Bonus_SQL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sqlalchemy\n",
    "import pprint\n",
    "import psycopg2\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# connect to local database\n",
    "from sqlalchemy import create_engine\n",
    "engine = create_engine('postgresql+psycopg2://postgres:postgres@localhost:5433/Homework')\n",
    "connection = engine.connect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dept_no</th>\n",
       "      <th>dept_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d001</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d002</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d003</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d004</td>\n",
       "      <td>Production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d005</td>\n",
       "      <td>Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>d006</td>\n",
       "      <td>Quality Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>d007</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>d008</td>\n",
       "      <td>Research</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>d009</td>\n",
       "      <td>Customer Service</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dept_no           dept_name\n",
       "0    d001           Marketing\n",
       "1    d002             Finance\n",
       "2    d003     Human Resources\n",
       "3    d004          Production\n",
       "4    d005         Development\n",
       "5    d006  Quality Management\n",
       "6    d007               Sales\n",
       "7    d008            Research\n",
       "8    d009    Customer Service"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departments = pd.read_sql('select * from departments', connection)\n",
    "departments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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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>birth_date</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>1953-09-02</td>\n",
       "      <td>Georgi</td>\n",
       "      <td>Facello</td>\n",
       "      <td>M</td>\n",
       "      <td>1986-06-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>1964-06-02</td>\n",
       "      <td>Bezalel</td>\n",
       "      <td>Simmel</td>\n",
       "      <td>F</td>\n",
       "      <td>1985-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>1959-12-03</td>\n",
       "      <td>Parto</td>\n",
       "      <td>Bamford</td>\n",
       "      <td>M</td>\n",
       "      <td>1986-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>1954-05-01</td>\n",
       "      <td>Chirstian</td>\n",
       "      <td>Koblick</td>\n",
       "      <td>M</td>\n",
       "      <td>1986-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005</td>\n",
       "      <td>1955-01-21</td>\n",
       "      <td>Kyoichi</td>\n",
       "      <td>Maliniak</td>\n",
       "      <td>M</td>\n",
       "      <td>1989-09-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emp_no birth_date first_name last_name gender  hire_date\n",
       "0   10001 1953-09-02     Georgi   Facello      M 1986-06-26\n",
       "1   10002 1964-06-02    Bezalel    Simmel      F 1985-11-21\n",
       "2   10003 1959-12-03      Parto   Bamford      M 1986-08-28\n",
