Oracle_Jupyter/Oracle_IPython_SQL_magic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SQL Magic extension for Oracle and IPython\n",
    "- An example of how to query Oracle from IPython using SQL magic extensions\n",
    "- This is a straighforward interface for running SQL\n",
    "- The results integrate easily into the python environment and with [pandas](http://pandas.pydata.org/)\n",
    "- Dependencies: needs an [Oracle client installation](http://www.oracle.com/technetwork/topics/linuxx86-64soft-092277.html), [cx_Oracle](https://pypi.python.org/pypi/cx_Oracle) and [ipython-sql](https://github.com/LucaCanali/ipython-sql)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup and prerequisites"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "This is how you can setup an Oracle instance for testing using a docker image for oracle-free\n",
    "\n",
    "# run oracle free on a container from gvenzl dockerhub repo, use docker or podman\n",
    "# see also https://github.com/gvenzl/oci-oracle-free\n",
    "docker run -d --name mydb1 -e ORACLE_PASSWORD=oracle -p 1521:1521 gvenzl/oracle-free:23.5-slim\n",
    "\n",
    "# wait till the DB is started, it will take a few minutes\n",
    "# check progress at:\n",
    "docker logs -f mydb1\n",
    "\n",
    "# install the scott/tiger schema with the emp table in PDB freepdb1\n",
    "docker exec -it mydb1 /bin/bash\n",
    "sed -e s=SCOTT/tiger=SCOTT/tiger@freepdb1= -e s/OFF/ON/ /opt/oracle/product/23ai/dbhomeFree/rdbms/admin/utlsampl.sql > script.sql\n",
    "sqlplus system/oracle@freepdb1\n",
    "@script.sql\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Optional instructions to setup Oracle client\n",
    "\n",
    "- Download from https://www.oracle.com/database/technologies/instant-client/linux-x86-64-downloads.html\n",
    "- Install locally \n",
    "- Test connectivity using sqlplus:\n",
    "\n",
    "cd <oracle_client_home_path> # edit as neeeded\n",
    "export LD_LIBRARY_PATH=$PWD\n",
    "./sqlplus scott/tiger@//localhost:1521/FREEPDB1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connect to Oracle and run a query using %SQL magic extensions\n",
    "- SQL magic extensions introduce the %%sql cell magic and %sql line magic for running SQL in IPython\n",
    "- The connect string to the database uses [sql alchemy syntax](http://docs.sqlalchemy.org/en/latest/core/engines.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the following requires ipython-sql, install if neeeded using\n",
    "! pip install ipython-sql"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notes on the oracledb library:**   \n",
    "This uses oracledb to connect to oracle, so no need to install the Oracle client for this example.  \n",
    "Note: oracledb can also work with the oracle client using `oracledb.init_oracle_client()`, see documentation for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use oracledb library in thin mode for this, so no need to install Oracle client libraries\n",
    "# if you want to use cx_Oracle or oracledb in client mode you will need to install the Oracle Client,\n",
    "# set LD_LIBRARY_PATH and restart jupyter\n",
    "\n",
    "! pip install oracledb\n",
    "\n",
    "import oracledb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loads the SQL magic extensions\n",
    "%load_ext sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to Oracle using oracledb library\n",
    "# this is for legacy cx_Oracle %sql oracle+cx_oracle://scott:tiger@localhost:1521?service_name=FREEPDB1\n",
    "\n",
    "%sql oracle+oracledb://scott:tiger@localhost:1521?service_name=FREEPDB1\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * oracle+oracledb://scott:***@localhost:1521?service_name=FREEPDB1\n",
      "0 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>empno</th>\n",
       "            <th>ename</th>\n",
       "            <th>job</th>\n",
       "            <th>mgr</th>\n",
       "            <th>hiredate</th>\n",
       "            <th>sal</th>\n",
       "            <th>comm</th>\n",
       "            <th>deptno</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>7369</td>\n",
       "            <td>SMITH</td>\n",
       "            <td>CLERK</td>\n",
       "            <td>7902</td>\n",
       "            <td>1980-12-17 00:00:00</td>\n",
       "            <td>800</td>\n",
       "            <td>None</td>\n",
       "            <td>20</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7499</td>\n",
       "            <td>ALLEN</td>\n",
