code/Connect_to_PostgresDB_and_Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connect to the Postgres DB using SQL Alchemy toolkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import username, password, hostname_or_ip, port, DB\n",
    "from sqlalchemy import create_engine\n",
    "db_uri = f'postgresql://{username}:{password}@{hostname_or_ip}:{port}/{DB}'\n",
    "engine = create_engine(db_uri, echo=True) #echo = True to log every query our SQL database executes to the terminal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-16 02:29:31,150 INFO sqlalchemy.engine.base.Engine select version()\n",
      "2020-07-16 02:29:31,151 INFO sqlalchemy.engine.base.Engine {}\n",
      "2020-07-16 02:29:31,154 INFO sqlalchemy.engine.base.Engine select current_schema()\n",
      "2020-07-16 02:29:31,155 INFO sqlalchemy.engine.base.Engine {}\n",
      "2020-07-16 02:29:31,164 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1\n",
      "2020-07-16 02:29:31,165 INFO sqlalchemy.engine.base.Engine {}\n",
      "2020-07-16 02:29:31,177 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1\n",
      "2020-07-16 02:29:31,179 INFO sqlalchemy.engine.base.Engine {}\n",
      "2020-07-16 02:29:31,189 INFO sqlalchemy.engine.base.Engine show standard_conforming_strings\n",
      "2020-07-16 02:29:31,191 INFO sqlalchemy.engine.base.Engine {}\n"
     ]
    }
   ],
   "source": [
    "connection = engine.connect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a histogram to visualize the most common salary ranges for employees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Direct SQL query result to pandas DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-16 02:29:35,720 INFO sqlalchemy.engine.base.Engine select relname from pg_class c join pg_namespace n on n.oid=c.relnamespace where pg_catalog.pg_table_is_visible(c.oid) and relname=%(name)s\n",
      "2020-07-16 02:29:35,722 INFO sqlalchemy.engine.base.Engine {'name': 'SELECT emp.emp_no, sal.salary FROM employees AS emp LEFT JOIN salaries AS sal ON emp.emp_no = sal.emp_no'}\n",
      "2020-07-16 02:29:35,792 INFO sqlalchemy.engine.base.Engine SELECT emp.emp_no, sal.salary FROM employees AS emp LEFT JOIN salaries AS sal ON emp.emp_no = sal.emp_no\n",
      "2020-07-16 02:29:35,800 INFO sqlalchemy.engine.base.Engine {}\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "query = \"SELECT emp.emp_no, sal.salary \\\n",
    "FROM employees AS emp \\\n",
    "LEFT JOIN salaries AS sal \\\n",
    "ON emp.emp_no = sal.emp_no\"\n",
    "\n",
    "#Using this query instead of \"query = \"SELECT * FROM salaries\"\" as emp_no ideally should be queries based on this table\"\n",
    "\n",
    "Employee_Salary_DF = pd.read_sql(\n",
    "            query,\n",
    "            con=connection\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-16 02:29:38,691 INFO sqlalchemy.engine.base.Engine select relname from pg_class c join pg_namespace n on n.oid=c.relnamespace where pg_catalog.pg_table_is_visible(c.oid) and relname=%(name)s\n",
      "2020-07-16 02:29:38,694 INFO sqlalchemy.engine.base.Engine {'name': 'SELECT * FROM salaries'}\n",
      "2020-07-16 02:29:38,715 INFO sqlalchemy.engine.base.Engine SELECT * FROM salaries\n",
      "2020-07-16 02:29:38,717 INFO sqlalchemy.engine.base.Engine {}\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "query = \"SELECT * FROM salaries\"\n",
    "\n",
    "Employee_Salary_DF = pd.read_sql(\n",
    "            query,\n",
    "            con=connection\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create histograms\n",
    "- **Salary range is 40000 to 129492**\n",
    "- **Mean salary is 52970.7**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "#For readability, create custom bins\n",
    "custom_bins= np.arange(40000, 130001, 10000)\n",
    "n, bins, patches = ax.hist(Employee_Salary_DF['salary'], color='lightcoral', alpha=0.8,bins=custom_bins, rwidth=0.95, zorder=1)\n",
    "_=ax.set_title(\"Histogram of Salaries of All employees\", fontsize=18, weight='bold', y=1.01)\n",
    "_=ax.set_xlabel('Salary Range',fontsize = 15, labelpad=15)\n",
    "_=ax.set_ylabel('Number of Employees \\nreceiving salary (at that range)',fontsize = 15, labelpad=15)\n",