       "3   10004 1954-05-01  Chirstian   Koblick      M 1986-12-01\n",
       "4   10005 1955-01-21    Kyoichi  Maliniak      M 1989-09-12"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Import the Employees table\"\n",
    "employees = pd.read_sql('select * from employees', connection, parse_dates=['birth_date', 'hire_date'])\n",
    "employees.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dept_no</th>\n",
       "      <th>dept_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d001</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d002</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d003</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d004</td>\n",
       "      <td>Production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d005</td>\n",
       "      <td>Development</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  dept_no        dept_name\n",
       "0    d001        Marketing\n",
       "1    d002          Finance\n",
       "2    d003  Human Resources\n",
       "3    d004       Production\n",
       "4    d005      Development"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Import the Departments table\"\n",
    "departments = pd.read_sql('select * from departments', connection)\n",
    "departments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>salary</th>\n",
       "      <th>from_date</th>\n",
       "      <th>to_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>60117</td>\n",
       "      <td>1986-06-26</td>\n",
       "      <td>1987-06-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>65828</td>\n",
       "      <td>1996-08-03</td>\n",
       "      <td>1997-08-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>40006</td>\n",
       "      <td>1995-12-03</td>\n",
       "      <td>1996-12-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>40054</td>\n",
       "      <td>1986-12-01</td>\n",
       "      <td>1987-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005</td>\n",
       "      <td>78228</td>\n",
       "      <td>1989-09-12</td>\n",
       "      <td>1990-09-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   emp_no  salary  from_date    to_date\n",
       "0   10001   60117 1986-06-26 1987-06-26\n",
       "1   10002   65828 1996-08-03 1997-08-03\n",
       "2   10003   40006 1995-12-03 1996-12-02\n",
       "3   10004   40054 1986-12-01 1987-12-01\n",
       "4   10005   78228 1989-09-12 1990-09-12"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Import the Salaries table\n",
    "salaries = pd.read_sql('select * from salaries', connection, parse_dates=['from_date', 'to_date'])\n",
    "salaries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dept_no</th>\n",
       "      <th>emp_no</th>\n",
       "      <th>from_date</th>\n",
       "      <th>to_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d001</td>\n",
       "      <td>110022</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>1991-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d001</td>\n",
       "      <td>110039</td>\n",
       "      <td>1991-10-01</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d002</td>\n",
       "      <td>110085</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>1989-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d002</td>\n",
