Section 04 Joining Tables/19 MySQL CROSS JOIN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ff1afc93-3423-4be6-b53f-19a391b73dff",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## **[MySQL CROSS JOIN](https://www.mysqltutorial.org/mysql-cross-join/)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "217cf46c-f7a8-4bca-86f4-e781623ae640",
   "metadata": {},
   "source": [
    "Learn about the MySQL CROSS JOIN clause and how to use it more effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a08fbef-9b84-4857-9915-f78b7ae31f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "from IPython.display import Image, SVG\n",
    "\n",
    "from sqlalchemy_utils import database_exists, create_database\n",
    "from sqlalchemy import create_engine, inspect, MetaData, text\n",
    "from sqlalchemy_schemadisplay import create_schema_graph\n",
    "import pymysql\n",
    "\n",
    "pd.set_option(\n",
    "    'display.max_columns', None,\n",
    "    'expand_frame_repr', True,\n",
    "    'display.max_colwidth', None,\n",
    "    'display.max_rows', 10,\n",
    ")\n",
    "\n",
    "pd.set_option('display.width', 65)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87300d45-565e-4cad-980c-24d8758e76e7",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Introduction to MySQL CROSS JOIN clause"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7e3ea48b-1a7c-464f-a0db-4ac75ff2d7f0",
   "metadata": {},
   "source": [
    "SELECT * FROM t1\n",
    "CROSS JOIN t2;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b693035-bd10-4695-b93d-84e064df9a6a",
   "metadata": {
    "tags": []
   },
   "source": [
    "### MySQL CROSS JOIN clause examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf14d75e-97d8-40c5-8960-ad5cf3f07f17",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Setting up sample tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f28b595-feb1-4262-95cb-b38d1cd1ad06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# create salesdb database\n",
    "# https://stackoverflow.com/a/30971098/2757266\n",
    "connect_args={'ssl':{'fake_flag_to_enable_tls': True}}\n",
    "\n",
    "engine = create_engine(\n",
    "    'mysql+pymysql://namlq:abc123@localhost/salesdb',\n",
    "    connect_args=connect_args)    \n",
    "    \n",
    "if not database_exists(engine.url):\n",
    "    create_database(engine.url)\n",
    "\n",
    "print(database_exists(engine.url))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "edc168d4-6d96-4499-bc53-66704fd84c44",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19485/2027352558.py:2: RemovedIn20Warning: Deprecated API features detected! These feature(s) are not compatible with SQLAlchemy 2.0. To prevent incompatible upgrades prior to updating applications, ensure requirements files are pinned to \"sqlalchemy<2.0\". Set environment variable SQLALCHEMY_WARN_20=1 to show all deprecation warnings.  Set environment variable SQLALCHEMY_SILENCE_UBER_WARNING=1 to silence this message. (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9)\n",
      "  engine.execute('DROP TABLE IF EXISTS sales, products, stores')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x7f69f115cd90>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delete exists tables\n",
    "engine.execute('DROP TABLE IF EXISTS sales, products, stores')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da67e585-562f-470e-83c7-6f7b5b41e230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x7f69f11819d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create table products\n",
    "string = '''\n",
    "CREATE TABLE products (\n",
    "    id INT PRIMARY KEY AUTO_INCREMENT,\n",
    "    product_name VARCHAR(100),\n",
    "    price DECIMAL(13, 2))\n",
    ";'''\n",
    "\n",
    "engine.execute(string)\n",
    "\n",
    "# create table stores\n",
    "string = '''\n",
    "CREATE TABLE stores (\n",
    "    id INT PRIMARY KEY AUTO_INCREMENT,\n",
    "    store_name VARCHAR(100))\n",
    ";'''\n",
    "\n",
    "engine.execute(string)\n",
    "\n",
    "# create table sales\n",
    "string = '''\n",
    "CREATE TABLE sales (\n",
    "    product_id INT,\n",
    "    store_id INT,\n",
    "    quantity DECIMAL(13, 2) NOT NULL,\n",
    "    sales_date DATE NOT NULL,\n",
    "    PRIMARY KEY (product_id, store_id),\n",
    "    FOREIGN KEY (product_id)\n",
    "        REFERENCES products (id)\n",
    "        ON DELETE CASCADE ON UPDATE CASCADE,\n",
    "    FOREIGN KEY (store_id)\n",
    "        REFERENCES stores (id)\n",
    "        ON DELETE CASCADE ON UPDATE CASCADE)\n",
    ";'''\n",
    "\n",
    "engine.execute(string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9bc8bab-4274-411a-a41b-9dcd868538e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(235, 391, 4)\n"
     ]
    }
   ],
   "source": [
    "metadata = MetaData(bind=engine)\n",
    "graph = create_schema_graph(\n",
    "    metadata=metadata,\n",
    "    show_datatypes=True, # The image would get nasty big if we'd show the datatypes\n",
    "    show_indexes=True, # ditto for indexes\n",
