Average Salary by Title.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine('postgres://postgres:jbm713177@localhost:5432/Employee_DB')\n",
    "conn = engine.connect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a bar chart of average salary by title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All records in salaries table\n",
    "salaries_df = pd.read_sql(\"SELECT * FROM salaries\", conn)\n",
    "salaries_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All records in titles table\n",
    "titles_df = pd.read_sql(\"SELECT * FROM titles\", conn)\n",
    "titles_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "      <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",
       "    </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",
       "      <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine dataframes\n",
    "combined_df = pd.merge(salaries_df, titles_df, on=\"emp_no\", how=\"inner\")\n",
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>title</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Assistant Engineer</td>\n",
       "      <td>48493.204786</td>\n",
       "    </tr>\n",
       "    <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",
       "</div>"
      ],
      "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": [
    "# Find mean of salary by job title\n",
    "grouped_df = combined_df.groupby(\"title\").mean()\n",
    "middle_df = grouped_df.drop(columns = \"emp_no\")\n",
    "revised_data = middle_df.reset_index()\n",
    "revised_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting x-axis, y-axis, and ticks\n",
    "x_axis = revised_data['title']\n",
    "y_axis = revised_data['salary']\n",
    "x_ticks = np.arange(len(x_axis))\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "\n",
    "plt.bar(x_axis, y_axis, align='center', alpha=0.77, color=\n",
    "        ['k', 'r', 'g', 'm', 'b', 'c', 'y'])\n",
    "plt.xticks(x_ticks, x_axis, fontsize=13, rotation='70')\n",
    "plt.yticks(fontsize=13)\n",
    "\n",
    "plt.title(\"Average Employee Salary \\n By Job Title\", size=20)\n",
    "plt.xlabel(\"Employee Title\", size=16)\n",
    "plt.ylabel(\"Salary (USD $)\", size=16)\n",
    "\n",
    "plt.savefig(\"Average_Salary_by_Title.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Engineering Technical Report\n",
    "\n",
    "1. Designed Entity Relationship Diagram (ERD) of tables from CSV files\n",
    "2. Created a table schema for each CSV file; specify data types, primary keys, and foreign keys\n",
    "3. Imported CSV files into corresponding SQL tables in order to run queries for report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:PythonData] *",
   "language": "python",
   "name": "conda-env-PythonData-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}