Week_2b.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Tips To Get Started - Data Analytics for Engineers "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Wednesday 21th of February 2018* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Keeping track of absence + Group Formation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Note about set-up demos (watching vs doing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Using for-loops with plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib as plt \n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "# import data\n",
    "student_grades = pd.read_csv(\"http://bit.ly/dae_student_grades\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>student</th>\n",
       "      <th>assignment1</th>\n",
       "      <th>assignment2</th>\n",
       "      <th>homework</th>\n",
       "      <th>exam</th>\n",
       "      <th>resit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sem</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tess</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  student  assignment1  assignment2  homework  exam  resit\n",
       "0     Sem          6.7          7.5       9.5   7.8    NaN\n",
       "1   Lucas          8.2          8.2      10.0   6.9    NaN\n",
       "2    Tess          9.0          9.8      10.0   9.2    NaN\n",
       "3    Adam          5.0          4.9      10.0   3.4    5.8\n",
       "4   James          3.4          5.3       8.0   4.9    6.2"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect data\n",
    "student_grades.head()\n",
    "\n",
    "# what does NaN mean? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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k2K24Xj8JHtsN9/wMHW6Dg8vg69vgnZawaJRlMln6IUufwJmYzZBxGLbMgQX3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\nLAe0GwgPr4Nml1v6sT69yjLizUpcHFy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vqvu95xO4K6UWf1Mtnoi0Ap8F7vM7S7lEpB74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\nBWOMMUVWFIwxxhT9D6BngXV+6pDCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dac22b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distribution of multiple columns\n",
    "student_grades['assignment1'].plot(kind='density')\n",
    "student_grades['assignment2'].plot(kind='density')\n",
    "student_grades['homework'].plot(kind='density')\n",
    "student_grades['exam'].plot(kind='density');\n",
    "\n",
    "# what is the problem of this approach?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['student', 'assignment1', 'assignment2', 'homework', 'exam', 'resit'], dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect columns\n",
    "student_grades.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# how do we select the following four columns: \n",