    "    rankdir='TB', # LR: From left to right, TB: top to bottom\n",
    "    concentrate=True # Don't try to join the relation lines together\n",
    ")\n",
    "\n",
    "import io\n",
    "\n",
    "# render the `pydot` by calling `dot`, no file saved to disk\n",
    "png_str = graph.create_png(prog='dot')\n",
    "\n",
    "# treat the DOT output as an image file\n",
    "sio = io.BytesIO()\n",
    "sio.write(png_str)\n",
    "sio.seek(0)\n",
    "img = mpimg.imread(sio)\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2068a19-3216-4b76-98e2-9debf18b194c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f69f11814f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 782x470 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the image\n",
    "fig, ax = plt.subplots(figsize=(3.91*2, 2.35*2))\n",
    "ax.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f5eb12b1-1262-4dff-ac51-d069dd4581f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x7f6a442b7670>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert data into the three tables\n",
    "string = '''\n",
    "INSERT INTO products (product_name, price)\n",
    "VALUES ('iPhone', 699), ('iPad', 599), ('Macbook Pro', 1299)\n",
    ";'''\n",
    "\n",
    "engine.execute(string)\n",
    "\n",
    "string = '''\n",
    "INSERT INTO stores(store_name)\n",
    "VALUES ('North'), ('South')\n",
    ";'''\n",
    "\n",
    "engine.execute(string)\n",
    "\n",
    "string = '''\n",
    "INSERT INTO sales(store_id, product_id, quantity, sales_date)\n",
    "VALUES  (1,1,20,'2017-01-02'),\n",
    "        (1,2,15,'2017-01-05'),\n",
    "        (1,3,25,'2017-01-05'),\n",
    "        (2,1,30,'2017-01-02'),\n",
    "        (2,2,35,'2017-01-05')\n",
    ";'''\n",
    "\n",
    "engine.execute(string)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b594c8-6a97-4dd8-a517-fa7e27e0647c",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### MySQL CROSS JOIN example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6595ddb3-106f-465c-a07d-9edb46a93815",
   "metadata": {},
   "outputs": [],
   "source": [
    "# returns total sales for each store and product\n",
    "# SUM() and GROUP BY are not necessary because\n",
    "# (store_name, product_name) are unique\n",
    "string = '''\n",
    "SELECT\n",
    "    store_name,\n",
    "    product_name,\n",
    "    SUM(quantity * price) AS revenue\n",
    "FROM sales\n",
    "INNER JOIN products ON sales.product_id = products.id\n",
    "INNER JOIN stores ON sales.store_id = stores.id\n",
    "GROUP BY store_name, product_name\n",
    "ORDER BY revenue\n",
    ";'''\n",
    "\n",
    "df1 = pd.read_sql(string, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b264298-8338-4f30-821f-44ec7eb5a507",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = (\n",
    "    pd.read_sql_table('sales', engine)\n",
    "    .merge(pd.read_sql_table('products', engine),\n",
    "           left_on='product_id', right_on='id',\n",
    "           how='inner')\n",
    "    .merge(pd.read_sql_table('stores', engine),\n",
    "           left_on='store_id', right_on='id',\n",
    "           how='inner')\n",
    "    .assign(revenue = lambda df: df['quantity'] * df['price'])\n",
    "    # groupby and agg here are not necessary\n",
    "    .groupby(by=['store_name', 'product_name'], as_index=False)\n",
    "    .agg({'revenue': 'sum'})\n",
    "    [['store_name', 'product_name', 'revenue']]\n",
    "    .sort_values('revenue', ignore_index=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1429d6f4-a1de-43ce-8a62-ce9106659929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.equals(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ec0a33fb-dd5a-4d91-aa96-4bdbe49ac11a",
   "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>store_name</th>\n",
       "      <th>product_name</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>North</td>\n",
       "      <td>iPad</td>\n",
       "      <td>8985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>13980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South</td>\n",
       "      <td>iPad</td>\n",
       "      <td>20965.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>20970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>North</td>\n",
       "      <td>Macbook Pro</td>\n",
       "      <td>32475.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  store_name product_name  revenue\n",
       "0      North         iPad   8985.0\n",
       "1      North       iPhone  13980.0\n",
       "2      South         iPad  20965.0\n",
       "3      South       iPhone  20970.0\n",
       "4      North  Macbook Pro  32475.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08cf12c4-a0f7-4020-b271-103de0f9a643",
   "metadata": {},
   "source": [
    "Which store had no sales of a specific product?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c7b4c96-a400-48d2-86b4-2e73cc14acee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use CROSS JOIN clause to get the combination of all stores and products\n",
    "string = '''\n",
    "SELECT store_name, product_name\n",
