mself\u001b[39;49m)\n\u001b[1;32m    662\u001b[0m     pool\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mdebug(\u001b[39m\"\u001b[39m\u001b[39mCreated new connection \u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m\"\u001b[39m, connection)\n\u001b[1;32m    663\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfresh \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/create.py:590\u001b[0m, in \u001b[0;36mcreate_engine.<locals>.connect\u001b[0;34m(connection_record)\u001b[0m\n\u001b[1;32m    588\u001b[0m         \u001b[39mif\u001b[39;00m connection \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    589\u001b[0m             \u001b[39mreturn\u001b[39;00m connection\n\u001b[0;32m--> 590\u001b[0m \u001b[39mreturn\u001b[39;00m dialect\u001b[39m.\u001b[39;49mconnect(\u001b[39m*\u001b[39;49mcargs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcparams)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/sqlalchemy/engine/default.py:597\u001b[0m, in \u001b[0;36mDefaultDialect.connect\u001b[0;34m(self, *cargs, **cparams)\u001b[0m\n\u001b[1;32m    595\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mconnect\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39mcargs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcparams):\n\u001b[1;32m    596\u001b[0m     \u001b[39m# inherits the docstring from interfaces.Dialect.connect\u001b[39;00m\n\u001b[0;32m--> 597\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdbapi\u001b[39m.\u001b[39;49mconnect(\u001b[39m*\u001b[39;49mcargs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcparams)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/pymysql/connections.py:353\u001b[0m, in \u001b[0;36mConnection.__init__\u001b[0;34m(self, user, password, host, database, unix_socket, port, charset, sql_mode, read_default_file, conv, use_unicode, client_flag, cursorclass, init_command, connect_timeout, read_default_group, autocommit, local_infile, max_allowed_packet, defer_connect, auth_plugin_map, read_timeout, write_timeout, bind_address, binary_prefix, program_name, server_public_key, ssl, ssl_ca, ssl_cert, ssl_disabled, ssl_key, ssl_verify_cert, ssl_verify_identity, compress, named_pipe, passwd, db)\u001b[0m\n\u001b[1;32m    351\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sock \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    352\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 353\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnect()\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/dataviz/lib/python3.10/site-packages/pymysql/connections.py:664\u001b[0m, in \u001b[0;36mConnection.connect\u001b[0;34m(self, sock)\u001b[0m\n\u001b[1;32m    662\u001b[0m     \u001b[39mif\u001b[39;00m DEBUG:\n\u001b[1;32m    663\u001b[0m         \u001b[39mprint\u001b[39m(exc\u001b[39m.\u001b[39mtraceback)\n\u001b[0;32m--> 664\u001b[0m     \u001b[39mraise\u001b[39;00m exc\n\u001b[1;32m    666\u001b[0m \u001b[39m# If e is neither DatabaseError or IOError, It's a bug.\u001b[39;00m\n\u001b[1;32m    667\u001b[0m \u001b[39m# But raising AssertionError hides original error.\u001b[39;00m\n\u001b[1;32m    668\u001b[0m \u001b[39m# So just reraise it.\u001b[39;00m\n\u001b[1;32m    669\u001b[0m \u001b[39mraise\u001b[39;00m\n",
      "\u001b[0;31mOperationalError\u001b[0m: (pymysql.err.OperationalError) (2003, \"Can't connect to MySQL server on 'Your URL' ([Errno 8] nodename nor servname provided, or not known)\")\n(Background on this error at: https://sqlalche.me/e/14/e3q8)"
     ]
    }
   ],
   "source": [
    "# Insert whole DataFrame into MySQL\n",
    "df_data.to_sql('gapminder_tidy', con = db_connection, if_exists = 'append', index = False, chunksize = 1000, dtype ={\n",
    "    'country': sqlalchemy.types.NVARCHAR(length=255),\n",
    "    'continent': sqlalchemy.types.NVARCHAR(length=255),\n",
    "    'year': sqlalchemy.types.INTEGER(),\n",
    "    'metric': sqlalchemy.types.NVARCHAR(length=255),\n",
    "    'value': sqlalchemy.types.Float(precision=3, asdecimal=True)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0f7b5ad-2fdb-4663-95c0-54a428014423",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1952</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>2449.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1957</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>3013.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1957</td>\n",
       "      <td>lifeExp</td>\n",
       "      <td>45.685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1962</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>2550.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1962</td>\n",
       "      <td>lifeExp</td>\n",
       "      <td>48.303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1997</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>21050.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2002</td>\n",
       "      <td>lifeExp</td>\n",
       "      <td>79.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2007</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>25185.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2007</td>\n",
       "      <td>lifeExp</td>\n",
       "      <td>80.204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5111</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2007</td>\n",
       "      <td>pop</td>\n",
       "      <td>4115770.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5112 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year     metric        value\n",
       "0         Algeria    Africa  1952  gdpPercap     2449.010\n",
       "1         Algeria    Africa  1957  gdpPercap     3013.980\n",
       "2         Algeria    Africa  1957    lifeExp       45.685\n",
       "3         Algeria    Africa  1962  gdpPercap     2550.820\n",
       "4         Algeria    Africa  1962    lifeExp       48.303\n",
       "...           ...       ...   ...        ...          ...\n",
       "5107  New Zealand   Oceania  1997  gdpPercap    21050.400\n",
       "5108  New Zealand   Oceania  2002    lifeExp       79.110\n",
       "5109  New Zealand   Oceania  2007  gdpPercap    25185.000\n",
       "5110  New Zealand   Oceania  2007    lifeExp       80.204\n",
       "5111  New Zealand   Oceania  2007        pop  4115770.000\n",
       "\n",
