BONUS_employees.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import dependencies\n",
    "# Pandas\n",
    "import pandas as pd\n",
    "\n",
    "import psycopg2\n",
    "\n",
    "# Matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# NumPy\n",
    "import numpy as np\n",
    "\n",
    "import scipy.stats as st\n",
    "\n",
    "from config import password"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "engine = create_engine(f'postgresql://postgres:{password}@localhost:5432/EmployeeSQL')\n",
    "connection = engine.connect()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emp_no</th>\n",
       "      <th>emp_title</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>473302</td>\n",
       "      <td>s0001</td>\n",
       "      <td>2053-07-25</td>\n",
       "      <td>Hideyuki</td>\n",
       "      <td>Zallocco</td>\n",
       "      <td>M</td>\n",
       "      <td>1990-04-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>475053</td>\n",
       "      <td>e0002</td>\n",
       "      <td>2054-11-18</td>\n",
       "      <td>Byong</td>\n",
       "      <td>Delgrande</td>\n",
       "      <td>F</td>\n",
       "      <td>1991-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57444</td>\n",
       "      <td>e0002</td>\n",
       "      <td>2058-01-30</td>\n",
       "      <td>Berry</td>\n",
       "      <td>Babb</td>\n",
       "      <td>F</td>\n",
       "      <td>1992-03-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>421786</td>\n",
       "      <td>s0001</td>\n",
       "      <td>2057-09-28</td>\n",
       "      <td>Xiong</td>\n",
       "      <td>Verhoeff</td>\n",
       "      <td>M</td>\n",
       "      <td>1987-11-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>282238</td>\n",
       "      <td>e0003</td>\n",
       "      <td>2052-10-28</td>\n",
       "      <td>Abdelkader</td>\n",
       "      <td>Baumann</td>\n",
       "      <td>F</td>\n",
       "      <td>1991-01-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emp_no emp_title  birth_date  first_name  last_name sex   hire_date\n",
       "0  473302     s0001  2053-07-25    Hideyuki   Zallocco   M  1990-04-28\n",
       "1  475053     e0002  2054-11-18       Byong  Delgrande   F  1991-09-07\n",
       "2   57444     e0002  2058-01-30       Berry       Babb   F  1992-03-21\n",
       "3  421786     s0001  2057-09-28       Xiong   Verhoeff   M  1987-11-26\n",
       "4  282238     e0003  2052-10-28  Abdelkader    Baumann   F  1991-01-18"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_employees = pd.read_sql(\"select * from data_employees\", connection)\n",
    "data_employees.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>emp_no</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>60117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>65828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>40006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>40054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005</td>\n",
       "      <td>78228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emp_no  salary\n",
       "0   10001   60117\n",
       "1   10002   65828\n",
       "2   10003   40006\n",
       "3   10004   40054\n",
       "4   10005   78228"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Salaries analysis \n",
    "data_salaries = pd.read_sql(\"SELECT * from data_salaries\", connection)\n",
    "data_salaries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_no    10001\n",
       "salary    40000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_salaries.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_no    499999\n",
       "salary    129492\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_salaries.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emp_no</th>\n",
       "      <th>title_id</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>473302</td>\n",
       "      <td>s0001</td>\n",
       "      <td>2053-07-25</td>\n",
       "      <td>Hideyuki</td>\n",
       "      <td>Zallocco</td>\n",
       "      <td>M</td>\n",
       "      <td>1990-04-28</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>475053</td>\n",
       "      <td>e0002</td>\n",
       "      <td>2054-11-18</td>\n",
