DataAnalysisandVisualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "import pymysql\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams[\"figure.figsize\"] = (18,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>salary</th>\n",
       "      <th>title</th>\n",
       "      <th>dept_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>Georgi</td>\n",
       "      <td>Facello</td>\n",
       "      <td>M</td>\n",
       "      <td>60117</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>Bezalel</td>\n",
       "      <td>Simmel</td>\n",
       "      <td>F</td>\n",
       "      <td>65828</td>\n",
       "      <td>Staff</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>Parto</td>\n",
       "      <td>Bamford</td>\n",
       "      <td>M</td>\n",
       "      <td>40006</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>Production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>Chirstian</td>\n",
       "      <td>Koblick</td>\n",
       "      <td>M</td>\n",
       "      <td>40054</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>Production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>Chirstian</td>\n",
       "      <td>Koblick</td>\n",
       "      <td>M</td>\n",
       "      <td>40054</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>Production</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10005</td>\n",
       "      <td>Kyoichi</td>\n",
       "      <td>Maliniak</td>\n",
       "      <td>M</td>\n",
       "      <td>78228</td>\n",
       "      <td>Senior Staff</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10005</td>\n",
       "      <td>Kyoichi</td>\n",
       "      <td>Maliniak</td>\n",
       "      <td>M</td>\n",
       "      <td>78228</td>\n",
       "      <td>Staff</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10006</td>\n",
       "      <td>Anneke</td>\n",
       "      <td>Preusig</td>\n",
       "      <td>F</td>\n",
       "      <td>40000</td>\n",
       "      <td>Senior Engineer</td>\n",
       "      <td>Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10007</td>\n",
       "      <td>Tzvetan</td>\n",
       "      <td>Zielinski</td>\n",
       "      <td>F</td>\n",
       "      <td>56724</td>\n",
       "      <td>Senior Staff</td>\n",
       "      <td>Research</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10007</td>\n",
       "      <td>Tzvetan</td>\n",
       "      <td>Zielinski</td>\n",
       "      <td>F</td>\n",
       "      <td>56724</td>\n",
       "      <td>Staff</td>\n",
       "      <td>Research</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emp_no first_name  last_name gender  salary            title  \\\n",
       "0   10001     Georgi    Facello      M   60117  Senior Engineer   \n",
       "1   10002    Bezalel     Simmel      F   65828            Staff   \n",
       "2   10003      Parto    Bamford      M   40006  Senior Engineer   \n",
       "3   10004  Chirstian    Koblick      M   40054         Engineer   \n",
       "4   10004  Chirstian    Koblick      M   40054  Senior Engineer   \n",
       "5   10005    Kyoichi   Maliniak      M   78228     Senior Staff   \n",
       "6   10005    Kyoichi   Maliniak      M   78228            Staff   \n",
       "7   10006     Anneke    Preusig      F   40000  Senior Engineer   \n",
       "8   10007    Tzvetan  Zielinski      F   56724     Senior Staff   \n",
       "9   10007    Tzvetan  Zielinski      F   56724            Staff   \n",
       "\n",
       "         dept_name  \n",
       "0      Development  \n",
       "1            Sales  \n",
       "2       Production  \n",
       "3       Production  \n",
       "4       Production  \n",
       "5  Human Resources  \n",
       "6  Human Resources  \n",
       "7      Development  \n",