       "            <td>SALESMAN</td>\n",
       "            <td>7698</td>\n",
       "            <td>1981-02-20 00:00:00</td>\n",
       "            <td>1600</td>\n",
       "            <td>300</td>\n",
       "            <td>30</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7521</td>\n",
       "            <td>WARD</td>\n",
       "            <td>SALESMAN</td>\n",
       "            <td>7698</td>\n",
       "            <td>1981-02-22 00:00:00</td>\n",
       "            <td>1250</td>\n",
       "            <td>500</td>\n",
       "            <td>30</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7566</td>\n",
       "            <td>JONES</td>\n",
       "            <td>MANAGER</td>\n",
       "            <td>7839</td>\n",
       "            <td>1981-04-02 00:00:00</td>\n",
       "            <td>2975</td>\n",
       "            <td>None</td>\n",
       "            <td>20</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7654</td>\n",
       "            <td>MARTIN</td>\n",
       "            <td>SALESMAN</td>\n",
       "            <td>7698</td>\n",
       "            <td>1981-09-28 00:00:00</td>\n",
       "            <td>1250</td>\n",
       "            <td>1400</td>\n",
       "            <td>30</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7698</td>\n",
       "            <td>BLAKE</td>\n",
       "            <td>MANAGER</td>\n",
       "            <td>7839</td>\n",
       "            <td>1981-05-01 00:00:00</td>\n",
       "            <td>2850</td>\n",
       "            <td>None</td>\n",
       "            <td>30</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7782</td>\n",
       "            <td>CLARK</td>\n",
       "            <td>MANAGER</td>\n",
       "            <td>7839</td>\n",
       "            <td>1981-06-09 00:00:00</td>\n",
       "            <td>2450</td>\n",
       "            <td>None</td>\n",
       "            <td>10</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7788</td>\n",
       "            <td>SCOTT</td>\n",
       "            <td>ANALYST</td>\n",
       "            <td>7566</td>\n",
       "            <td>1987-04-19 00:00:00</td>\n",
       "            <td>3000</td>\n",
       "            <td>None</td>\n",
       "            <td>20</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7839</td>\n",
       "            <td>KING</td>\n",
       "            <td>PRESIDENT</td>\n",
       "            <td>None</td>\n",
       "            <td>1981-11-17 00:00:00</td>\n",
       "            <td>5000</td>\n",
       "            <td>None</td>\n",
       "            <td>10</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7844</td>\n",
       "            <td>TURNER</td>\n",
       "            <td>SALESMAN</td>\n",
       "            <td>7698</td>\n",
       "            <td>1981-09-08 00:00:00</td>\n",
       "            <td>1500</td>\n",
       "            <td>0</td>\n",
       "            <td>30</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7876</td>\n",
       "            <td>ADAMS</td>\n",
       "            <td>CLERK</td>\n",
       "            <td>7788</td>\n",
       "            <td>1987-05-23 00:00:00</td>\n",
       "            <td>1100</td>\n",
       "            <td>None</td>\n",
       "            <td>20</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7900</td>\n",
       "            <td>JAMES</td>\n",
       "            <td>CLERK</td>\n",
       "            <td>7698</td>\n",
       "            <td>1981-12-03 00:00:00</td>\n",
       "            <td>950</td>\n",
       "            <td>None</td>\n",
       "            <td>30</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7902</td>\n",
       "            <td>FORD</td>\n",
       "            <td>ANALYST</td>\n",
       "            <td>7566</td>\n",
       "            <td>1981-12-03 00:00:00</td>\n",
       "            <td>3000</td>\n",
       "            <td>None</td>\n",
       "            <td>20</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7934</td>\n",
       "            <td>MILLER</td>\n",
       "            <td>CLERK</td>\n",
       "            <td>7782</td>\n",
       "            <td>1982-01-23 00:00:00</td>\n",
       "            <td>1300</td>\n",
       "            <td>None</td>\n",
       "            <td>10</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(7369, 'SMITH', 'CLERK', 7902, datetime.datetime(1980, 12, 17, 0, 0), Decimal('800'), None, 20),\n",
       " (7499, 'ALLEN', 'SALESMAN', 7698, datetime.datetime(1981, 2, 20, 0, 0), Decimal('1600'), Decimal('300'), 30),\n",