    "FROM stores\n",
    "CROSS JOIN products\n",
    ";'''\n",
    "\n",
    "df3 = pd.read_sql(string, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c4522e5f-f816-477d-b0ac-5ff97e8770ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = (\n",
    "    pd.read_sql_table('stores', engine)\n",
    "    .merge(pd.read_sql_table('products', engine),\n",
    "           how='cross')\n",
    "    [['store_name', 'product_name']]\n",
    "    .sort_values(['product_name', 'store_name'], \n",
    "                 ascending=[False, False],\n",
    "                 ignore_index=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eae7a154-0137-48a2-9a72-49bf99e33b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.equals(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b78bacbf-4080-4b98-928f-c09790a9395e",
   "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>store_name</th>\n",
       "      <th>product_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>South</td>\n",
       "      <td>iPhone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North</td>\n",
       "      <td>iPhone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South</td>\n",
       "      <td>iPad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North</td>\n",
       "      <td>iPad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South</td>\n",
       "      <td>Macbook Pro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>North</td>\n",
       "      <td>Macbook Pro</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  store_name product_name\n",
       "0      South       iPhone\n",
       "1      North       iPhone\n",
       "2      South         iPad\n",
       "3      North         iPad\n",
       "4      South  Macbook Pro\n",
       "5      North  Macbook Pro"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c7e86663-e48b-4942-a8a1-80db65870dbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# join the result of the query above with a query\n",
    "# that returns the total of sales by store and product\n",
    "string = '''\n",
    "SELECT \n",
    "    b.store_name,\n",
    "    a.product_name,\n",
    "    IFNULL(c.revenue, 0) AS revenue\n",
    "FROM\n",
    "    products AS a\n",
    "CROSS JOIN\n",
    "    stores AS b\n",
    "LEFT JOIN\n",
    "    (SELECT \n",
    "        stores.id AS store_id,\n",
    "        products.id AS product_id,\n",
    "        store_name,\n",
    "        product_name,\n",
    "        ROUND(SUM(quantity * price), 0) AS revenue\n",
    "    FROM sales\n",
    "    INNER JOIN products ON products.id = sales.product_id\n",
    "    INNER JOIN stores ON stores.id = sales.store_id\n",
    "    GROUP BY stores.id, products.id, store_name , product_name\n",
    "    ) AS c \n",
    "    \n",
    "    ON c.store_id = b.id AND c.product_id= a.id\n",
    "    \n",
    "ORDER BY a.product_name, b.store_name;\n",
    "'''\n",
    "\n",
    "df5 = pd.read_sql(string, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dfe0094c-cd3b-4ec8-91c5-a121f6fd3a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "df6 = (\n",
    "    df4.merge(\n",
    "        df2, on=['store_name', 'product_name'], how='left')\n",
    "    .fillna(0)\n",
    "    .sort_values(\n",
    "        by=['product_name', 'store_name'], ascending=[True, True],\n",
    "        ignore_index=True, key = lambda col: col.str.upper())\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7517d799-9b00-4934-9248-647a1a73f6a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.equals(df6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b5efce56-bb20-49e7-817e-dfa290562fe6",
   "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>store_name</th>\n",
       "      <th>product_name</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>North</td>\n",
       "      <td>iPad</td>\n",
       "      <td>8985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South</td>\n",
       "      <td>iPad</td>\n",
       "      <td>20965.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>13980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>20970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>North</td>\n",
       "      <td>Macbook Pro</td>\n",
       "      <td>32475.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South</td>\n",
       "      <td>Macbook Pro</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  store_name product_name  revenue\n",
       "0      North         iPad   8985.0\n",
       "1      South         iPad  20965.0\n",
       "2      North       iPhone  13980.0\n",
       "3      South       iPhone  20970.0\n",
       "4      North  Macbook Pro  32475.0\n",
       "5      South  Macbook Pro      0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "14676311-a6db-4f03-991d-600975480d53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x7f6a453ed730>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delete exists tables\n",
    "engine.execute('DROP TABLE IF EXISTS sales, products, stores')"
   ]
  }
 ],
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