       "8         Research  \n",
       "9         Research  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_connection_str = 'mysql+pymysql://root:iamroot@localhost/sql_challenge'\n",
    "db_connection = create_engine(db_connection_str)\n",
    "\n",
    "# Custom SQL to extract desired result set from MySQL database\n",
    "df = pd.read_sql('SELECT e.emp_no, e.first_name, e.last_name, e.gender, s.salary, t.title, d.dept_name \\\n",
    "                    FROM Employees e \\\n",
    "                    LEFT JOIN Salaries s ON e.emp_no=s.emp_no \\\n",
    "                    LEFT JOIN Titles t ON e.emp_no=t.emp_no \\\n",
    "                    LEFT JOIN (SELECT * FROM Dept_emp UNION ALL SELECT * FROM Dept_manager) t1 ON t1.emp_no=e.emp_no \\\n",
    "                    LEFT JOIN Departments d ON t1.dept_no=d.dept_no',\n",
    "                 con=db_connection)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a histogram to visualize the most common salary ranges for employees. Based on the data, a majority of employees\n",
    "# earns between $40-50k\n",
    "\n",
    "n_bin = 10\n",
    "x = df[\"salary\"]\n",
    "fig1 = plt.hist(x, bins=n_bin, color='teal')\n",
    "\n",
    "plt.savefig(\"histogram.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y0dHRWrp0qSRp6dKlatGihdmelJSkrKwsHTp0SPv371etWrUUFBSkMmXKaNu2bTIMQ8uWLVN0dLQkqXnz5lqyZIkkaeXKlWrQoMEFR0IAAAAAAICSq8iREG+88YY2bdqkkydP6oknnlCPHj3UtWtXTZ48WYmJiQoNDdWQIUMkSZUrV1br1q01ZMgQeXh4qE+fPvLwOJfn6Nu3r2bMmKHMzExFRUWpadOmkqROnTpp2rRpGjhwoPz8/DRo0CDX7S0AAAAAALBNkUmICyUFRo4cWWh7t27d1K1btwLtNWvWVHx8fIF2Hx8fM4kBAAAAAACuXZc1HQMAAAAAAOBSkYQAAAAAAACWIAkBAAAAAAAsQRICAAAAAABYgiQEAAAAAACwBEkIAAAAAABgCZIQAAAAAADAEiQhAAAAAACAJUhCAAAAAAAAS5CEAAAAAAAAliAJAQAAAAAALEESAgAAAAAAWIIkBAAAAAAAsARJCAAAAAAAYAmSEAAAAAAAwBIkIQAAAAAAgCVIQgAAAAAAAEuQhAAAAAAAAJYgCQEAAAAAACxBEgIAAAAAAFiCJAQAAAAAALAESQgAAAAAAGAJkhAAAAAAAMASJCEAAAAAAIAlSEIAAAAAAABLkIQAAAAAAACWIAkBAAAAAAAsQRICAAAAAABYgiQEAAAAAACwBEkIAAAAAABgCZIQAAAAAADAEiQhAAAAAACAJUhCAAAAAAAAS5CEAAAAAAAAliAJAQAAAAAALEESAgAAAAAAWMLL7gDyrF27VnPmzFFubq46d+6srl272h0SAAAAAAC4itxiJERubq5mzZqlF198UZMnT9by5cu1Z88eu8MCAAAAAABXkVskIbZv366IiAiVL19eXl5eatOmjZKTk+0OCwAAAAAAXEUOwzAMu4NYuXKl1q5dqyeeeEKStGzZMv3555/q06eP03YJCQlKSEiQJI0bN87yOAEAnIsBwG6chwGUZG4xEqKwPIjD4SjQFhsbq3Hjxtl2sn3++edt6be43Dk+d45Ncu/43Dk2yb3jc+fYSjK7z8UlDcchrjaOKdhxHrbruLPzeGefr/1+7ez7etznPG6RhAgJCdHRo0fN50ePHlVQUJCNEQEAAAAAgKvNLZIQNWvW1P79+3Xo0CFlZ2crKSlJ0dHRdocFAAAAAACuIrdYotPT01OPPvqoxo4dq9zcXN10002qXLmy3WEVEBsba3cIF+XO8blzbJJ7x+fOsUnuHZ87x4brB8chrjaOKdjBruPOzuOdfb72+7Wz7+txn/O4RWFKAAAAAABw7XOL6RgAAAAAAODaRxICAAAAAABYwi1qQgAAcK257777VKVKFfP5c889p/DwcBsjQknWo0cPtW/fXgMHDpQk5eTk6PHHH1ft2rVtX2oNAIBLQRKiGMaMGaM2bdqodevW8vPzszucQv3zn/+Ut7e3HA6HJOns2bPy8fGRw+HQmTNn9OGHH9oWW8+ePWUYhhmbJOWVIjl79qytsaWkpKh06dKKjIyUJJ05c0br169XuXLlVKdOHdvicvfYJPeP70JOnTrltn/HuLb4+Pho4sSJdoeBa0SpUqW0e/duZWZmysfHR+vXr1dwcLDdYeE6snHjRu3evVuSVLlyZTVs2NDlfZ5/fZufq69vz79+dadra1ex65o9f7/5P2fJ9b9nSdqwYYN2794th8Nh2bFt1z67y/U7SYhi6Nq1q3799VfNmzdPDRs2VNu2bdWiRQuVLl3a7tBMVapU0fjx483nw4YNM58PGzbMrrAkSfPmzXN6vmvXLiUlJen333+3fRWUOXPmaMCAAZLOnWRHjhyp0NBQnTx5Uk2bNlW3bt2IrYTGdyEvv/yyJkyYYHcYAHDJoqKitGbNGrVq1UrLly9X27ZttWXLFrvDwjXu2LFjmjhxory8vFSzZk1J0qpVq7Ro0SI9++yzKleunMv6Pv/6Nj9XX9+ef/1qxbV1YYmPUqVKSTp3rfXee++5pN88dl2z5+83/+ec99xVUlNTNXHiRPn4+Dgd2wsXLtSzzz7r0kSvXfvsLtfvJCGKoUmTJmrSpInWrl2ruLg4LV++XAsXLlSdOnXUtm1btWzZ0u4Qdf4iJ/mfu8MCKKmpqUpMTNRvv/2msLAwtW3bVt27d5e3t7etcaWnpysiIkKStHnzZmVmZmro0KHKycnR0KFDbf0i7c6xlYT4LqSwuymAK2RmZuq5556TJIWHh5uPgcvVtm1bffLJJ2rWrJl27typm266iSQEXG7OnDnq3LmzOnXq5NSekJCguXPnatCgQS7rOzs7+7JecwUrrq0vlviwijtcs+fk5MjT01OSa3/P7777rm699VZ16NDBqX3p0qV69913NXToUJf1nScjI0OHDh1Sbm6uPDzOlWt05T67y/U7SYhL4HA41KhRIzVq1EgHDhzQW2+9pfj4eLcYjnX+F6v8z93hS9eqVav07bff6uGHH1bbtm1tTz7kyftjl84NM8wbfuXp6en0