       " (7521, 'WARD', 'SALESMAN', 7698, datetime.datetime(1981, 2, 22, 0, 0), Decimal('1250'), Decimal('500'), 30),\n",
       " (7566, 'JONES', 'MANAGER', 7839, datetime.datetime(1981, 4, 2, 0, 0), Decimal('2975'), None, 20),\n",
       " (7654, 'MARTIN', 'SALESMAN', 7698, datetime.datetime(1981, 9, 28, 0, 0), Decimal('1250'), Decimal('1400'), 30),\n",
       " (7698, 'BLAKE', 'MANAGER', 7839, datetime.datetime(1981, 5, 1, 0, 0), Decimal('2850'), None, 30),\n",
       " (7782, 'CLARK', 'MANAGER', 7839, datetime.datetime(1981, 6, 9, 0, 0), Decimal('2450'), None, 10),\n",
       " (7788, 'SCOTT', 'ANALYST', 7566, datetime.datetime(1987, 4, 19, 0, 0), Decimal('3000'), None, 20),\n",
       " (7839, 'KING', 'PRESIDENT', None, datetime.datetime(1981, 11, 17, 0, 0), Decimal('5000'), None, 10),\n",
       " (7844, 'TURNER', 'SALESMAN', 7698, datetime.datetime(1981, 9, 8, 0, 0), Decimal('1500'), Decimal('0'), 30),\n",
       " (7876, 'ADAMS', 'CLERK', 7788, datetime.datetime(1987, 5, 23, 0, 0), Decimal('1100'), None, 20),\n",
       " (7900, 'JAMES', 'CLERK', 7698, datetime.datetime(1981, 12, 3, 0, 0), Decimal('950'), None, 30),\n",
       " (7902, 'FORD', 'ANALYST', 7566, datetime.datetime(1981, 12, 3, 0, 0), Decimal('3000'), None, 20),\n",
       " (7934, 'MILLER', 'CLERK', 7782, datetime.datetime(1982, 1, 23, 0, 0), Decimal('1300'), None, 10)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql \n",
    "select * from emp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bind variables and DML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * oracle+oracledb://scott:***@localhost:1521?service_name=FREEPDB1\n",
      "0 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>empno</th>\n",
       "            <th>ename</th>\n",
       "            <th>job</th>\n",
       "            <th>mgr</th>\n",
       "            <th>hiredate</th>\n",
       "            <th>sal</th>\n",
       "            <th>comm</th>\n",
       "            <th>deptno</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>7788</td>\n",
       "            <td>SCOTT</td>\n",
       "            <td>ANALYST</td>\n",
       "            <td>7566</td>\n",
       "            <td>1987-04-19 00:00:00</td>\n",
       "            <td>3000</td>\n",
       "            <td>None</td>\n",
       "            <td>20</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(7788, 'SCOTT', 'ANALYST', 7566, datetime.datetime(1987, 4, 19, 0, 0), Decimal('3000'), None, 20)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Employee_name=\"SCOTT\"\n",
    "\n",
    "%sql select * from emp where ename=:Employee_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * oracle+oracledb://scott:***@localhost:1521?service_name=FREEPDB1\n",
      "1 rows affected.\n",
      " * oracle+oracledb://scott:***@localhost:1521?service_name=FREEPDB1\n",
      "0 rows affected.\n",
      " * oracle+oracledb://scott:***@localhost:1521?service_name=FREEPDB1\n",
      "0 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>empno</th>\n",
       "            <th>ename</th>\n",
       "            <th>job</th>\n",
       "            <th>mgr</th>\n",
       "            <th>hiredate</th>\n",
       "            <th>sal</th>\n",
       "            <th>comm</th>\n",
       "            <th>deptno</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>7788</td>\n",
       "            <td>SCOTT</td>\n",
       "            <td>ANALYST</td>\n",
       "            <td>7566</td>\n",
       "            <td>1987-04-19 00:00:00</td>\n",
       "            <td>3500</td>\n",
       "            <td>None</td>\n",
       "            <td>20</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(7788, 'SCOTT', 'ANALYST', 7566, datetime.datetime(1987, 4, 19, 0, 0), Decimal('3500'), None, 20)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql update emp set sal=3500 where ename=:Employee_name\n",
    "%sql commit\n",
    "%sql select * from emp where ename=:Employee_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional example of the integration with the IPython environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * oracle+oracledb://scott:***@localhost:1521?service_name=FREEPDB1\n",
      "0 rows affected.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myResultSet=%sql select ename \"Employee Name\", sal \"Salary\" from emp\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "plt.style.use('seaborn-v0_8-darkgrid')\n",
    "\n",
    "myResultSet.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Integration with Pandas \n",