       "      <td>Byong</td>\n",
       "      <td>Delgrande</td>\n",
       "      <td>F</td>\n",
       "      <td>1991-09-07</td>\n",
       "      <td>53422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57444</td>\n",
       "      <td>e0002</td>\n",
       "      <td>2058-01-30</td>\n",
       "      <td>Berry</td>\n",
       "      <td>Babb</td>\n",
       "      <td>F</td>\n",
       "      <td>1992-03-21</td>\n",
       "      <td>48973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>421786</td>\n",
       "      <td>s0001</td>\n",
       "      <td>2057-09-28</td>\n",
       "      <td>Xiong</td>\n",
       "      <td>Verhoeff</td>\n",
       "      <td>M</td>\n",
       "      <td>1987-11-26</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>282238</td>\n",
       "      <td>e0003</td>\n",
       "      <td>2052-10-28</td>\n",
       "      <td>Abdelkader</td>\n",
       "      <td>Baumann</td>\n",
       "      <td>F</td>\n",
       "      <td>1991-01-18</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300019</th>\n",
       "      <td>464231</td>\n",
       "      <td>s0001</td>\n",
       "      <td>2058-08-14</td>\n",
       "      <td>Constantino</td>\n",
       "      <td>Eastman</td>\n",
       "      <td>M</td>\n",
       "      <td>1988-10-28</td>\n",
       "      <td>69696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300020</th>\n",
       "      <td>255832</td>\n",
       "      <td>e0002</td>\n",
       "      <td>2055-05-08</td>\n",
       "      <td>Yuping</td>\n",
       "      <td>Dayang</td>\n",
       "      <td>F</td>\n",
       "      <td>1995-02-26</td>\n",
       "      <td>75355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300021</th>\n",
       "      <td>76671</td>\n",
       "      <td>s0001</td>\n",
       "      <td>2059-06-09</td>\n",
       "      <td>Ortrud</td>\n",
       "      <td>Plessier</td>\n",
       "      <td>M</td>\n",
       "      <td>1988-02-24</td>\n",
       "      <td>61886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300022</th>\n",
       "      <td>264920</td>\n",
       "      <td>s0001</td>\n",
       "      <td>2059-09-22</td>\n",
       "      <td>Percy</td>\n",
       "      <td>Samarati</td>\n",
       "      <td>F</td>\n",
       "      <td>1994-09-08</td>\n",
       "      <td>62772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300023</th>\n",
       "      <td>464503</td>\n",
       "      <td>s0002</td>\n",
       "      <td>2064-05-31</td>\n",
       "      <td>Arvind</td>\n",
       "      <td>Slobodova</td>\n",
       "      <td>M</td>\n",
       "      <td>1987-11-23</td>\n",
       "      <td>41708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300024 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        emp_no title_id  birth_date   first_name  last_name sex   hire_date  \\\n",
       "0       473302    s0001  2053-07-25     Hideyuki   Zallocco   M  1990-04-28   \n",
       "1       475053    e0002  2054-11-18        Byong  Delgrande   F  1991-09-07   \n",
       "2        57444    e0002  2058-01-30        Berry       Babb   F  1992-03-21   \n",
       "3       421786    s0001  2057-09-28        Xiong   Verhoeff   M  1987-11-26   \n",
       "4       282238    e0003  2052-10-28   Abdelkader    Baumann   F  1991-01-18   \n",
       "...        ...      ...         ...          ...        ...  ..         ...   \n",
       "300019  464231    s0001  2058-08-14  Constantino    Eastman   M  1988-10-28   \n",
       "300020  255832    e0002  2055-05-08       Yuping     Dayang   F  1995-02-26   \n",
       "300021   76671    s0001  2059-06-09       Ortrud   Plessier   M  1988-02-24   \n",
       "300022  264920    s0001  2059-09-22        Percy   Samarati   F  1994-09-08   \n",
       "300023  464503    s0002  2064-05-31       Arvind  Slobodova   M  1987-11-23   \n",
       "\n",
       "        salary  \n",
       "0        40000  \n",
       "1        53422  \n",
       "2        48973  \n",
       "3        40000  \n",
       "4        40000  \n",
       "...        ...  \n",
       "300019   69696  \n",
       "300020   75355  \n",
       "300021   61886  \n",
       "300022   62772  \n",
       "300023   41708  \n",
       "\n",