mh3cOTbJveP7+OOPC213OBxKS0uzOBpcr5iOgautatWqOnz4sJYvX66mTZvaHQ6uEykpKRo8eHCB9tjYWP33v/91ad+enp46ceKEAgICnNpPnDhhfkm1Sv6aPu5wbe0qdl+z16tXT/Hx8WratKk2b96sSpUquayvvXv3FkhASFJMTIw+++wzl/WbZ8eOHZozZ45q1KihN954Q40aNdK2bdtUvnx5l/XpLtfvJCEuQW5urr799lslJSXp1KlTatu2rfr162d3WJLOjdbIr2vXrubjNm3aWBxNQbfeeqsaNmyo5cuXa/jw4apSpYratWunxo0b2/qFNSIiQosWLVK1atX0008/6emnn5Z0rm6A3V+k3Tk2yb3jK1OmjHJzc5WVlSWHwyEvLy8zJrtjA4ArER0drfnz52v06NE6efKk3eHgOuDj43NZr10NHTp00NSpU9W3b1/zi9mhQ4f0zjvvFPrl0ZWeffbZQh+7kh2jme2+Zn/kkUeUmJioXbt2qWbNmrr55ptd0s+2bdt04sQJpxEIeQzDUG5urkv6zTNhwgTt3LlTd9xxh7p06aINGzYoOTlZkZGR6tKli8v6dZfrd4fhDmP13dx3332nVatW6dChQ2rdurXatWunatWq2R1WibFp06YCbdu3b9fy5ct15MgRzZo1y4aozjl16pQ++ugjHTlyRDfeeKNiYmIknZv/eOTIEdWqVYvYiojv8OHDuvHGG9WxY0d9++23atWqlY4ePWprfDt37tSkSZMUEBCgXbt2qUqVKurdu7dq1KihoUOHUhMClnj44Yc1f/58u8PANSLveDp69KhWrVql2267TX/88Ye+/PJLVseAS40bN0733nuvOWc+z86dO/XBBx+4/Pj75JNP9O233yo3N1cOh0MOh0NdunTRvffe69IRCRcq0vjee+9pxIgReuWVV656n0uWLHF6/vHHH+vee+81n3fs2PGq95mfXdfshfUrSfXr13dJf5I0YMAARUVFKTc3Vw8//LB8fX0lnZseMW/ePPn4+OjRRx91Wf//+9//1KZNG2VmZsrhcFhWazDv+n3fvn1q3769bd8vGAlRDHv27NF9992nevXq2R3KBf3rX/8qNFs6ffp0G6Jx9uWXXxbaHhwcbHtlbz8/Pz3yyCPat2+fHA6HOQetXLlyLi20VNJjk6RffvlFmzdvVkZGhnbv3q1PPvlEqamp+uqrrxQXF2drEmL27NkaMGCA6tSpo2HDhmno0KGKj4/X6NGjr+khlACufSEhIbrtttvsDgPXkV69ehU6lTEjI0O9evVyef/33HOP7rnnHp04cUKSCkzNcJXz6zPk54oEhHRu6kt+zZs3d2pzdRLCrmv2/P2ePXtW27dvV82aNTVq1CiX9fnss8+qevXqWrx4sQYPHqzAwEA5HA4dPnxYHTp00EMPPeSyvqVz005Gjx6tQ4cO6fTp06pbt64ef/xxl07FkKSvvvpKK1eulCSVLl1ap0+f1rfffqt77rnH0u8XJCGKoXbt2goNDZV0roDHX3/9pXbt2rnFF8E848aNMx9nZWXp119/1fHjx22M6P/ZvTrHxVzsjrnd3Dk2Sfr+++/1wgsvmJlj6dxytqNGjXL58MyipKenm8sMGYYhf39/nT17VpL073//287QcB1hFASupsKOpwYNGqhBgwY2RIPrSUREhLy9vbVlyxanIequvEudxzAM/fTTT1q3bp0cDocaN26sTp06WTJs3I678668814cdl2zn9/vkSNHtHDhQpf2Wb16dUnnprDHxcVp3759ks4d73krkrjSzJkzFRcXp1atWmnYsGF67LHHNGvWLL344osu7TcpKUnTpk3TqVOnFB8frxtvvFFr1qzRPffc49J+z0cSohi++OILtW/fXseOHdPbb7+tjh07avLkyRozZozdoZn8/Pycnnfp0kXDhg1Tjx49bIro/23btk1ffPGFfH19dd9996ls2bLas2eP7dMJpIvfMbebO8cmSUFBQU5FmiTJ39+/wLFoh9zcXHPkiGEYSkpKkr+/vyRZXsgKAICSbMGCBVqxYoUqVapkjiY0DMOSJMSiRYu0Z88edenSRbNnz1a1atW0cOFCl9+llpzvzmdnZ2v79u2qVq2aS+/OHz9+XD/99JO5WkKe/v37u6zP/Nzlmj00NFQ7d+4stF7D1bZlyxYdPnxYOTk5kqS///7b5SNOJCktLU2tWrWSdO7vKSIiwpI6P+Hh4crNzVVwcLB5gy4zM9Pl/Z6PJEQx5BW1W7Nmjdq3b6+7777bHMbiLvIP1crNzVVKSorLC6oU1/Tp0/Xggw8qNTVVc+bM0ZAhQzRv3jyXDWW7FBe7Y243d45NkkaOHKmdO3eqTJkyZjJi5MiRNkd1TlxcnPbv369KlSopKChI69ats+wfcAAAriWrV6/WG2+8IS8v6782/Pbbbxo/frw8PT3l4+Ojbt26ufxOcZ7z786npaVpzpw5Lu1zwoQJql+/vqKiomyZPmrXNXtWVpa+//57bd68WQ6HQ/Xq1dO4ceNcnoCYMmWKDh8+rGrVqjl93lYkIc7/nrZ9+3ZLRhKHhIRo+PDhatWqlU6fPq1p06aZ3zesRBKiGEqXLq1vvvlGiYmJZu2FvGyZu8g/TNPT01NhYWGFLqdkh8DAQLVs2VKS9OOPP8rDw8OWjFthLnbH3G7uHJskzZgxQzt27NCZM2d0++23q23btvrggw/Ut29fu0Mz1zLPyMjQkCFDLCv2AwDAtSYiIkKZmZm2JCEMw3AawZidna2srCzL45CkcuXKaffu3S7v58EHH3R5Hxdi1zX7zJkz5eHhYda7WbZsmWbOnOnyG0g7d+7U66+/bkvCp02bNtq5c6eqVq2qnJwcLVy40JJVF8PCwhQWFiZJuuWWW1S5cmU1a9bM5f2ejyREMfzrX//SN998ozvvvFPVqlXTmTNn3OKLVn6uHBp2pRo2bKiPPvpIN910kxwOhzZs2GB7zYA87nzH3J1jk85lbOPj45WRkaExY8boH//4h/766y+7w5IkHThwQNOmTdPRo0clnSuoNHDgQEVERNgcGQAAJYuXl5eGDh2qhg0bOl2/WVG/IDAwUPv371eFChWUkZGhESNGuHT5wvxmz55tPs7NzdWOHTtcXperQYMG+vXXX81EgNXsumZPSUnR66+/bj5v0KCBJcugVq5cWUePHjVr/1mpe/fu5uP4+HjL+rW69sOFkIQohvT0dHXv3l1+fn46ffq0Dh48aMuwlcIcOHBAoaGhSk9P18cff6wtW7aYw5juueceyyoIX8yyZcvM/3t7e+uHH35wmy/TeXfMJVk2vK+43Dk26dycsuPHj6tcuXLKzc1Vbm6u24xw+c9//qO77rpLLVq0kHRuKOnMmTM1YsQImyMDAKBkiY6OVnR0tC19P/fcc+ZIiMcee0wVKlQw7+K6Wv4lST09PdW+fXvVrVvXpX1+//33+u9//ysvLy9z5EnesqBWsOua3dPTU/v27VNkZKQkad++fZbU8Dp+/LieeeYZ1apVS97e3ma7Fcsev/7663r44YdVvnx5zZw5U1u3blX37t3VunVrl/Y7Y8aMi75u1Xc0khDFMGPGDI0ePVpnz57VCy+8oPDwcAUHB7vFF+nJkyfrtdde05tvvqnGjRtr6NChkqTly5drypQpeumll2yOUJo2bZrdIVzQhYqLusPIEneOTZK8vb313HPPqWnTpjp27JheeeUVs8CO3U6ePGkmIKRzF1AffvihjREBAFAyNWnSRGXKlFGpUqWUm5ur9PR0y4pQlylTxnx