employee_db (Bonus).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Mystery in Two Parts - Bonus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Employee Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For PostGREs and SQLAlchemy\n",
    "!pip install psycopg2 sqlalchemy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "# SQL Alchemy\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "# Pandas\n",
    "import pandas as pd\n",
    "\n",
    "# Matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# NumPy\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/josephyon/anaconda3/lib/python3.7/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
      "  \"\"\")\n"
     ]
    }
   ],
   "source": [
    "# Create Engine and Pass in Postgres Connection\n",
    "# Setup to Connect to Database \n",
    "engine = create_engine('postgres://postgres:password1234@localhost:5432/employee_db')\n",
    "conn = engine.connect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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",
       "</div>"
      ],
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query All Records in the Salaries Table\n",
    "salaries_data = pd.read_sql(\"SELECT * FROM salaries\", conn)\n",
    "salaries_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>9999-01-01</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>9999-01-01</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>9999-01-01</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>9999-01-01</td>\n",
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      "text/plain": [
       "   emp_no            title   from_date     to_date\n",
       "0   10001  Senior Engineer  1986-06-26  9999-01-01\n",
       "1   10002            Staff  1996-08-03  9999-01-01\n",
       "2   10003  Senior Engineer  1995-12-03  9999-01-01\n",
       "3   10004         Engineer  1986-12-01  1995-12-01\n",
       "4   10004  Senior Engineer  1995-12-01  9999-01-01"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query All Records in the Titles Table\n",
    "titles_data = pd.read_sql(\"SELECT * FROM titles\", conn)\n",
    "titles_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>emp_no</th>\n",
       "      <th>salary</th>\n",
       "      <th>from_date_x</th>\n",
       "      <th>to_date_x</th>\n",
       "      <th>title</th>\n",
       "      <th>from_date_y</th>\n",
       "      <th>to_date_y</th>\n",
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       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>60117</td>\n",
       "      <td>1986-06-26</td>\n",
       "      <td>1987-06-26</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1986-06-26</td>\n",
       "      <td>9999-01-01</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",
       "      <td>Staff</td>\n",
       "      <td>1996-08-03</td>\n",
       "      <td>9999-01-01</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",
       "      <td>Senior Engineer</td>\n",
       "      <td>1995-12-03</td>\n",
       "      <td>9999-01-01</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",
       "      <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>40054</td>\n",
       "      <td>1986-12-01</td>\n",
       "      <td>1987-12-01</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>1995-12-01</td>\n",
       "      <td>9999-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emp_no  salary from_date_x   to_date_x            title from_date_y  \\\n",
       "0   10001   60117  1986-06-26  1987-06-26  Senior Engineer  1986-06-26   \n",
       "1   10002   65828  1996-08-03  1997-08-03            Staff  1996-08-03   \n",
       "2   10003   40006  1995-12-03  1996-12-02  Senior Engineer  1995-12-03   \n",
       "3   10004   40054  1986-12-01  1987-12-01         Engineer  1986-12-01   \n",
       "4   10004   40054  1986-12-01  1987-12-01  Senior Engineer  1995-12-01   \n",
       "\n",
       "    to_date_y  \n",
       "0  9999-01-01  \n",
       "1  9999-01-01  \n",
       "2  9999-01-01  \n",
       "3  1995-12-01  \n",
       "4  9999-01-01  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge Columns / Combine Data\n",
    "combined_data = pd.merge(salaries_data, titles_data, on=\"emp_no\", how=\"inner\")\n",
    "combined_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emp_no</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>Assistant Engineer</th>\n",
       "      <td>251495.398533</td>\n",
       "      <td>48493.204786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineer</th>\n",
       "      <td>252943.159987</td>\n",
       "      <td>48539.781423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manager</th>\n",
       "      <td>110780.833333</td>\n",
       "      <td>51531.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Engineer</th>\n",
       "      <td>253034.375949</td>\n",
       "      <td>48506.751806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Staff</th>\n",
       "      <td>253423.367183</td>\n",
       "      <td>58503.286614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff</th>\n",
       "      <td>253399.240672</td>\n",
       "      <td>58465.271904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technique Leader</th>\n",
       "      <td>251708.951052</td>\n",
       "      <td>48580.505772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                           emp_no        salary\n",
       "title                                          \n",
       "Assistant Engineer  251495.398533  48493.204786\n",
       "Engineer            252943.159987  48539.781423\n",
       "Manager             110780.833333  51531.041667\n",
       "Senior Engineer     253034.375949  48506.751806\n",
       "Senior Staff        253423.367183  58503.286614\n",
       "Staff               253399.240672  58465.271904\n",
       "Technique Leader    251708.951052  48580.505772"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby Title\n",
    "grouped_df = combined_data.groupby(\"title\").mean()\n",
    "grouped_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>salary</th>\n",
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       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Assistant Engineer</th>\n",
       "      <td>48493.204786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineer</th>\n",
       "      <td>48539.781423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manager</th>\n",
       "      <td>51531.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Engineer</th>\n",
       "      <td>48506.751806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Staff</th>\n",
       "      <td>58503.286614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staff</th>\n",
       "      <td>58465.271904</td>\n",
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       "    <tr>\n",
       "      <th>Technique Leader</th>\n",
       "      <td>48580.505772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                          salary\n",
       "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"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop emp_no from grouped_df to Show Only title & salary\n",
    "revised_grouped_df = grouped_df.drop(columns = \"emp_no\")\n",
    "revised_grouped_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Assistant Engineer</td>\n",
       "      <td>48493.204786</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Engineer</td>\n",
       "      <td>48539.781423</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>Senior Engineer</td>\n",
       "      <td>48506.751806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Senior Staff</td>\n",
       "      <td>58503.286614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Staff</td>\n",
       "      <td>58465.271904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Technique Leader</td>\n",
       "      <td>48580.505772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                title        salary\n",
       "0  Assistant Engineer  48493.204786\n",
       "1            Engineer  48539.781423\n",
       "2             Manager  51531.041667\n",
       "3     Senior Engineer  48506.751806\n",
       "4        Senior Staff  58503.286614\n",
       "5               Staff  58465.271904\n",
       "6    Technique Leader  48580.505772"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset Index\n",
    "revised_grouped_df = revised_grouped_df.reset_index()\n",
    "revised_grouped_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set x_axis, y_axis & Tick Locations\n",
    "x_axis = revised_grouped_df[\"title\"]\n",
    "ticks = np.arange(len(x_axis))\n",
    "y_axis = revised_grouped_df[\"salary\"]\n",
    " \n",
    "# Create Bar Chart Based on Above Data\n",
    "plt.bar(x_axis, y_axis, align=\"center\", alpha=0.5, color=[\"k\", \"r\", \"g\", \"m\", \"b\", \"c\", \"y\"])\n",
    "\n",
    "# Create Ticks for Bar Chart's x_axis\n",
    "plt.xticks(ticks, x_axis, rotation=\"vertical\")\n",
    "\n",
    "# Set Labels & Title\n",
    "plt.ylabel(\"Salaries ($)\")\n",
    "plt.xlabel(\"Employee Titles\")\n",
    "plt.title(\"Average Employee Salary by Title\")\n",
    "\n",
    "# Save Figure\n",
    "plt.savefig(\"./Images/average_salary_by_title.png\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Technical Report Outlining Data Engineering Steps Taken:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Sketched out Entity Relationship Diagram (ERD) of employee database tables; specifying data types, primary keys and foreign keys.\n",
    "2. Exported ERD to PostgreSQL thus creating table schemas and contraints for each of the six CSV files.\n",
    "3. Imported CSV files to each corresponding SQL table."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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