       "[300024 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees_salary = pd.merge(data_employees, data_salaries, on = \"emp_no\")\n",
    "employees_salary\n",
    "employees_salary2= employees_salary.rename(columns={\"emp_title\":\"title_id\"})\n",
    "employees_salary2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram to visualize the most common salary ranges for employees.\n",
    "x = employees_salary2['salary']\n",
    "plt.hist(x, bins=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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_id</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s0001</td>\n",
       "      <td>Staff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s0002</td>\n",
       "      <td>Senior Staff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e0001</td>\n",
       "      <td>Assistant Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e0002</td>\n",
       "      <td>Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e0003</td>\n",
       "      <td>Senior Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>e0004</td>\n",
       "      <td>Technique Leader</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>m0001</td>\n",
       "      <td>Manager</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  title_id               title\n",
       "0    s0001               Staff\n",
       "1    s0002        Senior Staff\n",
       "2    e0001  Assistant Engineer\n",
       "3    e0002            Engineer\n",
       "4    e0003     Senior Engineer\n",
       "5    e0004    Technique Leader\n",
       "6    m0001             Manager"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a bar chart of average salary by title\n",
    "titles_salary= pd.read_sql('select * from data_titles', connection)\n",
    "titles_salary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>emp_no</th>\n",
       "      <th>title_id</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>473302</td>\n",
       "      <td>s0001</td>\n",
       "      <td>2053-07-25</td>\n",
       "      <td>Hideyuki</td>\n",
       "      <td>Zallocco</td>\n",
       "      <td>M</td>\n",
       "      <td>1990-04-28</td>\n",
       "      <td>40000</td>\n",
       "      <td>Staff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>421786</td>\n",
       "      <td>s0001</td>\n",
       "      <td>2057-09-28</td>\n",
       "      <td>Xiong</td>\n",
       "      <td>Verhoeff</td>\n",
       "      <td>M</td>\n",
       "      <td>1987-11-26</td>\n",
       "      <td>40000</td>\n",
       "      <td>Staff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>273487</td>\n",
       "      <td>s0001</td>\n",
       "      <td>2057-04-14</td>\n",
       "      <td>Christoph</td>\n",
       "      <td>Parfitt</td>\n",
       "      <td>M</td>\n",
       "      <td>1991-06-28</td>\n",
       "      <td>56087</td>\n",
       "      <td>Staff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>246449</td>\n",
       "      <td>s0001</td>\n",
       "      <td>2058-03-23</td>\n",
       "      <td>Subbu</td>\n",
       "      <td>Bultermann</td>\n",
       "      <td>F</td>\n",
       "      <td>1988-03-25</td>\n",
       "      <td>87084</td>\n",
       "      <td>Staff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48085</td>\n",
       "      <td>s0001</td>\n",
       "      <td>2064-01-19</td>\n",
       "      <td>Venkatesan</td>\n",
       "      <td>Gilg</td>\n",
       "      <td>M</td>\n",
       "      <td>1993-06-28</td>\n",
       "      <td>63016</td>\n",
       "      <td>Staff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300019</th>\n",
       "      <td>111400</td>\n",
       "      <td>m0001</td>\n",
       "      <td>2059-11-09</td>\n",
       "      <td>Arie</td>\n",
       "      <td>Staelin</td>\n",
       "      <td>M</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>72446</td>\n",
       "      <td>Manager</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300020</th>\n",
       "      <td>111133</td>\n",
       "      <td>m0001</td>\n",
       "      <td>2055-03-16</td>\n",
       "      <td>Hauke</td>\n",
       "      <td>Zhang</td>\n",
       "      <td>M</td>\n",
       "      <td>1986-12-30</td>\n",
       "      <td>71148</td>\n",
       "      <td>Manager</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300021</th>\n",