    "\n",
    "\n",
    "#Annotation\n",
    "#Mean, 25%, 50%(Median), 75% quartiles\n",
    "label = '\\n\\n'.join(Employee_Salary_DF['salary'].describe().iloc[[1,4,5,6]].reset_index().apply(lambda x: \"{} = {:,.1f} $\".format(x['index'], x['salary']), axis=1).to_list())\n",
    "(x,y) = (115000, 70000)\n",
    "_=ax.annotate(label, # this is the text\n",
    "                 (x,y), # this is the point to label\n",
    "                 textcoords=\"offset points\", # how to position the text ('offset pixels'  is the other option)\n",
    "                 xytext=(10,10), # distance from text to points (x,y)\n",
    "                 ha='center', # horizontal alignment can be left, right or center\n",
    "                 fontsize=14,\n",
    "                 color='k') \n",
    "_=plt.xticks(bins)\n",
    "_=ax.set_xticklabels([\"$ {:,}\".format(int(val)) for val in bins], rotation=0)\n",
    "_ = plt.tight_layout()\n",
    "_= plt.savefig('../Images/histogram.png', bbox_inches = \"tight\" )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a bar chart of average salary by title."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Direct SQL query result to pandas DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-16 02:32:27,831 INFO sqlalchemy.engine.base.Engine select relname from pg_class c join pg_namespace n on n.oid=c.relnamespace where pg_catalog.pg_table_is_visible(c.oid) and relname=%(name)s\n",
      "2020-07-16 02:32:27,833 INFO sqlalchemy.engine.base.Engine {'name': 'SELECT titles.title, AVG(sal.salary) AS Average_Salary FROM employees AS emp LEFT JOIN salaries AS sal ON emp.emp_no = sal.emp_no LEFT JOIN titles ON emp.emp_title_id = titles.title_id GROUP BY titles.title ORDER BY Average_Salary DESC'}\n",
      "2020-07-16 02:32:27,839 INFO sqlalchemy.engine.base.Engine SELECT titles.title, AVG(sal.salary) AS Average_Salary FROM employees AS emp LEFT JOIN salaries AS sal ON emp.emp_no = sal.emp_no LEFT JOIN titles ON emp.emp_title_id = titles.title_id GROUP BY titles.title ORDER BY Average_Salary DESC\n",
      "2020-07-16 02:32:27,840 INFO sqlalchemy.engine.base.Engine {}\n"
     ]
    }
   ],
   "source": [
    "query = \"SELECT titles.title, AVG(sal.salary) AS Average_Salary \\\n",
    "FROM employees AS emp \\\n",
    "LEFT JOIN salaries AS sal ON emp.emp_no = sal.emp_no \\\n",
    "LEFT JOIN titles ON emp.emp_title_id = titles.title_id \\\n",
    "GROUP BY titles.title \\\n",
    "ORDER BY Average_Salary DESC\"\n",
    "\n",
    "Employee_title_Agg_Salary_DF = pd.read_sql(\n",
    "            query,\n",
    "            con=connection\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>title</th>\n",
       "      <th>average_salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Senior Staff</td>\n",
       "      <td>58550.172704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Staff</td>\n",
       "      <td>58465.382850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Manager</td>\n",
       "      <td>51531.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Technique Leader</td>\n",
       "      <td>48582.896092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Assistant Engineer</td>\n",
       "      <td>48564.434447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Engineer</td>\n",
       "      <td>48535.336511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>48506.799871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                title  average_salary\n",
       "0        Senior Staff    58550.172704\n",
       "1               Staff    58465.382850\n",
       "2             Manager    51531.041667\n",
       "3    Technique Leader    48582.896092\n",
       "4  Assistant Engineer    48564.434447\n",
       "5            Engineer    48535.336511\n",
       "6     Senior Engineer    48506.799871"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Employee_title_Agg_Salary_DF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create bar chart\n",
    "- **All the technical positions have less average salary compared to managerial positions**\n",