       "      <td>110114</td>\n",
       "      <td>1989-12-17</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d003</td>\n",
       "      <td>110183</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>1992-03-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  dept_no  emp_no  from_date    to_date\n",
       "0    d001  110022 1985-01-01 1991-10-01\n",
       "1    d001  110039 1991-10-01        NaT\n",
       "2    d002  110085 1985-01-01 1989-12-17\n",
       "3    d002  110114 1989-12-17        NaT\n",
       "4    d003  110183 1985-01-01 1992-03-21"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " ### Import the Department Manager table\n",
    "dept_manager = pd.read_sql('select * from dept_manager', connection, parse_dates=['from_date', 'to_date'])\n",
    "dept_manager.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dept_no</th>\n",
       "      <th>emp_no</th>\n",
       "      <th>from_date</th>\n",
       "      <th>to_date</th>\n",
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       "      <th>0</th>\n",
       "      <td>d001</td>\n",
       "      <td>110022</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>1991-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d001</td>\n",
       "      <td>110039</td>\n",
       "      <td>1991-10-01</td>\n",
       "      <td>2050-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d002</td>\n",
       "      <td>110085</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>1989-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d002</td>\n",
       "      <td>110114</td>\n",
       "      <td>1989-12-17</td>\n",
       "      <td>2050-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d003</td>\n",
       "      <td>110183</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>1992-03-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  dept_no  emp_no  from_date    to_date\n",
       "0    d001  110022 1985-01-01 1991-10-01\n",
       "1    d001  110039 1991-10-01 2050-12-31\n",
       "2    d002  110085 1985-01-01 1989-12-17\n",
       "3    d002  110114 1989-12-17 2050-12-31\n",
       "4    d003  110183 1985-01-01 1992-03-21"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Replace null dates\n",
    "dept_manager.to_date = dept_manager['to_date'].fillna(pd.to_datetime('2050-12-31'))\n",
    "dept_manager.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emp_no</th>\n",
       "      <th>title</th>\n",
       "      <th>from_date</th>\n",
       "      <th>to_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1986-06-26</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>Staff</td>\n",
       "      <td>1996-08-03</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1995-12-03</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>1986-12-01</td>\n",
       "      <td>1995-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1995-12-01</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   emp_no            title  from_date    to_date\n",
       "0   10001  Senior Engineer 1986-06-26        NaT\n",
       "1   10002            Staff 1996-08-03        NaT\n",
       "2   10003  Senior Engineer 1995-12-03        NaT\n",
       "3   10004         Engineer 1986-12-01 1995-12-01\n",