8ww036Pfff9eCBQs0aNAgl/cdExOj9PR0eXt7O31BdaWLLQtqBbuu2Xv27KmxY8eaIxIOHz6sAQMGuLzfe++91+V9XMiBAwdUvnx5bd++XQcPHtTw4cP16quvujwJ0bx5c5f+/OIiCVEMhmGoTJky+vXXX9W0aVP17t3bkiFCxZFXNfb06dO66667zPauXbu6TfHMJUuWFNpuRdGXojz88MPm4+zsbK1atcqSZZ+Kw51jk5zvjERFRalSpUqqUqWKzVGd4+XlpezsbPMuQnZ2NqtiAABwGSZMmKDnn39enp6eevHFF5WZmakWLVpYUrsgJydH69atU1JSkjZt2qQGDRpYdv24aNEiJSYmyjAM9enTR40aNdK3337r0uHsdiwLmp9d1+z169fXm2++aS6TGRkZaUkNkvr16xcosm61NWvWqHXr1goKCrLkWvXGG29Udna29u3bJ4fDocjISFuukUlCFIPD4dCOHTv0888/m/PQ7ChgUhhvb29t27ZNN9xwg9atW6cmTZpIktauXevyeWvFlX/ljuzsbG3YsEFVq1Z1iyTE+Z9RnTp13GbqgzvHJp27Q3C+xMREp2kkdhk8eLBT1eHc3FwNGTLExogAACiZsrKyFBAQYF5bPvHEExoyZIglSYjHH39cN9xwgzp27KgnnnjC0uKYSUlJmj59uk6ePKn4+Hi1atVKa9ascWkSorBlQatWrWrZ8uz5r9nPnj2r9evXq3bt2i6/Zs/MzNQPP/ygzZs3Szo36uUf//iHy+tR2FlkvVGjRho2bJhOnDihiRMnKj09Xb6+vi7vd+fOnZo0aZICAgK0a9cuValSRb1797b8eyNJiGJ44IEH9NZbb6l69epq0qSJ0tPTddNNN9kdliSpb9++euedd3T8+HF98803Klu2rBwOh06dOmVLkZXCnF+46MyZM5o0aZJN0TjLvx5v3tKm6enpNkb0/9w5NqnwOWWrV6/WX3/9pfbt26tevXo2RHXOkSNHdOTIkULbJevuKAAAcC04deqUfvnlF7NgolV3Tlu0aKH169ebX0ajoqIsS0SEh4crJydHwcHB5hLprq59df6yoMePH3cqkOlq51+zp6ena+rUqS7vd8aMGSpdurTi4uJkGIb+97//acaMGS6fdmNnkfWePXtq586dCgkJMac3WZFsmj17tgYMGKA6depo2LBhGjp0qOLj4y1LdOUhCVEMTZo0MUcYSJKvr6+5hIzdatSoofHjxys7O1tnzpyxO5xi8fT01MGDB+0OQ5L0wgsvyDAMORwOeXp6KjQ0VE888YTdYUly79ikwueUbd68WbVr19bMmTMtrfR7vsLuJFSrVk2lS5eWYRgkIQAAKKY77rhDTz/9tKpWrapmzZopPT3d6brYlZ544glzSsby5cs1e/ZsNWzY0JK6bCEhIRo+fLhatWql06dPa9q0aZYXpi9btqx27txpaZ/5+fr6KjMz05z+7Sp79+7VxIkTzecNGjTQc88957L+8thZZN0wDP3555/65JNP5HA41LhxY3Xq1MnlU6/T09PN49gwDPn7+5tJNiuRhCiGC1UR7d+/vz766CP16NHD4ogK8vLysqxI0KUaP368DMOQdO5g37Nnj8uLrhSXOxfNdOfYpHNzys7366+/qmPHjvr2229tiOj/nX8nIS0tTXPmzGFKBgAAl6hDhw7q0KGD+dzLy8vSJSQ9PT3VrFkzNWvWTFlZWfr9998t6TcsLMxcieOWW25R5cqV1axZM5f2mb8oeW5urg4ePOj02btaYTUpunfvri1btkhy3UjSatWqmVMDJGnXrl2qWLGiS/rKz84i64sWLdKePXvUpUsXzZ49W9WqVdPChQv10EMPubTf3Nxc5eTkyNPTU4ZhKCkpSf7+/i7tszAkIYrhYlVE3eWOas+ePc275nnylvQZMWKEXnnlFdtiu+OOO8zHnp6eCgsLU3BwsG3x5HexAkApKSm21tVw59guJCMjQ5L06quv2hyJs3Llymn37t12hwEAQImzdetWLV++3GnE7erVqxUdHa0WLVo4rUZ1tRmGoZ9++knr1q1zultsBVfWfriQ84uSr1u3ThUqVLCs/7yRpKdPn1ZaWpoqVapkvubKkaQHDhzQsGHDVKVKFTkcDu3cuVM1atQwkzKuWhnOziLrv/32m8aPHy9PT0/5+PioW7dultR+i4uL0/79+1WpUiUFBQVp3bp1tqz4SBKiGOrWrXvBpYkaNmxoc3TnXGxJHzsTEJJ7VJ69kPzD9vPknWSXLVtm6xd9d45NKjzxdfbsWfXs2VOxsbFO/5BaLf/8ydzcXO3YscP2zwsAgJLonXfe0Z133um0XObmzZvVrFkzRUZGurRvu+4WS/YslX6houTt2rVzWZ/55RVKfO2111SqVCndfffdlkxB6d27t8v7KExhRdatYhiGU22V7OxsZWVlubzfmJgYs187C96ThCgGO5cmKi67l/S5GDsrzxbl/GH7+fXq1cu6QArhzrFJhSe+hg0bprFjx2rYsGG2JiFq1qxpPvb09FT79u1Vt25d2+IBAKCk8vb2LrA6wmeffWbJsHW77hZL9iyVnv96Pjc3V7t27bLki2meI0eOaPz48frnP/9p1p3r06ePatWq5dJ+7bpRdKFEUx5XJpwCAwO1f/9+VahQQRkZGRoxYoS5CqMrjRkzRgMHDlRoaKjGjRunHTt26K677rK83iFJiGKwc2mi4rpQIT5X/vEUl52VZ4ty6tQpffzxx9q8ebMcDofq1q2rHj16uEV9DXeO7UK6du0qLy8vNW3a1NY47MxsAwBwLXnmmWckyVyhy9fX12xzNbvuFkv2LJWe/3o+bwqzFQUa87z22mvq06ePeRPzueee0/jx4zV+/HiX9rty5Up98MEHOnbsmDnKNm9auSvZecPsueeeM5Najz32mCpUqGDWIHGljIwMhYWFadOmTSpVqpSmT5+uoUOHkoRwV3YtTVRcFyrE5w7srDxblBkzZqhmzZp69tlnJUnLli3TW2+9ZekJ/0LcObYLySs4asUwyYv517/+ZRZDzW/69Ok2RAMAQMmVkZGhoUOH6vTp05LOJSEGDBhgSd923S2W7Fkq/WKjYK3w5JNPmkUiHQ6HIiMjLUk4LViwQC+88ILLp/ecz86putnZ2frf//4nX19ftW/fXg6HQ2fOnFHp0qVd2q9hGMrOzlZycrJatGghLy8vW77XkoQoBjuXJrpc7lSIz87Ks0U5fPiwhg4daj6/55573OZLvjvH5u7GjRtnPs7KytKvv/6q48eP2xgRAAAl03/+8x89+uijqlevniRpy5YtmjlzpsaOHevyvp977jnzC5KVd4sle5ZKX716tT7++GP5+vqqd+/eCg0N1ebNmy9aJP9q8vb21jPPPKOAgABztYrevXu7vKZbaGioIiIiXNpHYc6/aZU3AmP69OkaN26cnn/+eZf1PX78eNWsWVPHjx/XX3/9pQcffFATJ07UiBEjXNandG61m/79+8vX11f33Xef0tPTnQqQWoUkRDHYvTRRcbhzIT47K88WpVSpUvrjjz/UoEEDSdLGjRtVqlQpm6M6x51jc3fnT1np0qWLhg0b5hbL6QIAUJJkZmaaCQhJqlevnmUjWk+dOmU+zlsl4vDhw2abKxMSdiyVPm/ePA0ZMkRpaWmaP3++hg8frs8++8yyJMTs2bM1YMAA1alTR8OGDdPQoUMVHx+v0aNHu7TfypUra/LkyWrZsqW8vb3NdlfftMx/0+p8Tz31lEv7zsnJUe/evZWbm6thw4apdOnS5mgjV