    "This opens many additional possibilities for data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * oracle+oracledb://scott:***@localhost:1521?service_name=FREEPDB1\n",
      "0 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Employee Name</th>\n",
       "            <th>Job</th>\n",
       "            <th>Manager Name</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>SCOTT</td>\n",
       "            <td>ANALYST</td>\n",
       "            <td>JONES</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>FORD</td>\n",
       "            <td>ANALYST</td>\n",
       "            <td>JONES</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ALLEN</td>\n",
       "            <td>SALESMAN</td>\n",
       "            <td>BLAKE</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>WARD</td>\n",
       "            <td>SALESMAN</td>\n",
       "            <td>BLAKE</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MARTIN</td>\n",
       "            <td>SALESMAN</td>\n",
       "            <td>BLAKE</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>TURNER</td>\n",
       "            <td>SALESMAN</td>\n",
       "            <td>BLAKE</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>JAMES</td>\n",
       "            <td>CLERK</td>\n",
       "            <td>BLAKE</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>MILLER</td>\n",
       "            <td>CLERK</td>\n",
       "            <td>CLARK</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ADAMS</td>\n",
       "            <td>CLERK</td>\n",
       "            <td>SCOTT</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>JONES</td>\n",
       "            <td>MANAGER</td>\n",
       "            <td>KING</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>BLAKE</td>\n",
       "            <td>MANAGER</td>\n",
       "            <td>KING</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CLARK</td>\n",
       "            <td>MANAGER</td>\n",
       "            <td>KING</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SMITH</td>\n",
       "            <td>CLERK</td>\n",
       "            <td>FORD</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>KING</td>\n",
       "            <td>PRESIDENT</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('SCOTT', 'ANALYST', 'JONES'),\n",
       " ('FORD', 'ANALYST', 'JONES'),\n",
       " ('ALLEN', 'SALESMAN', 'BLAKE'),\n",
       " ('WARD', 'SALESMAN', 'BLAKE'),\n",
       " ('MARTIN', 'SALESMAN', 'BLAKE'),\n",
       " ('TURNER', 'SALESMAN', 'BLAKE'),\n",
       " ('JAMES', 'CLERK', 'BLAKE'),\n",
       " ('MILLER', 'CLERK', 'CLARK'),\n",
       " ('ADAMS', 'CLERK', 'SCOTT'),\n",
       " ('JONES', 'MANAGER', 'KING'),\n",
       " ('BLAKE', 'MANAGER', 'KING'),\n",
       " ('CLARK', 'MANAGER', 'KING'),\n",
       " ('SMITH', 'CLERK', 'FORD'),\n",
       " ('KING', 'PRESIDENT', None)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select e1.ename \"Employee Name\", e1.job \"Job\", e2.ename \"Manager Name\" \n",
    "from emp e1, emp e2\n",
    "where e1.mgr = e2.empno(+)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Employee Name</th>\n",
       "      <th>Job</th>\n",
       "      <th>Manager Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SCOTT</td>\n",
       "      <td>ANALYST</td>\n",
       "      <td>JONES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FORD</td>\n",
       "      <td>ANALYST</td>\n",
       "      <td>JONES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALLEN</td>\n",
       "      <td>SALESMAN</td>\n",
       "      <td>BLAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WARD</td>\n",
       "      <td>SALESMAN</td>\n",
       "      <td>BLAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MARTIN</td>\n",
       "      <td>SALESMAN</td>\n",
       "      <td>BLAKE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Employee Name       Job Manager Name\n",
       "0         SCOTT   ANALYST        JONES\n",
       "1          FORD   ANALYST        JONES\n",
       "2         ALLEN  SALESMAN        BLAKE\n",
       "3          WARD  SALESMAN        BLAKE\n",
       "4        MARTIN  SALESMAN        BLAKE"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save result set into my_ResultSet and copy it to pandas in my_DataFrame\n",
    "my_ResultSet = _\n",
    "\n",
    "my_DataFrame=my_ResultSet.DataFrame()\n",
    "\n",
    "my_DataFrame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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