       "4   10004  Senior Engineer 1995-12-01        NaT"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Import the Titles table\n",
    "titles = pd.read_sql('select * from titles', connection, parse_dates=['from_date', 'to_date'])\n",
    "titles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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</th>\n",
       "      <th>from_date</th>\n",
       "      <th>to_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1986-06-26</td>\n",
       "      <td>2050-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>Staff</td>\n",
       "      <td>1996-08-03</td>\n",
       "      <td>2050-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1995-12-03</td>\n",
       "      <td>2050-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>1986-12-01</td>\n",
       "      <td>1995-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1995-12-01</td>\n",
       "      <td>2050-12-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emp_no            title  from_date    to_date\n",
       "0   10001  Senior Engineer 1986-06-26 2050-12-31\n",
       "1   10002            Staff 1996-08-03 2050-12-31\n",
       "2   10003  Senior Engineer 1995-12-03 2050-12-31\n",
       "3   10004         Engineer 1986-12-01 1995-12-01\n",
       "4   10004  Senior Engineer 1995-12-01 2050-12-31"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Replace null dates\n",
    "titles.to_date = titles['to_date'].fillna(pd.to_datetime('2050-12-31'))\n",
    "titles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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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</th>\n",
       "      <th>from_date_x</th>\n",
       "      <th>to_date_x</th>\n",
       "      <th>salary</th>\n",
       "      <th>from_date_y</th>\n",
       "      <th>to_date_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1986-06-26</td>\n",
       "      <td>2050-12-31</td>\n",
       "      <td>60117</td>\n",
       "      <td>1986-06-26</td>\n",
       "      <td>1987-06-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>Staff</td>\n",
       "      <td>1996-08-03</td>\n",
       "      <td>2050-12-31</td>\n",
       "      <td>65828</td>\n",
       "      <td>1996-08-03</td>\n",
       "      <td>1997-08-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1995-12-03</td>\n",
       "      <td>2050-12-31</td>\n",
       "      <td>40006</td>\n",
       "      <td>1995-12-03</td>\n",
       "      <td>1996-12-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>1986-12-01</td>\n",
       "      <td>1995-12-01</td>\n",
       "      <td>40054</td>\n",
       "      <td>1986-12-01</td>\n",
       "      <td>1987-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1995-12-01</td>\n",
       "      <td>2050-12-31</td>\n",
       "      <td>40054</td>\n",
       "      <td>1986-12-01</td>\n",
       "      <td>1987-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emp_no            title from_date_x  to_date_x  salary from_date_y  \\\n",
       "0   10001  Senior Engineer  1986-06-26 2050-12-31   60117  1986-06-26   \n",
       "1   10002            Staff  1996-08-03 2050-12-31   65828  1996-08-03   \n",
       "2   10003  Senior Engineer  1995-12-03 2050-12-31   40006  1995-12-03   \n",
       "3   10004         Engineer  1986-12-01 1995-12-01   40054  1986-12-01   \n",
