Section 04 Joining Tables/18 MySQL RIGHT JOIN.ipynb

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    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "## **[MySQL RIGHT JOIN](https://www.mysqltutorial.org/mysql-right-join/)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70e0e967-28d4-443d-9bf5-1ce29570ebba",
   "metadata": {},
   "source": [
    "Use the MySQL RIGHT JOIN to query data from two tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "41332f6b-f3fb-4a26-92bd-11cf0c0b5ce5",
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   "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": "code",
   "execution_count": 2,
   "id": "64d45c6c-2906-4289-995b-9e747c29998a",
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   "outputs": [],
   "source": [
    "connect_args={'ssl':{'fake_flag_to_enable_tls': True}}\n",
    "\n",
    "engine = create_engine('mysql+pymysql://namlq:abc123@localhost/classicmodels',\n",
    "                       connect_args=connect_args, echo=False\n",
    "                         )\n",
    "inspector = inspect(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fc22d07-243c-4435-8ef8-7af1bf59daa7",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Introduction to MySQL RIGHT JOIN clause"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10bf2c74-568d-4cc7-bebc-c9521d759d06",
   "metadata": {},
   "source": [
    "syntax:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "cd80f5b2-cdd8-4e78-85f0-1fffe5ffb339",
   "metadata": {},
   "source": [
    "SELECT \n",
    "    select_list\n",
    "FROM t1\n",
    "RIGHT JOIN t2 ON \n",
    "    join_condition;"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "### MySQL RIGHT JOIN clause examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0bc4a99c-d365-45d1-a769-82657d63b31e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../images/customers-employees.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c67b7c6-c51d-439f-aeec-89fa29d0d7d1",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 1) Simple MySQL RIGHT JOIN example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3167adf2-4d85-43fa-8b63-9443ce060ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "string = '''\n",
    "SELECT employeeNumber, customerNumber\n",
    "FROM customers\n",
    "RIGHT JOIN employees ON salesRepEmployeeNumber = employeeNumber\n",
    "ORDER BY\n",
    "    customerNumber DESC,\n",
    "    employeeNumber ASC\n",
    ";'''\n",
    "\n",
    "df1 = pd.read_sql(string, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "076feca2-dee8-4577-9fa2-dbf22097c61c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = (\n",
    "    pd.read_sql_table('customers', engine)\n",
    "    .merge(pd.read_sql_table('employees', engine),\n",
    "           left_on='salesRepEmployeeNumber',\n",
    "           right_on='employeeNumber',\n",
    "           how='right')\n",
    "    [['employeeNumber', 'customerNumber']]\n",
    "    .sort_values(by=['customerNumber', 'employeeNumber'], \n",
    "                 ascending=[False, True],\n",
    "                 ignore_index=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e588caa-9fa9-42ee-a545-ee6d993d8270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.equals(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d57196bd-e84d-4ee2-8e3c-8cdbc5b630af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employeeNumber</th>\n",
       "      <th>customerNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1612</td>\n",
       "      <td>496.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1188</td>\n",
       "      <td>495.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1501</td>\n",
       "      <td>489.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1165</td>\n",
       "      <td>487.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1323</td>\n",
       "      <td>486.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1088</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1102</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1143</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1619</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1625</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     employeeNumber  customerNumber\n",
       "0              1612           496.0\n",
       "1              1188           495.0\n",
       "2              1501           489.0\n",
       "3              1165           487.0\n",
       "4              1323           486.0\n",
       "..              ...             ...\n",
       "103            1088             NaN\n",
       "104            1102             NaN\n",
       "105            1143             NaN\n",
       "106            1619             NaN\n",
       "107            1625             NaN\n",
       "\n",
       "[108 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7868274d-9f7f-4f56-ba81-b33eec98789c",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 2) Using MySQL RIGHT JOIN to find unmatching rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3789c6c6-c5aa-46ed-88a4-047786e85ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "string = '''\n",
    "SELECT employeeNumber, customerNumber\n",
    "FROM customers\n",
    "RIGHT JOIN employees ON salesRepEmployeeNumber = employeeNumber\n",
    "WHERE customerNumber IS NULL\n",
    "ORDER BY employeeNumber\n",
    ";'''\n",
    "df1 = pd.read_sql_query(\n",
    "    string, engine, dtype={'customerNumber': float})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e6cc3b53-ef70-4d5e-90a2-ca7c1986d92c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = (\n",
    "    pd.read_sql_table('customers', engine)\n",
    "    .merge(pd.read_sql_table('employees', engine),\n",
    "           left_on='salesRepEmployeeNumber',\n",
    "           right_on='employeeNumber',\n",
    "           how='right')\n",
    "    .query('customerNumber.isna()')\n",
    "    [['employeeNumber', 'customerNumber']]\n",
    "    .sort_values('employeeNumber', ignore_index=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f0595004-ba8b-4c97-9738-3c3350f5533f",
   "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": "88e9a5dc-b873-4261-a6d3-267b07e106c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>employeeNumber</th>\n",
       "      <th>customerNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1056</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1076</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1088</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>1102</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1143</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1619</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1625</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employeeNumber  customerNumber\n",
       "0            1002             NaN\n",
       "1            1056             NaN\n",
       "2            1076             NaN\n",
       "3            1088             NaN\n",
       "4            1102             NaN\n",
       "5            1143             NaN\n",
       "6            1619             NaN\n",
       "7            1625             NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  }
 ],
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