    "- **Senior Engineer position has less average salary than Engineer and Assistant Engineer. This is a proof that this data is fake**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "#_=Employee_Salary_DF.hist(column='salary', ax=ax, color='lightcoral', alpha=0.8, grid=False, bins=16, rwidth=0.9, zorder=1)\n",
    "_=Employee_title_Agg_Salary_DF.plot.bar(x='title', y='average_salary',color='lightcoral',alpha=0.8, ax=ax, legend=None, width=0.6, rot=-10, fontsize=15)\n",
    "_=ax.set_title(\"Average Salaries for job titles\", fontsize=18, weight='bold', y=1.01)\n",
    "_=ax.set_xlabel('Job Titles',fontsize = 15, labelpad=15)\n",
    "_=ax.set_ylabel('Average Salary',fontsize = 15, labelpad=15)\n",
    "\n",
    "\n",
    "#Annotate\n",
    "_=ax.set_yticklabels([\"$ {:,}\".format(int(val)) for val in ax.get_yticks()])\n",
    "for p in ax.patches:\n",
    "    _=ax.annotate(\"$ {:,.1f}\".format(p.get_height()), (p.get_x(), p.get_height()+1000), fontsize=12)\n",
    "    \n",
    "_ = plt.tight_layout()\n",
    "_= plt.savefig('../Images/Avg_salary_per_title.png', bbox_inches = \"tight\" )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Epilogue\n",
    "## What is with \"499942\" emp id ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-16 02:32:46,485 INFO sqlalchemy.engine.base.Engine select relname from pg_class c join pg_namespace n on n.oid=c.relnamespace where pg_catalog.pg_table_is_visible(c.oid) and relname=%(name)s\n",
      "2020-07-16 02:32:46,489 INFO sqlalchemy.engine.base.Engine {'name': 'SELECT emp.birth_date, emp.first_name, emp.last_name, emp.sex, emp.hire_date, sal.salary,  titles.title, dept.dept_name FROM employees AS emp LEFT JO ... (99 characters truncated) ... d LEFT JOIN dept_emp AS depEmp ON emp.emp_no = depEmp.emp_no LEFT JOIN departments AS dept ON depEmp.dept_no = dept.dept_no WHERE emp.emp_no = 499942'}\n",
      "2020-07-16 02:32:46,495 INFO sqlalchemy.engine.base.Engine SELECT emp.birth_date, emp.first_name, emp.last_name, emp.sex, emp.hire_date, sal.salary,  titles.title, dept.dept_name FROM employees AS emp LEFT JOIN salaries AS sal ON emp.emp_no = sal.emp_no LEFT JOIN titles ON emp.emp_title_id = titles.title_id LEFT JOIN dept_emp AS depEmp ON emp.emp_no = depEmp.emp_no LEFT JOIN departments AS dept ON depEmp.dept_no = dept.dept_no WHERE emp.emp_no = 499942\n",
      "2020-07-16 02:32:46,497 INFO sqlalchemy.engine.base.Engine {}\n"
     ]
    }
   ],
   "source": [
    "query = \"SELECT emp.birth_date, emp.first_name, emp.last_name, emp.sex, emp.hire_date, \\\n",
    "sal.salary,  titles.title, dept.dept_name \\\n",
    "FROM employees AS emp \\\n",
    "LEFT JOIN salaries AS sal ON emp.emp_no = sal.emp_no \\\n",
    "LEFT JOIN titles ON emp.emp_title_id = titles.title_id \\\n",
    "LEFT JOIN dept_emp AS depEmp ON emp.emp_no = depEmp.emp_no \\\n",
    "LEFT JOIN departments AS dept ON depEmp.dept_no = dept.dept_no \\\n",
    "WHERE emp.emp_no = 499942\"\n",
    "\n",
    "MyData_DF = pd.read_sql(\n",
    "            query,\n",
    "            con=connection\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>birth_date</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>sex</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>salary</th>\n",
       "      <th>title</th>\n",
       "      <th>dept_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1963-01-10</td>\n",
       "      <td>April</td>\n",
       "      <td>Foolsday</td>\n",
       "      <td>F</td>\n",
       "      <td>1997-02-10</td>\n",
       "      <td>40000</td>\n",
       "      <td>Technique Leader</td>\n",
       "      <td>Development</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   birth_date first_name last_name sex   hire_date  salary             title  \\\n",
       "0  1963-01-10      April  Foolsday   F  1997-02-10   40000  Technique Leader   \n",
       "\n",
       "     dept_name  \n",
       "0  Development  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MyData_DF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------------------------END--------------------------------------------------------------------------------------------"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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