7rzzjsVGxur0qVLm9NBnn76aZf3ez6SEMVg59JExeXOhfhiYmKUmZmpffv2SZKlSw0VpV+/fpo2bZo55M7Pz0//+te/bI7qHHeOzd2lpKSYj/OGUObm5toYEQAAJVP58uX18ccfmzfkli5dqvLly1vS98VqERiGofj4eJf1nZub67Q8aKNGjRQbG+vS4pTBwcGqXr26qlevroULF0o6N2zfKunp6eZqGIZhyN/fX2fPnnV5v2fOnFGZMmW0YcMGp3ZXJyH8/Px06tQpc4l56dzUkAceeMDlNdiqVaumjRs3qmHDhnI4HDp58qRycnJc2mceX19fS/q5GJIQxWDn0kTFFRMTo+zsbO3bt8+cw2W3r7/+Wp07d9amTZv07rvvKjw8XA6HQwcOHNBjjz2mZs2a2R2iKleurPHjx+vMmTMyDMPpd2w3d47N3c2fP998nFfYafDgwTZGBABAydS/f3998sknmjx5siTphhtuUP/+/S3p+/XXX7ekn8K8//77OnLkiG655RYZhqEff/xRBw8edGkxw9q1a2vGjBnq2LGjcnJylJiYKH9/f5f1d77c3Fzl5OTI09NThmEoKSnJkv6tOp7ON2XKFG3dutXpGvvAgQPavXu3unTpon/84x8u63vr1q1KTExUaGioTpw4oeHDh+uRRx5xWX/uhiREMdi5NFFx7dy5U5MmTSowh8vOKRlLly5VXFyc5s+fr5dfflmhoaGSzi3/M3bsWLdIQnz88ceFtt97770WR1KQO8fm7txhVRgAAK4Fvr6+tn05ysrK0vfff2+uFFavXj116dLFaci+q6xdu1avv/66OfKhYcOGeu6551yahMhbPe7jjz9WeHi49u7d6/JpAfnFxcVp//79qlSpkoKCgrRu3TpLEgTHjx/X3LlztXHjRhmGoYYNG6p3794KDAx0ab+7du0qULR82LBhLl8NRJLTSive3t4u31d3QxKiGAqrCmvV0kTFZdccrovJGz4WEBBgJiCkc8VnAgIC7ArLSf7M55kzZ/Tbb7+pevXqNkb0/9w5Nne3ZMmSQts7duxoTqUCAABFGzNmTKHtViT8Z86cKQ8PD3P5wGXLlmnmzJmWfDH28vJymnrhcDjkcDhc2qfdN1E6depkPs77krxy5UqX33h955131LBhQ3PVlR9++EEzZ840V4hzlaioqAJtjRo1cmmfefIXxMzMzDRrnVhVeNVuJCGKYd26dfrwww9VunRp9e/fX1WqVNGGDRvUuXNnu0Mz2TWH62KqV6+ud999VzVr1tSUKVPUpk0bSdLy5ctVrVo1W2PLc/vttzs979q1qyXZz+Jw59jcXf6aEPl17NhRe/fuJQkBAEAx5b/zn52drdWrVysrK8uSvlNSUpymZDRo0MDlX0zzdO7cWadOnTJrA5w+fdrl1/52j4JdtWqVli5d6lQj4a+//tL333+vmJiYAiPDr5ZDhw6ZiSZJuu222/Tzzz+7pC9J2rBhgxo1alRgSflNmzbp2LFjLus3v/Hjx5urr5w9e1aHDx9WhQoVzGlP1zqSEMXw3//+V5MmTdKxY8c0a9YsjRkzRgkJCW6VhLBrDtfF9OvXTwkJCfrrr7+UkZGhn376yXwt/8nNnXh5ecnf31+5ubkuLTx0Odw5Nnfz6KOPXvC1u+66y8JIAAAo2c6f2lunTh3L7th7enpq3759Zq2zffv2mUt2ulqXLl2UnZ2tXbt2mfXWbrnlFpf2mX8UbFZWltasWWNpnbcPPvhAjz32mBmHw+HQlClT9PDDDys4ONhl/Xp6ejpd3+Z9OXeVt956S5MnT1apUqV0/PhxLV26VMuWLVP58uV10003uazf/M6vd7J9+/YLjuS9FpGEKIawsDD5+voqICDAXDrFquqlxWXXHK6L8fHxccpqlhRWzr27VO4cmzs5dOiQ5s6dq61bt0qS6tatq169erl8nWsAAK41mzZtMh/n5uZq165d5spdrtazZ0+NHTvWnNZ7+PBhc8i+q9lRb+38UbB33nmnXn31VZf1d75SpUqZS8Pn8fHxcXmNub59++rs2bNm8uPMmTPq27evy/q744479NxzzykiIkJ79+7VbbfdppEjR9o6XbxWrVqaOXOmbf1bzWHkn5CCQs2ZM0d79+5Vu3bt9Omnn6pt27bat2+fhgwZYndoBWRkZMjhcKh06dJ2h2LKzMzUp59+ai5x1LhxY3Xr1k2lSpWyOzRco1566SV16dJFbdu2lXRuCtD3339v6T/kAABcC/JPBc0bGTB06FCn5eFdKW/1N0mKjIyUl5c191BHjRqlBx980Ky39tJLL1leb+3kyZN68cUXNXXqVEv6y8zMlI+PT5FtrnCxpTJdsaRkRkaGli9fbk776Nixo9q2bWvp8pVr167V+vXrJUmNGzdWpUqVFBIS4vLaI+6AkRDFkJGRoeDgYG3atElNmjRRuXLl1K1bN7vDcnLo0CFNnTpVhw4d0unTp1W3bl09/vjjlq3jfDFz586Vl5eXnn76acXHx6ty5cqaPXu2nnzySbtDwzUqNzdX7du3N5+3b99e3377rY0RAQBQMg0bNszp+fHjx11eNPBCw9Lzaj65qjZBfnbUW3v22WfNgoWGYej48eO65557XNpnfq+99lqh7aNGjdI777yjfv36uaRfO5bKLFOmjGJjYxUbG6s9e/ZoyZIlGjZsmGrXrm3JyOP//ve/Wr16tTn949NPP1V0dPR1M22YJEQx2D2toTjeffddxcXFqVWrVho2bJgee+wxzZo1y2n5F7ts27bNnPfk4eGhdu3a6ZtvvrE5qnPyKtFeiJ0VateuXavAwEBzRYzU1FQlJSUpKChIbdq0uS6ypJfrxhtv1A8//KB27drJ4XBo2bJlatKkid1hAQBQ4pUtW1a7d+92aR95yYYTJ05oy5Ytaty4sQzD0Pr161WzZk1LkhB21Ft7/vnnzcceHh4qV66cpXXALrb86PlTRa4mO5fKlKRKlSrpoYce0gMPPKDff//dkj6XLl2qcePGmaNM2rdvrxdeeIEkBP7fjBkzCm13p+REWlqauXyOYRiKiIiwbL5eUc6f8ZOenu42NTWGDBmi8PBw8wv9gQMHVL58eTkcDh04cEDvv/++bbEtWrTIvPuQnZ2tESNGKDo6Wtu2bdO2bdvUu3dv22JzVz179nQqZrRgwQLzNcMwdN9999kVGgAAJVL+JToNw9DBgwcVExPj0j7zCkyPGzdO8fHxKlu2rKRzK1S8+eabLu07jx311vIvaW+Hi9V+qFixosv6tXOpzPw8PDzUvHlzS/ry9vZ2mubi7e19XRWeJwlRDPkPxqysLCUnJ6tcuXL2BVSI3Nxcp+fbt2+3ZP5WcYSHh2vHjh2qVq2aTp8+rRdeeOGimVYrRUZGOmVZ82ddzx9+aLXs7GwFBQVJkjZu3Cg/Pz8z8fDMM8/YGZrbmjdvnt0hAABwTcl/zebp6amQkBBz2UpXO3jwoLy9vc3n3t7eRY5ivVo6depkPnaHkcVWyH8zJzs7W9nZ2SpVqpTee+89l/bbpUuXAr/XLl26SDp3ozXvevha0rRp0wJLwBaWjLlWkYQohhtvvNHpebt27TRixAiboilcmzZttHPnTlWtWlU5OTlauHChy+ZtXar8X+aHDx+u0NBQt0mQnD9KI/9zd6jZmrdc0caNG52qFTMVo2hHjx7V4cOHnRJ09evXtzEiAABKnho1amjPnj3auHGjpHN3qK1KQrRr107Dhw9XixYtJEmrVq0yi07j6jv/Zs6aNWu0bds2l/db2LQLwzAUHx+vOXPmuOViAFfq/vvvd3petmxZPfDAAzZFYz2SEJdhz549OnbsmN1hOOnevbv5OD4+3sZICjq/uFDeycyK+XxFOf/LfP7ndn/Rr1WrlmbMmKGqVavq559/NhNfqampbpPEcVcLFizQihUrVKlSJfP3aBgGSQgAAC7RihUrtGjRIrVq1UpLly7VunXr1KZNG6cC0K7SvXt3NW3aVFu3bpVhGBowYIDLl4vE/2vWrJk++OCDAl+Yr7a82nGFcWUCws7acPmnORVm1KhRLuvbHZCEKIbz55kHBgbqoYcesjkqZ+5ctyKvuJAknT17VuvXr1ft2rXdIgmRV5E2T/46C3FxcVaH46Rv37767rvvdPjwYQ0ePFjVqlWTJJUuXVqDBw+2NTZ3t3r1ar3xxhuWLeMFAMC16vPPP9crr7yigIAArV27Vs8995xeeuklS5IQhw8flr+/v6Kjo53a7Cwc7kr/+te/Ch2J63A4ZBhGgeKNV9vKlSvNx7m5uUpJSVGpUqVc2qd0brr7999/r82bN8vhcKhevXrq0qWL01QcVxg/frzTd7y8zz4zM1OHDh3Shx9+6LK+809zys7O1qpVq+Th4aHWrVu7rE93whV6MZSEeeb561acPXtWK1asUIUKFWyM6P/lFRfKk56ebtl6x0W55ZZbnJ7Xq1fPfNyhQwerw3Hi5eVVoBLxwoUL9cADD1i6hnFJFBERoczMTJIQAABcIcMwFBAQYD728PCwrMB4/i+JWVlZOnTokCIiIjR58mRL+rfauHHjzMdjxoxxuhte1J3zq2HNmjXmY09PT4WFhWno0KEu73fmzJny8PDQbbfdJklatmyZZs6c6fKbqflHYOTk5Gjt2rVavny59u7d6zTK3BXOH9FTp04dvfjii3rwwQdd2q+74Aq9GHJycrRz506dOXPGbFuwYIEeeOABhYeHu0U29vy6Fe3bt9fLL79sUzQX5+vrq8zMTLPegZ3sKsBTHAsXLlRCQoKys7PNDO3Zs2f13Xff6e6779bdd99tc4Tuy8vLS0OHDlXDhg2dpq6cnxADAAAX5+XlZRbQy8rK0rvvvqtatWpZ0vf5w/T37Nmjr776ypK+7ZBXayMrK0vHjh2zrPZGHrtGUKekpDj9rhs0aKBnn33Wkr737NmjL7/8UikpKWrcuLFuv/12S6b85F/FMG/USXp6usv7dRckIYrh3//+t3Jzc1W6dGmzbd++ffryyy/Vrl07t0hCnM/hcKhGjRpu8UW/MO5S2NOuAjzFkZycrHfffdfp92flmsklWXR0tNPQTQAAcHn69OmjM2fOyM/PT23btlV4eLglUzEKU6lSJf3555+29G2VY8eOae7cuQoMDNTcuXMVFRWlbdu2uTQhsWXLFtWoUUOpqamaO3eu+RnXrl1bvXv3Vvny5V3Wt3Ru1MW+ffsUGRkp6dz3LE9PT5f2mSclJUVr165VXFyc2rVrp+DgYEv6feGFF8wboZ6engoNDdUTTzxhSd/uwGG4wxIAbm7o0KGaMGGCUxtfBq9dzz33nCZOnGh3GJo6daoGDhxYZBsKl52drX379snhcCgyMtKyf8wAAMDVkb/mWW5urvbs2aPIyEg99dRTNkblOnPmzNHy5cvVoUMH3XfffVq+fLl+/fVXBQUFqXv37goNDXVJv3nXvsOHD1dcXJxatWol6VyNiK+//lpjx451Sb95Nm3apOnTp5v7d/jwYQ0YMMBpZThXOnXqlFatWqWkpCTl5uaqdevWat26tfz9/S3p/3rESIhiKKyAYkxMjPWB4KqzqwBPcTz55JNavXq1fH19zVUdSEAUz86dOzVp0iQFBARo165dqlKlinr37k1FbQAALlH+qat502mtmrqav+ZZVlaWvLy8ruklOgMDA/XGG2+Yox46deqkTp06WRpDmzZtnB5bMf2lfv36evPNN7Vv3z5JUmRkpCV1vfKv4Ofp6akOHTooLS1NP/30k+bMmaMPPvjA5TFcrxgJgeta/gx7XgGe2NhYswCTnV577TV5enrq9OnTatCggeLi4jRjxgw999xzdofm9kaNGqUHH3xQderU0bBhw/TSSy8pPj5eo0ePtjs0AABKLMMwtGrVKu3atUs9evSwJYYRI0bolVdesaVvVzt16pRmz56t9evXy+FwqFGjRurVq5fLr0tHjx6tf/zjH9q3b5/KlSunNm3ayOFw6JdfflFaWprLf9f5kwH5uXolvdmzZ1/0dWqJuQ4jIXBdc4clTC8kNTVVEydOVFZWll588UX16NFDqampdodVIqSnp6tOnTqSzl0w+fv76+zZszZHBQBAyeZwONSqVSt9+eWXliQhDh8+bD42DEO7du3SiRMnXN6vXd59913VqFFD/fv31/Dhw9WlSxfNnDlTzzzzjEv7ffLJJ/Xee++ZxRHnz59vvmYYhst/1ykpKebj7OxsbdiwQVWrVnV5EoIkg31IQuC6ZHcBnuKIjIzU3r17VbFiRUnn1izOzMy0OaqSITc3Vzk5OfL09JRhGEpKSmJeHwAAV8kjjzxiSfHz/PXX8kasXqv1ICRp7969GjRokKRzX/7r1q1boIi6K5QvX97WkbbnJwPOnDmjSZMmubzf1atX6+OPP5avr6969+6t0NBQbd682WkaEFyDJEQx5M/CFsYdV8fAxc2aNUsTJ07U1KlTFRcXZ66BvHLlSk2ZMsXlBXiK48SJExo6dKjq1KmjI0eO6IUXXjDXT8bFxcXFaf/+/apUqZKCgoK0bt06tx71AgBASVK3bl1L+jl/ic5rXW5urtPzI0eO2BSJvTw9PXXw4EGX9zNv3jwNGTJEaWlpmj9/voYPH67PPvuMJIQFSEIUw5AhQxQeHi6HwyFJOnDggMqXLy+Hw6EDBw7o/ffftzlCXAk7CvAUx7333ms+9vHxUUREhOXrRZdU+Ys4vfjiizZGAgAALldmZqY+/fRTrVu3Tg6HQ40bN1a3bt3cpoj41XbDDTdox44dqlatmk6ePKmxY8fqySeftDsslxs/frzyyhQahqE9e/aodevWLu83ODhY1atXV/Xq1bVw4UJJ56aDwPVIQhRDZGSk03Cw/MtzDhs2zK6wcAXKli2rpKQkNW3aVAkJCU4FeKKiouwOT5LMFTHy++STT3TPPffYEA0AAIC15s6dKy8vLz399NOKj49X5cqVNXv27Gv2i3nfvn3Nx5MnT1bp0qVtjMY6d9xxh/k4b9pNcHCwy/utXbu2ZsyYoY4dOyonJ0eJiYlM37UISYhiOH8BkfzPWVykZLK7AE9xjBkzpkDbX3/9pW3btqlTp07mGs4AAACu8sYbb6hDhw5q1qyZpHM3RL755hsFBARowIABql27tsv63rZtmzklw8PDQ+3atdM333zjsv7sNnv2bMXFxal8+fL67rvvtHXrVt1xxx2F3pi6lti1f3/99Zck6eOPP1Z4eLj27t17TdcccSckIYohbxpGYc/Pfw0lg90FeIrj4YcfdnrucDg0ZcoUde3aVTNnziQJAQAAXG7Hjh1mscRDhw7p66+/1ptvvqljx47pnXfecWkdrfNv9qWnpysnJ8dl/dntjz/+0KOPPqrdu3crOTlZPXv21LvvvqsJEybYHdo1adSoUXaHcN0iCVEMN910k9Pz3r17m4/j4uKsDgdX0YWKjoaFhSktLU1BQUEWR/T/atSoUaCtefPmql+/vq1xlQQff/zxRV/PX28DAABcWP76C+vWrVODBg0UEBCggIAAZWVlubTv8PBws0bC6dOn9cILLxS4SXMt8fT0lCStWbNG7dq1U506dbjh6UIXul7