       "[5112 rows x 5 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFromDB = pd.read_sql('SELECT * FROM gapminder_tidy', con=db_connection)\n",
    "DataFromDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d8c6f8c-0569-4d1b-8cdb-9980092bffb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5014</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1952</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>10039.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5016</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1957</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>10949.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5019</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1962</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>12217.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5079</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1967</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>14526.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5081</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1972</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>16788.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5024</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1977</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>18334.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5083</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1982</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>19477.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5085</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1987</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>21888.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5028</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1992</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>23424.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5029</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1997</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>26997.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5090</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2002</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>30687.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5092</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2007</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>34435.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1952</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>10556.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5034</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1957</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>12247.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5037</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1962</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>13175.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5098</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1967</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>14463.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5038</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1972</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>16046.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5102</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1977</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>16233.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1982</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>17632.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5104</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1987</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>19007.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1992</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>18363.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>1997</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>21050.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5049</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2002</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>23189.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>2007</td>\n",
       "      <td>gdpPercap</td>\n",
       "      <td>25185.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year     metric    value\n",
       "5014    Australia   Oceania  1952  gdpPercap  10039.6\n",
       "5016    Australia   Oceania  1957  gdpPercap  10949.6\n",
       "5019    Australia   Oceania  1962  gdpPercap  12217.2\n",
       "5079    Australia   Oceania  1967  gdpPercap  14526.1\n",
       "5081    Australia   Oceania  1972  gdpPercap  16788.6\n",
       "5024    Australia   Oceania  1977  gdpPercap  18334.2\n",
       "5083    Australia   Oceania  1982  gdpPercap  19477.0\n",
       "5085    Australia   Oceania  1987  gdpPercap  21888.9\n",
       "5028    Australia   Oceania  1992  gdpPercap  23424.8\n",
       "5029    Australia   Oceania  1997  gdpPercap  26997.9\n",
       "5090    Australia   Oceania  2002  gdpPercap  30687.8\n",
       "5092    Australia   Oceania  2007  gdpPercap  34435.4\n",
       "5094  New Zealand   Oceania  1952  gdpPercap  10556.6\n",
       "5034  New Zealand   Oceania  1957  gdpPercap  12247.4\n",
       "5037  New Zealand   Oceania  1962  gdpPercap  13175.7\n",
       "5098  New Zealand   Oceania  1967  gdpPercap  14463.9\n",
       "5038  New Zealand   Oceania  1972  gdpPercap  16046.0\n",
       "5102  New Zealand   Oceania  1977  gdpPercap  16233.7\n",
       "5041  New Zealand   Oceania  1982  gdpPercap  17632.4\n",
       "5104  New Zealand   Oceania  1987  gdpPercap  19007.2\n",
       "5105  New Zealand   Oceania  1992  gdpPercap  18363.3\n",
       "5107  New Zealand   Oceania  1997  gdpPercap  21050.4\n",
       "5049  New Zealand   Oceania  2002  gdpPercap  23189.8\n",
       "5109  New Zealand   Oceania  2007  gdpPercap  25185.0"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gdp_oceania = DataFromDB.query(\"continent=='Oceania' & metric=='gdpPercap'\").sort_values(['country','year'],ascending = [True, True])\n",
    "df_gdp_oceania"
   ]
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   ],
   "source": [
    "fig = px.line(df_gdp_o, x = \"year\", y = \"value\", color = \"country\")\n",
    "fig.show()"
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