       "      <td>110085</td>\n",
       "      <td>m0001</td>\n",
       "      <td>2059-10-28</td>\n",
       "      <td>Ebru</td>\n",
       "      <td>Alpin</td>\n",
       "      <td>M</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>60026</td>\n",
       "      <td>Manager</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300022</th>\n",
       "      <td>110386</td>\n",
       "      <td>m0001</td>\n",
       "      <td>2053-10-04</td>\n",
       "      <td>Shem</td>\n",
       "      <td>Kieras</td>\n",
       "      <td>M</td>\n",
       "      <td>1988-10-14</td>\n",
       "      <td>40000</td>\n",
       "      <td>Manager</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300023</th>\n",
       "      <td>111784</td>\n",
       "      <td>m0001</td>\n",
       "      <td>2056-06-14</td>\n",
       "      <td>Marjo</td>\n",
       "      <td>Giarratana</td>\n",
       "      <td>F</td>\n",
       "      <td>1988-02-12</td>\n",
       "      <td>40000</td>\n",
       "      <td>Manager</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300024 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        emp_no title_id  birth_date  first_name   last_name sex   hire_date  \\\n",
       "0       473302    s0001  2053-07-25    Hideyuki    Zallocco   M  1990-04-28   \n",
       "1       421786    s0001  2057-09-28       Xiong    Verhoeff   M  1987-11-26   \n",
       "2       273487    s0001  2057-04-14   Christoph     Parfitt   M  1991-06-28   \n",
       "3       246449    s0001  2058-03-23       Subbu  Bultermann   F  1988-03-25   \n",
       "4        48085    s0001  2064-01-19  Venkatesan        Gilg   M  1993-06-28   \n",
       "...        ...      ...         ...         ...         ...  ..         ...   \n",
       "300019  111400    m0001  2059-11-09        Arie     Staelin   M  1985-01-01   \n",
       "300020  111133    m0001  2055-03-16       Hauke       Zhang   M  1986-12-30   \n",
       "300021  110085    m0001  2059-10-28        Ebru       Alpin   M  1985-01-01   \n",
       "300022  110386    m0001  2053-10-04        Shem      Kieras   M  1988-10-14   \n",
       "300023  111784    m0001  2056-06-14       Marjo  Giarratana   F  1988-02-12   \n",
       "\n",
       "        salary    title  \n",
       "0        40000    Staff  \n",
       "1        40000    Staff  \n",
       "2        56087    Staff  \n",
       "3        87084    Staff  \n",
       "4        63016    Staff  \n",
       "...        ...      ...  \n",
       "300019   72446  Manager  \n",
       "300020   71148  Manager  \n",
       "300021   60026  Manager  \n",
       "300022   40000  Manager  \n",
       "300023   40000  Manager  \n",
       "\n",
       "[300024 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging tables\n",
    "emp_salary_title = pd.merge(employees_salary2, titles_salary, on = \"title_id\")\n",
    "emp_salary_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Assistant Engineer    48564.43\n",
       "Engineer              48535.34\n",
       "Manager               51531.04\n",
       "Senior Engineer       48506.80\n",
       "Senior Staff          58550.17\n",
       "Staff                 58465.38\n",
       "Technique Leader      48582.90\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_avg = emp_salary_title.groupby(['title']).mean()['salary'] \n",
    "title_avg.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Assistant Engineer', 'Engineer', 'Manager', 'Senior Engineer',\n",
       "       'Senior Staff', 'Staff', 'Technique Leader'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_axis = title_avg.index.values\n",
    "x_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Assistant Engineer', 'Engineer', 'Manager', 'Senior Engineer',\n",
       "       'Senior Staff', 'Staff', 'Technique Leader'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_axis = title_avg.index.values\n",
    "y_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bar chart using pyplot\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(rotation = 45)\n",
    "plt.title(\"Average Salary per Job Title\")\n",
    "plt.xlabel(\"Job Title\")\n",
    "plt.ylabel(\"Salary ($)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}