kOtH1SEIUwy233OL0vF69eubjDh06WB0OrqL8tT7yGIah+Ph4zZkzR0OGDLEhqnMKS5CsW7dOt99+u0aOHGlDRCXHN998o27duvEPNwAAV8jDw0N79uxRZGSkli9frnbt2kk6d73k6iJ++WuvDR8+XKGhofLx8XFpn3aqVKmSJk6cqJSUFI0dO1Znz561O6RrWpkyZczHWVlZWrNmjSIjI22M6PrhMChqUKSePXvKMAw5HA5lZ2crOztbpUqV0nvvvWd3aLiGPfvss+Zxl+fgwYMKCAhQ7dq1zaGRKOjJJ5/UW2+9ZXcYAACUeOvXr9eUKVOUm5urqlWr6sUXX5S3t7f27t2rFStWuLRg9pIlSwpt79ixo8v6tFN2drbWrl2ryMhIRUZGKjc3V5mZmddNgUq75eTk6NVXX2WahgUYCVEM8+bNc3q+Zs0abdu2zaZocDW58z9uha2Nnbcyi7vXswAAANeGxo0ba+bMmTp9+rTTUuEVK1Z0+YpdKSkp5uPs7Gxt2LBBVatWdYvrNFfw8vJSbm6ufvjhBzkcDtWrV0833nij3WFdN9LT03XkyBG7w7gukIS4DM2aNdMHH3yg+++/3+5QcIVK2j9uefVIzq9TAgAA4AqZmZn69NNPtW7dOjkcDjVu3FjdunVzqhXhKo8++qjT8zNnzmjSpEku79cu77//vnbt2qU2bdpIkhISErRt27Zrug6GnfJGHUvnphcdP37c5Yk1nEMSohhWrlxpPs7NzVVKSoolJ164Xkn7xy2vHsltt91mcyTu7a233tL69eu1du1aORwONWnSRI0bN7Y7LAAASpy5c+fKy8tLTz/9tOLj41W5cmXNnj1bTz75pOWxeHp66uDBg5b3a5Xff/9dEydOlIeHh6RzI3OfeeYZkhAu8vzzz5uPPTw8VK5cOfOzh2uRhCiGNWvWmI89PT0VFhamoUOH2hgRXOVa/8ftevHNN99o+fLluummm7R48WJlZGRox44duvPOO+0ODQCAEmXbtm3mFFEPDw+1a9dO33zzjSV9jx8/3ulO9Z49e8xRAtciDw8PnTp1SgEBAZKk48eP86XYhcqVK6e1a9fK19dX9evXtzuc6wpJiGLo37+/3SHARQr7x61169Y2R4UrlZiYqFdffVWlS5fWjz/+qMcff1wvvPACSQgAAC7R+TXs09PTlZOTY0nfd9xxh/k470ZgcHCwJX3boXv37nrhhRd0ww03SJI2bdqkhx56yOaorl0TJ06Up6enTp8+rQYNGiguLk4zZsyg9poFSEJcxJYtW1SjRg2lpqZq7ty5+vPPPyVJtWvXVu/evVW+fHmbI8SVut7+cbue5FWSNgzDkmXEAAC4FoWHh2vHjh2qVq2aTp8+rRdeeMGy6QGF3Z1euHChHnjgAUv6t1qrVq10ww03aPv27ZKkhx56SOXKlbM3qGtYamqqJk6cqKysLL344ovq0aOHUlNT7Q7rukAS4iJmzZqliRMnaurUqYqLizOnYKxcuVJTpkzR2LFjbY4QV6p+/fo6ceKE/vzzTzkcDtYGvkaUKVNGqampCg4O1tmzZzV+/Hi1bNnS7rAAAChxhg0bZj4ePny4QkND5ePjY0nfCxcuVEJCgrKzs80ly8+ePavvvvtOd999t+6++25L4rBSdna2ypYtq9zcXO3bt0/79u1jqoCLREZGau/evapYsaKkc0VYMzMzbY7q+kASopjyzz9r06aNvvrqKxujwdXyxx9/aMaMGapbt67WrVunSpUqqXv37hQxLOEGDhwoL69zp7euXbuqQoUKZlFPAABQfOcvZ563TL0VK4klJyfr3XffdaqLkLdc+bVowYIFWrFihSpWrGjus2EYJCFc5MSJExo6dKjq1KmjI0eO6IUXXqD4u0VIQlxE2bJllZSUpKZNmyohIUFt2rSRw+HQL7/8oqioKLvDw1Xw/vvva8SIEYqIiNCwYcM0fPhwvfzyyyQhSrjg4GCz0BDLmQIAcPnyL2d+9uxZrV+/XrVr17YkCVGjRo0ChRkrVark8n7tsnr1ar3xxhvmjRS41r333ms+9vHxUUREhPz8/GyM6PrBEX4RTz75pN577z2lpKQoPT1d8+fPN18zDEM9evSwMTpcDdnZ2YqIiJB07nfq4+ND7YBrAIWGAAC4Os5fzjw9PV1Tp061pO+BAwcWq+1aERERoczMTJIQFqlfv75SU1O1fft2/V979xtTZd3HcfxzOAwUPTj+TWIExAYONHMKVpubrieuQUYh5VZMrAdRlLVqnJAaIFlRwTNdWxOo3Npyi5xbW8/6M0+1hnkk2ZLFklCnyWwCJ+D8ue4HzutWou6TnnN+N+e8X5vbdS6ffObYcXyv3/X5OhwOZWVlmY6UMPgJ/wcrV67kl5YEMDc3p5SUFAWDQR05coTC0ThA0RAAANGRlpamubk5hUIh1kdGWHJyspqbm7VmzZobejfmD4Jwaz766CPV1dXp2LFjGhgYUFlZmRwOh/r7+1VbW6v77rvPdMS4xxAiDL///vuC93NycnT58mVlZGTEOBEiZceOHZqYmNBtt92mkpISBQIBVrLGAYqGAACIPL/frx9//FEul4sBRBRUVFSooqLCdIy4NzQ0pPr6eh09elRvv/220tLSJF095dPS0sIQIgYYQoRhofIby7LU3d2tvr4+vfjiiwZSIRI2bNigQCCgsbExVVVVKS8vT06n03Qs3CKKhgAAiIxgMCiv1yuPx6Ph4WGtXr06Jn0QiWjz5s2mIyQEv9+vUCikjIwMpaam2vdTU1OVmZlpMFnicFiWZZkOAZhy5swZ9fT0KD09XWNjYyooKNCuXbtUXFxsOhpuwfDwsH1N0RAAADfvySefVFlZmbZs2aJ169bRVxBFzz77rBb61Wz//v0G0sSv/v5+XbhwQVlZWRofH9fdd98tSfruu++Ul5enp556ynDC+Me3SBjmrya6hinw4tfb26umpiaVlpbK7XarublZ3d3dam9vNx0Nt6C8vFx//PGHRkZGND09rezsbNORAABYlCorK3Xy5Em7o4BBRPS89dZb9vXMzIw8Hg+vk0ZBQ0ODfvjhB/3yyy8qKCjQ+fPnJUmFhYWGkyUOvkHCcP1qokAgoKGhIRUWFjKEiAM+n0+lpaWSrr5i43K5NDs7azgVbtWXX36pw4cP23u1+/r6VFdXx7pOAAD+pcbGRvuVjGPHjqm3t1dr1qyhQysKrj+1uXz5cm3btk1tbW3avn27wVTxqbKyUpWVlaZjJCyGEGGY30g7MzOjnp4eQ2kQSaFQSMFgUE6nU5ZlyePxyOVymY6FWzQwMKCuri77P/OpqSm1trYyhAAA4CY4nU6tX79e69evt8spER1XrlzR6dOn5XA4VFJSoq1bt7KJJEomJyfV19enkydPyuFw6M4771RDQ4PS09NNR4t7/DTfBKfTqQsXLpiOgQioqqqyj2BlZGTI6/Uy2Y8DmZmZWrJkif15yZIlFA0BAHAThoeHb/gzMjJiD/mvPy2MW3fixAm98sor+vbbb+XxeOR2u5WWlsYAIkoOHjyooqIivffee8rMzNTWrVv1/vvvm46VEDgJEYauri67JMayLI2Pj+vee+81nAqRcP0Knj179hhMgkgqLCxUZ2en7rnnHkmSx+NRfn6+3e/Cq1QAAITn6NGjf7lnWZbKy8v19ddfU+YdQR9//LE6OjqUk5MjSbp06ZLeeecdrVu3zmywOHX27Fm98MILkq7+TK9atUoffPCB2VAJgiFEGB544AH72ul0Kicnh6eqcaKjo2PB+21tbTFOgkgKhUIqLCy0T7nccccdkv77xIYhBAAA4XG73X/7dw0NDbELkgAsy7IHEJKUnZ294LYMREYoFLrh86VLlwwlSTwMIcJQXl6uK1euaGRkRA6HQ3l5eaYjIULq6+vt69nZWXk8HqWlpRlMhEiY3+MCAABuzszMjD799FN5vV77vfna2tobXntEZKxYsUJTU1P26y7T09N0lUVRWVmZfv31VxUVFWlyclL79u3T008/bTpWQnBYjNf+p1OnTunAgQNatWqVvF6v8vPzVVtbq7Vr15qOhihob29nReciR9EQAACRsX//fi1btkz333+/LMvSF198oenpaTq0EFdmZmYYrMUQJyHCcOjQIb322mvKzc2V2+1Wa2ur9u7dyxAiTlVUVNBCvMgdPHhQxcXFeuaZZ9Ta2moXDb300kumowEAsKiMjo6qu7vb/rxz5069/PLLBhPFr7m5OfvUiSStXbtWDz/8sFJTUw0ni0+HDx9e8H5dXV2MkyQefssKQyAQUG5urqSr72qlpKQoEAgYToVoqa6uZgCxyJ09e1bbtm1TcnKyXTQ0MTFhOhYAAIuO0+n8yz2Hw2EgSfzr7++Xz+fT7t275ff7dfvtt6u3t9d0rLi1dOlS+09ycrJ++uknXbx40XSshMBJiDDNzc0pJSVFwWBQR44c0cqVK01HAvA3KBoCACAytm/frunpaS1btkyS5PP5eFIcJadPn9a7774rSUpKStKmTZv0+eefG04Vv6qrq2/4/OCDD+rNN980lCax8Lg3DDt27LCfopaUlCgQCPAeHPB/7FrRkCS7aIgGbwAA/r2NGzcqGAxqcHBQx48fVzAY1MaNG03Hikvzq/p8Pp+CwaChNInFsiz99ttv9mY1RBfFlGEKBAI6d+6cvR1joaNpAP7/UDQEAMDNo6A9drq6uvToo4+qqKhITU1NSk5OVn19vSoqKkxHi0s7d+6UZVlyOBwKBAJKSkrSc889x5AtBhhChOHMmTPq6elRenq6xsbGVFBQoF27dqm4uNh0NAALoGgIAIDIaGlp0fPPP28XtHd2dmrv3r16/fXXTUeLa+fOnVN2drZSUlJMR0kYXq9XQ0NDevzxx01HiXu8jhGG3t5eNTU1qbOzU7m5uWpubtaHH35oOhaAv3F90ZDD4dDx48d1+fJl07EAAFh0KGiPnc8++8wuRhwdHdWhQ4c0Pj5uOFXiuOuuu3TixAnTMRICxZRh8Pl8Ki0tlXT1y9flcml2dtZwKgB/Z37RUE1Njbq6ugylAQBgcaOgPTa++eYb1dTU6OLFixoYGNBDDz2kAwcO6I033jAdLS5df3LWsiyNjY0pKyvLYKLEwUmIMIRCIbsUxrIseTweuVwuw6kAhCs5OVkul+svWzMAAMA/m1/Q7vf7KWiPkmudc4ODg9q8ebM2bdpEMWUUXX9ydvny5dqyZYvcbrfpWAmBkxBhqKqq0vnz55Wfn6+MjAx5vV6+fIFFZvfu3aYjAACw6GzYsMG+bmxsNJgk/mVkZKi/v1+Dg4NqbW1VMBjkAUoUVVdX/2X5QFISz+hjgWJKAAAAADBsenpaX331lQoLC7V69Wr5/X5NTEzYnRyILJYPmMMQIgwdHR0L3m9ra4txEgAAAADArWpra9Njjz2m0tJSud1uvfrqq+ru7lZ7e7vpaHGP1zHCUF9fb1/Pzs7K4/EoLS3NYCIAAAAAwM1i+YA5DCHCMP9ITllZGRMyAAAAAFikri0fcDqdLB+IMZo3blJFRQVFMQAAAACwCF1bPiCJ5QMxRicEAAAAAACICV7HAAAAAAAkFJYPmMMQAgAAAACQUK5fPhAIBPT9998rKYm2glhgCAEAAAAASCjzlw+UlpZqz549htIkFoYQAAAAAICEMjk5aV+HQiGNjo7K5/MZTJQ4GEIAAAAAABJKS0uLLMuSw+GQ0+lUdna2GhsbTcdKCGzHAAAAAAAkvEAgoORkntNHG//CAAAAAICEMjU1pVOnTunPP/+0733yySd65JFHVFRUpKKiInPh4hxDCAAAAABAQtm3b5/y8/O1dOlS+57f79fo6KhWrFjBECKKGEIAAAAAABJKKBRSU1PTDfd+/vlnPfHEE4YSJQ4WoQIAAAAAEkpNTU1Y9xB5FFMCAAAAAICY4CQEAAAAAACICYYQAAAAAAAgJhhCAAAAAACAmGAIAQAAAAAAYuI/iOBRixyp/0YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aggregate data to calculate mean across different attributes (job title, gender, department)\n",
    "avgSalaryByTitle = df[[\"title\",\"salary\"]].groupby([\"title\"]).mean()\n",
    "avgSalaryByGender = df[[\"gender\",\"salary\"]].groupby([\"gender\"]).mean()\n",
    "avgSalaryByDepartment = df[[\"dept_name\",\"salary\"]].groupby([\"dept_name\"]).mean()\n",
    "\n",
    "# Creating figure object with multiple subplots\n",
    "figure, axes = plt.subplots(nrows=1, ncols=3, sharey=True)\n",
    "figure.suptitle('Average Salary across Different Attributes', size=16)\n",
    "\n",
    "\n",
    "# Adding axes to each subplots\n",
    "axes[0].bar(x=avgSalaryByTitle.index, height=avgSalaryByTitle['salary'], width=0.8)\n",
    "axes[0].set_title('by Job Title')\n",
    "axes[1].bar(x=avgSalaryByGender.index, height=avgSalaryByGender['salary'], width=0.8)\n",
    "axes[1].set_title('by Gender')\n",
    "axes[2].bar(x=avgSalaryByDepartment.index, height=avgSalaryByDepartment['salary'], width=0.8)\n",
    "axes[2].set_title('by Department')\n",
    "\n",
    "# Rotating tick labels\n",
    "for tick in axes[0].get_xticklabels():\n",
    "    tick.set_rotation(270)\n",
    "for tick in axes[2].get_xticklabels():\n",
    "    tick.set_rotation(270)\n",
    "    \n",
    "figure.savefig(\"average_salaries.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving dataframe to .csv for further visualizations\n",
    "df.to_csv('sql_challenge_extract.csv', index_label='index')"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}