       "4   10004  Senior Engineer  1995-12-01 2050-12-31   40054  1986-12-01   \n",
       "\n",
       "   to_date_y  \n",
       "0 1987-06-26  \n",
       "1 1997-08-03  \n",
       "2 1996-12-02  \n",
       "3 1987-12-01  \n",
       "4 1987-12-01  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Create a merged dataframe of titles and salaries\n",
    "employee_salaries = titles.merge(salaries, on='emp_no')\n",
    "employee_salaries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>60117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>Staff</td>\n",
       "      <td>65828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>40006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>40054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>40054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emp_no            title  salary\n",
       "0   10001  Senior Engineer   60117\n",
       "1   10002            Staff   65828\n",
       "2   10003  Senior Engineer   40006\n",
       "3   10004         Engineer   40054\n",
       "4   10004  Senior Engineer   40054"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Creating Dataframe with emp_no, title, salary\n",
    "employee_salaries_df = employee_salaries[['emp_no', 'title', 'salary']]\n",
    "employee_salaries_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>title</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>60117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>Staff</td>\n",
       "      <td>65828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>40006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>40054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>40054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emp_no            title  salary\n",
       "0   10001  Senior Engineer   60117\n",
       "1   10002            Staff   65828\n",
       "2   10003  Senior Engineer   40006\n",
       "3   10004         Engineer   40054\n",
       "4   10004  Senior Engineer   40054"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee_salaries_df.groupby('title')['salary'].mean().round(2)\n",
    "employee_salaries_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001BA6E395F98>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEICAYAAACqMQjAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAU6klEQVR4nO3df7DldX3f8ecrEAihKiBxhwCTxcnWhsrE6BaW2mlvpbMsmAlkRmegxF2VzLYInSalUyHOFKtNB9vSZDApsqlbF4v8qI2FUex2B72T6YwaoFoWo5QrbmFlBXERWYhJVt/943xWz72cz713z+79sXefj5nv3O95fz/fH+dzvnte+/1xzklVIUnSKD+11BsgSVq+DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEtICSlJJfnGpt0MalyEhSeoyJKRlKMmxS70NEhgS0rwleW+SbyV5IcmjSS5Icm6SLyT5XpI9Sf4gyXGd+d+a5MtJvp/kySTvH5q2up2aujLJE8DnknwmyT+ZsYyHk1y6sM9U+glDQpqHJK8DrgH+VlW9ArgQ2AX8EPht4FTgfOAC4D2dxbwIbAROAt4KXDXiDf/vAb/Ulr8N+I2hbfhl4HTgvsPypKR5MCSk+fkhcDxwdpKfrqpdVfWNqnqoqr5YVfurahdwK4M3+pepqsmq2llVP6qqh4E7RrR9f1W9WFV/DtwDrEmypk17B3BXVf3lQjxBaRRDQpqHqpoCfgt4P/BMkjuT/HySv57k00m+neT7wL9hcFTxMknOS/L5JN9J8jzwj0e0fXJonX8B3A38RpKfAi4HPn7Yn5w0C0NCmqeq+kRV/R3gF4ACPgTcAnwdWFNVrwR+B0hnEZ8A7gXOrKpXAR8Z0Xbm1zJvA65gcBrrpar6wuF4LtJ8GRLSPCR5XZK3JDke+AHw5wxOQb0C+D6wL8nfAK6aZTGvAPZW1Q+SnAv8w7nW20LhR8BNeBShJWBISPNzPHAj8CzwbeA1DI4a/jmDN/sXgD8C7pplGe8BPpDkBeBfMjiVNB+3AecA/2WsLZcOQfzRIWl5S7IR2NxOdUmLyiMJaRlL8rMMjkC2LPW26OhkSEjLVJILge8ATzO46C0tOk83SZK6PJKQJHWtuC8RO/XUU2v16tVjzfviiy9y4oknHt4NOoLZH9PZH9PZH9Md6f3x0EMPPVtVPzezvuJCYvXq1Tz44INjzTs5OcnExMTh3aAjmP0xnf0xnf0x3ZHeH0n+36i6p5skSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldK+4T14di57ee553XfWZJ1r3rxrcuyXolaTYeSUiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK65gyJJGcm+XySryX5apJ/2uqnJNmR5LH29+RWT5Kbk0wleTjJG4eWtam1fyzJpqH6m5LsbPPcnCSzrUOStDjmcySxH7i2qn4JWAdcneRs4Drg/qpaA9zfHgNcBKxpw2bgFhi84QM3AOcB5wI3DL3p39LaHphvQ6v31iFJWgRzhkRV7amq/93GXwC+BpwOXAJsa822AZe28UuA22rgi8BJSU4DLgR2VNXeqnoO2AFsaNNeWVVfqKoCbpuxrFHrkCQtgoO6JpFkNfArwJeAVVW1BwZBArymNTsdeHJott2tNlt994g6s6xDkrQIjp1vwyR/DfhvwG9V1ffbZYORTUfUaoz6vCXZzOB0FatWrWJycvJgZv+xVSfAtefsH2veQzXuNi+kffv2LcvtWir2x3T2x3QrtT/mFRJJfppBQNxeVX/cyk8nOa2q9rRTRs+0+m7gzKHZzwCeavWJGfXJVj9jRPvZ1jFNVW0BtgCsXbu2JiYmRjWb04dvv4ebds47Nw+rXVdMLMl6ZzM5Ocm4fbkS2R/T2R/TrdT+mM/dTQE+Cnytqv7D0KR7gQN3KG0C7hmqb2x3Oa0Dnm+nirYD65Oc3C5Yrwe2t2kvJFnX1rVxxrJGrUOStAjm89/mNwPvAHYm+Uqr/Q5wI3B3kiuBJ4C3t2n3ARcDU8BLwLsAqmpvkg8CD7R2H6iqvW38KuBjwAnAZ9vALOuQJC2COUOiqv4Xo68bAFwwon0BV3eWtRXYOqL+IPD6EfXvjlqHJGlx+IlrSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqSuOUMiydYkzyR5ZKj2/iTfSvKVNlw8NO36JFNJHk1y4VB9Q6tNJbluqH5Wki8leSzJXUmOa/Xj2+OpNn314XrSkqT5mc+RxMeADSPqv1dVb2jDfQBJzgYuA/5mm+c/JjkmyTHAHwIXAWcDl7e2AB9qy1oDPAdc2epXAs9V1S8Cv9faSZIW0ZwhUVV/Auyd5/IuAe6sqr+oqm8CU8C5bZiqqser6i+BO4FLkgR4C/DJNv824NKhZW1r458ELmjtJUmL5NhDmPeaJBuBB4Frq+o54HTgi0NtdrcawJMz6ucBrwa+V1X7R7Q//cA8VbU/yfOt/bMzNyTJZmAzwKpVq5icnBzrCa06Aa49Z//cDRfAuNu8kPbt27cst2up2B/T2R/TrdT+GDckbgE+CFT7exPwbmDU//SL0UcsNUt75pg2vVi1BdgCsHbt2pqYmJhl0/s+fPs93LTzUHJzfLuumFiS9c5mcnKScftyJbI/prM/plup/THW3U1V9XRV/bCqfgT8EYPTSTA4EjhzqOkZwFOz1J8FTkpy7Iz6tGW16a9i/qe9JEmHwVghkeS0oYe/Dhy48+le4LJ2Z9JZwBrgT4EHgDXtTqbjGFzcvreqCvg88LY2/ybgnqFlbWrjbwM+19pLkhbJnOdWktwBTACnJtkN3ABMJHkDg9M/u4B/BFBVX01yN/BnwH7g6qr6YVvONcB24Bhga1V9ta3ivcCdSf418GXgo63+UeDjSaYYHEFcdsjPVpJ0UOYMiaq6fET5oyNqB9r/LvC7I+r3AfeNqD/OT05XDdd/ALx9ru2TJC0cP3EtSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkrjlDIsnWJM8keWSodkqSHUkea39PbvUkuTnJVJKHk7xxaJ5Nrf1jSTYN1d+UZGeb5+YkmW0dkqTFM58jiY8BG2bUrgPur6o1wP3tMcBFwJo2bAZugcEbPnADcB5wLnDD0Jv+La3tgfk2zLEOSdIimTMkqupPgL0zypcA29r4NuDSofptNfBF4KQkpwEXAjuqam9VPQfsADa0aa+sqi9UVQG3zVjWqHVIkhbJsWPOt6qq9gBU1Z4kr2n104Enh9rtbrXZ6rtH1Gdbx8sk2czgaIRVq1YxOTk53pM6Aa49Z/9Y8x6qcbd5Ie3bt29ZbtdSsT+msz+mW6n9MW5I9GRErcaoH5Sq2gJsAVi7dm1NTEwc7CIA+PDt93DTzsPdJfOz64qJJVnvbCYnJxm3L1ci+2M6+2O6ldof497d9HQ7VUT7+0yr7wbOHGp3BvDUHPUzRtRnW4ckaZGMGxL3AgfuUNoE3DNU39jucloHPN9OGW0H1ic5uV2wXg9sb9NeSLKu3dW0ccayRq1DkrRI5jy3kuQOYAI4NcluBncp3QjcneRK4Ang7a35fcDFwBTwEvAugKram+SDwAOt3Qeq6sDF8KsY3EF1AvDZNjDLOiRJi2TOkKiqyzuTLhjRtoCrO8vZCmwdUX8QeP2I+ndHrUOStHj8xLUkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6luZn2PQyq6/7zJKsd9eNb12S9Uo6MngkIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlz86dJSb7ceOrj1nP+9coB9D8seOpCODRxKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYcUEkl2JdmZ5CtJHmy1U5LsSPJY+3tyqyfJzUmmkjyc5I1Dy9nU2j+WZNNQ/U1t+VNt3hzK9kqSDs7hOJL4+1X1hqpa2x5fB9xfVWuA+9tjgIuANW3YDNwCg1ABbgDOA84FbjgQLK3N5qH5NhyG7ZUkzdNCnG66BNjWxrcBlw7Vb6uBLwInJTkNuBDYUVV7q+o5YAewoU17ZVV9oaoKuG1oWZKkRXCon7gu4H8mKeDWqtoCrKqqPQBVtSfJa1rb04Enh+bd3Wqz1XePqL9Mks0MjjhYtWoVk5OTYz2ZVScMPmWsgYXsj3Ffo6W0b9++I3K7F4r9Md1K7Y9DDYk3V9VTLQh2JPn6LG1HXU+oMeovLw7CaQvA2rVra2JiYtaN7vnw7fdw006/qeSAa8/Zv2D9seuKiQVZ7kKanJxk3H1rJbI/plup/XFIp5uq6qn29xngUwyuKTzdThXR/j7Tmu8Gzhya/QzgqTnqZ4yoS5IWydghkeTEJK84MA6sBx4B7gUO3KG0Cbinjd8LbGx3Oa0Dnm+npbYD65Oc3C5Yrwe2t2kvJFnX7mraOLQsSdIiOJRzCauAT7W7Uo8FPlFV/yPJA8DdSa4EngDe3trfB1wMTAEvAe8CqKq9ST4IPNDafaCq9rbxq4CPAScAn22DJGmRjB0SVfU48Msj6t8FLhhRL+DqzrK2AltH1B8EXj/uNkqSDo2fuJYkdRkSkqQuQ0KS1OWHArQkZvvZ1IXmT6dK8+eRhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSery9yR01Bn3tyyuPWc/7zyE38Hwdyx0JPJIQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpff3SQtknG/M+pw8HujNC6PJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1OXdTdJRYCHurJrPL/V5V9WRb9kfSSTZkOTRJFNJrlvq7ZGko8myDokkxwB/CFwEnA1cnuTspd0qSTp6LPfTTecCU1X1OECSO4FLgD9b0q2SNC9+gPDIl6pa6m3oSvI2YENV/WZ7/A7gvKq6Zka7zcDm9vB1wKNjrvJU4Nkx512J7I/p7I/p7I/pjvT++IWq+rmZxeV+JJERtZelWlVtAbYc8sqSB6tq7aEuZ6WwP6azP6azP6Zbqf2xrK9JALuBM4cenwE8tUTbIklHneUeEg8Aa5KcleQ44DLg3iXeJkk6aizr001VtT/JNcB24Bhga1V9dQFXecinrFYY+2M6+2M6+2O6Fdkfy/rCtSRpaS33002SpCVkSEiSulZsSCQ5JsmXk3y6PT4ryZeSPJbkrnYhnCTHt8dTbfrqoWVc3+qPJrlwqH5EfVVIkpOSfDLJ15N8Lcn5SU5JsqP1x44kJ7e2SXJze24PJ3nj0HI2tfaPJdk0VH9Tkp1tnpuTjLp1edlI8ttJvprkkSR3JPmZo23/SLI1yTNJHhmqLfg+0VvHUur0xb9r/14eTvKpJCcNTTuo132cfWtZqaoVOQD/DPgE8On2+G7gsjb+EeCqNv4e4CNt/DLgrjZ+NvB/gOOBs4BvMLh4fkwbfy1wXGtz9lI/3zn6Yhvwm238OOAk4N8C17XadcCH2vjFwGcZfEZlHfClVj8FeLz9PbmNn9ym/Slwfpvns8BFS/2cZ+mL04FvAicM7RfvPNr2D+DvAm8EHhmqLfg+0VvHMuyL9cCxbfxDQ31x0K/7we5by21Y8g1YoBf9DOB+4C3Ap9uO+uzQi34+sL2NbwfOb+PHtnYBrgeuH1rm9jbfj+dt9WntltsAvJLBm2Jm1B8FTmvjpwGPtvFbgctntgMuB24dqt/aaqcBXx+qT2u33AYGIfFke2M7tu0fFx6N+wewesYb44LvE711LPUwsy9mTPt14PZRr+dcr/s47z1L3Rczh5V6uun3gX8B/Kg9fjXwvara3x7vZvBmAT9506BNf761/3F9xjy9+nL1WuA7wH/O4PTbf0pyIrCqqvYAtL+vae0P9nmf3sZn1pelqvoW8O+BJ4A9DF7vhzh6949hi7FP9NaxnL2bwdEQHHxfjPPes6ysuJBI8qvAM1X10HB5RNOaY9rB1perYxkcSt9SVb8CvMjgML9nRfdHOwd+CYNTBT8PnMjgW4ZnOlr2j/k4avsgyfuA/cDtB0ojmo3bF0dEP624kADeDPxakl3AnQxOOf0+cFKSAx8eHP56jx9/9Ueb/ipgL/2vBDnSvipkN7C7qr7UHn+SQWg8neQ0gPb3maH2B/O8d7fxmfXl6h8A36yq71TVXwF/DPxtjt79Y9hi7BO9dSw77UL8rwJXVDsnxMH3xbMc/L61rKy4kKiq66vqjKpazeBi0Oeq6grg88DbWrNNwD1t/N72mDb9c22HuBe4rN2BcBawhsHFuCPqq0Kq6tvAk0le10oXMPiq9eHnPbM/NrY7WtYBz7fTAtuB9UlObv8bX8/g3Ooe4IUk69odLBuHlrUcPQGsS/KzbXsP9MdRuX/MsBj7RG8dy0qSDcB7gV+rqpeGJh3U6972lYPdt5aXpb4ospADMMFP7m56LYMXcwr4r8Dxrf4z7fFUm/7aofnfx+COhUcZumOHwd0e/7dNe99SP8959MMbgAeBh4H/zuBOlFczuLj/WPt7SmsbBj/09A1gJ7B2aDnvbv00BbxrqL4WeKTN8wcsw4tvM/rjXwFfb9v8cQZ3qhxV+wdwB4NrMn/F4H+0Vy7GPtFbxzLsiykG1wu+0oaPjPu6j7NvLafBr+WQJHWtuNNNkqTDx5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6vr/SVaY+VdEEbIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "employee_salaries_df.hist(column='salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Assistant Engineer    48493.204786\n",
       "Engineer              48539.781423\n",
       "Manager               51531.041667\n",
       "Senior Engineer       48506.751806\n",
       "Senior Staff          58503.286614\n",
       "Staff                 58465.271904\n",
       "Technique Leader      48580.505772\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees_grouped_by_title = employee_salaries_df.groupby(['title'])['salary'].mean()\n",
    "employees_grouped_by_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba6d7342b0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "employees_grouped_by_title.plot.bar()"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}