    "# \"assignment1\", \"assignment2\", \"homework\", \"exam\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['assignment1', 'assignment2', 'homework', 'exam'], dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_grades.columns[1:5] # 1 inclusive; #5 exclusive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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37kzLli35/fffsbe3x93dncaNGxMVFcXp06dNuu4zzzzDAw88QJ8+fa7an0FRrCE2IxZb\njS1+Tn63fI7aLrWJzVRbxd4KlRQs7Ouvv2bMmDGEhYVx8ODBMpWZbtq0KVOmTKF79+6EhYVx7733\ncvHiRcCYFGJiYujcuTM2NjYEBgYWbcvp4ODAkiVLeOKJJwgNDUWj0TB8+PCbXYpXX32Vli1bMnjw\n4KJS34piDTEZMQS4BGCjuXFJ99IEuAQQn2Ucm1DKRpXOVixC/UyUW9VnQx+8Hb357J7Pbvkca06u\nYeLOiaqEdjGmls5WPQVFUSoMKSUxGTG3NZ4AagbS7VBJQVGUCiM1L5VMXWbRWoNbVZgULmZdNEdY\n1YpKCoqiVBjmmHkEFA1Sx2fF33ZM1Y1KCoqiVBixGcYZQ7ebFBxsHfC091RJ4RaopKAoSoVxuwvX\nivN39ldJ4RaopKAoSoURkxGDr6MvDra3v4jSz9mP+GyVFMpKJQVFUSqMmIwYs/QSAGo61yQ+UyWF\nslJJQVGUCiM2I9ZsScHf2Z8MXQZZuiyznK+6UEnBjK4tc33+/HkaNmxIcnIyBoOBTp06sWnTJgB6\n9epF69atadasGYsWLSo6h4uLC2PGjKFZs2bcc8897N69m65du1KvXj3Wr19vrZemKBaXq88lMSfx\ntgeZC/k7+QNqBlJZVbmCePHvv09epHlLZ9uHNMH/rbdu2iYyMvK6Mtd//PEHY8eO5YUXXqBt27Y0\nbdqU7t27A7B48WK8vLzIycmhTZs29O7dG29vb7Kysrjrrrv44IMPePTRRxk/fjybN2/m2LFjDBky\npKiyqqJUNYULzcyWFJz/Swr1Peqb5ZzVgUWTghCiBzAHsAG+kFJOv0G7NsBOoJ+UcrUlY7KU4mWu\nAXJycvD19WXixImsWrWKBQsWcPDgwaL2c+fOZd26dQDExMRw6tQpvL29sbOzo0ePHoCxpLa9vT1a\nrZbQ0NBSq58qSmVmrjUKhYonBcV0FksKQggb4FPgXiAW2COEWC+lPFZCuxnAJnNct7S/6C3lRmWu\ns7OziY01zr3OzMzE1dWVbdu2sWXLFnbu3ImTkxNdu3YlNzcXAK1WW7QvbfGS2oXltBWlqjJ3Uqjh\nVAON0KgZSGVkyTGFtsBpKeVZKWU+sBJ4pIR2LwNrgEQLxmJxNypzPXbsWAYOHMh7773Hc889B0Ba\nWhqenp44OTlx/Phxdu3aZc3QFaVCiMmIwUXrgoe9h1nOp9Vo8XH04WKmKnVRFpa8fVQbiCn2dSzQ\nrngDIURt4FGgG9DmRicSQjwPPA9Qp04dswdqDsXLXBsMBrRaLR999BF79uxhx44d2NjYsGbNGpYs\nWcKAAQNYsGABISEhNG7cmPbt21s7fEWxusLpqIU9ZXPwd/ZXPYUysvZA88fAWCml4Wb/EaSUi4BF\nYCydXU6xlVnfvn2v2+qyeC9g7dq1RZ///PPPJZ4jMzOz6POJEyfe8JiiVDWxGbE09Gxo1nP6O/lz\n8vJJs56zqrPk7aM4oPjNwYArjxUXAawUQkQBjwPzhRC9LBiToigVUIGhgLjMOLOtUShUWOqisu0b\nY02W7CnsARoKIYIxJoN+wIDiDaSUwYWfCyG+An6UUn5vwZgURamAErMT0Rl0ZhtkLlTTuSa5Bbmk\n5aXh4WCesYqqzmI9BSmlHhgB/ApEAt9JKY8KIYYLIW6+N+StXc/cp1RukfpZKGVl7plHhYqmpapx\nBZNZdExBSrkR2HjNYwtu0HborV7HwcGBlJQUvL29zTpIpZSdlJKUlBQcHG6/oJlSfVg6KVzMvEgT\nryZmPXdVZe2BZrMICAggNjaWpKQka4eiYEzSAQHmvTesVG2xmbHYCtuizXHMpfB8idmVesZ7uaoS\nSUGr1RIcHFx6Q0VRKqSYjBhqudTCVmPetyRvR29sha26fVQGqiCeoihWF5MRY/ZbRwAaocHXyZeE\nrASzn7uqUklBURSrM+c+Ctfyc/YjIVslBVOppKAoilWl5aWRkZ9hkZ4CGMcVVFIwnUoKiqJYlaVm\nHhVSC9jKRiUFRVGsKjbDWEXYYrePnPzIK8gjLS/NIuevalRSUBTFqgp7CgEulhtTANQtJBOppKAo\nilXFZMTg4+iDk9bJIucvXKugkoJpVFJQFMWqLDUdtZDaga1sVFJQFMWqLJ0UvB28sRE2KimYSCUF\nRVGsJq8gj8TsRIsNMgPYaGyo4VRD3T4ykUoKiqJYTVxmHBJpsUHmQmqtgulUUlAUxWpi0o0zj+q4\nWXabXX9nf1XqwkQqKSiKYjWWXrhWqLCnoBawlU4lBUVRrCYmIwYXrQue9p4WvY6fkx85+hzS89Mt\nep2qQCUFRVGsJjojmkDXQItvjqUWsJlOJQVFUawmNiPWojOPCqm1CqZTSUFRFKsoMBQQmxlLHVfL\nDjKDWtVcFiopKIpiFfHZ8egNeosPMgP4OPqgERo1A8kEKikoimIV5TXzCMBWY4uPo4/qKZhAJQVF\nUawiOj0asPwahUKF+yooN6eSgqIoVhGbEYudxg5fJ99yuZ5a1WwalRQURbGK6IxoarvWRiPK523I\nz8lP7cBmApN+GkKItUKIB4Uop5+eoihVXkxGTLnMPCrk7+xPjj6HTF1muV2zMjL1TX4+MAA4JYSY\nLoRobMGYFEWp4qSUFi+Zfa3CBWxqXOHmTEoKUsotUsqBQCsgCtgihPhbCPGUEEJryQAVRal6UnJT\nyNHnlGtS8HcyLmBT4wo3Z/LtICGENzAUeBY4AMzBmCQ23+Q5PYQQJ4QQp4UQb5Zw/BEhxL9CiINC\niL1CiDvL/AoURal0ynM6aqGiBWxqrcJN2ZrSSAixDmgMLAUellJevHLoWyHE3hs8xwb4FLgXiAX2\nCCHWSymPFWu2FVgvpZRCiDDgO6DJrb0URVEqC2skBR8nHwRC9RRKYVJSAD6XUm4s/oAQwl5KmSel\njLjBc9oCp6WUZ6+0Xwk8AhQlBSll8REfZ0BNC1CUaiA6PRqN0FDbpXa5XVOr0VLDsYYaUyiFqbeP\nppTw2M5SnlMbiCn2deyVx64ihHhUCHEc+Al42sR4FEWpxKLTo6npXBOtTfkOSfo5q7UKpblpT0EI\n4Y/xjdxRCNESKKxv6wY4mSMAKeU6YJ0QojMwGbinhDieB54HqFOn/KawKYpiGVHpUdR1q1vu1/Vz\n8uNc2rlyv25lUtrto/swDi4HAB8VezwDeKuU58YBxW8YBlx5rERSyj+FEPWEED5SyuRrji0CFgFE\nRESoW0yKUolJKYlKj6KVX6tyv7afsx+7Lu4q9+tWJjdNClLKr4GvhRC9pZRrynjuPUBDIUQwxmTQ\nD+NahyJCiAbAmSsDza0AeyCljNdRFKUSScxOJEefY5Wegr+TP5m6TDLzM3Gxcyn361cGpd0+GiSl\nXAbUFUK8eu1xKeVHJTyt8JheCDEC+BWwARZLKY8KIYZfOb4A6A08KYTQATlAX6nWoCtKlRaVHgVA\nXfe65X7t4juwqaRQstJuHzlf+feWvntXZixtvOaxBcU+nwHMuJVzK4pSOUWlRQFYbUwBjGsV6nvU\nL/frVwal3T5aeOXfSeUTjqIoVV1UehSOto5Fb9DlSe3VXDpTC+LNFEK4CSG0QoitQogkIcQgSwen\nKErVcy79HHXd6iKEKL2xmfk6+iIQaq3CTZi6TqG7lDIdeAhj7aMGwBhLBaUoStUVlWad6agAWhst\n3o7eqqdwE6YmhcLbTA8Cq6SUaRaKR1GUKiyvII8LmResMshcyM/Jj/hs1VO4EVOTwo9XVh23BrYK\nIWoAuZYLS1GUqig6PRqJJMgtyGox+Dn5qaJ4N2Fq6ew3gTuACCmlDsjCWMdIURTFZNacjlrIz1kl\nhZsxtSAeGKuX1hVCFH/ON2aOR1GUKux8+nnAOtNRC/k7+5OhyyBLl4Wz1rn0J1QzppbOXgrUBw4C\nBVcelqikoChKGZxLO4evo69V34yL1ipkJ1DPvZ7V4qioTO0pRABN1WpjRVFux6nLp2jg2cCqMRRf\nwKaSwvVMHWg+AvhbMhBFUaq2AkMB59LO0cDDyklB7dV8U6b2FHyAY0KI3UBe4YNSyp4WiUpRlCon\nLjOO3IJc6ycFJ7Wq+WZMTQoTLRmEoihV36nUUwBWTwp2NnZ4OXippHADJiUFKeUfQoggoKGUcosQ\nwglj5VNFURSTnL58GqBCFKJTaxVuzNTaR88Bq4GFVx6qDXxvqaAURal6zqSeobZLbZy0Ztm08bb4\nOatVzTdi6kDzS0BHIB1ASnkK8LVUUIqiVD2nUk9Z/dZRIX8nf9VTuAFTk0KelDK/8IsrC9jU9FRF\nUUyiK9ARlRZVYZKCn7Mf6fnpZOuyrR1KhWNqUvhDCPEW4CiEuBdYBWywXFiKolQl59PPo5d6q69R\nKKRmIN2YqUnhTSAJOAwMw7ib2nhLBaUoStVSUWYeFfJ3Ni67UknheqbOPjIIIb4HvpdSJlk4JkWp\nFnQFBvL0BgxS4mJni0ZT/pvOlJfIS5HYamyp7279mUdgHFMA1LhCCW6aFIRxa6R3gRFc6VUIIQqA\nT6SU71k+PEWpOk4nZvDb8UR2nknhZEImF9JyKCwco7UR+Lo60LSWG+GBHnRtXIOmNd2ssjuZJUSm\nRNLQoyFaG621QwHA19k4T0b1FK5XWk9hNMZZR22klOcAhBD1gM+EEKOllLMtHaCiVGYGg2TDvxf4\n+u8o9kenAtDA14U2dT2p41UbFwfjr+DlbB1xl3M4EpfG5mMJfPDrCQK9HOndKoABbevg6+ZgzZdx\nW6SURF6K5J4691g7lCL2NvZ42nuqUhclKC0pDAbulVImFz4gpTx7ZX/mTYBKCopyA3+eTGLqT5Gc\nSMigfg1nxj8YwkNhtfB3v/kbfHJmHlsjE/jx34t8vOUU8347zcMtajHq7obU9al8pZ4vZl0kLS+N\nEK8Qa4dyFT9nP9VTKEFpSUFbPCEUklImCSEqRj9QUSqYtGwdU346xqp9sQT7OPNJ/5Y8GFrT5DED\nHxd7+rapQ982dTiXnMXSnedZsTua9Ycu0CcigFF3Nyo1sVQkkZciAQjxrlhJwd/Jn4tZF60dRoVT\nWlLIv8VjilItHYlLY9jSfcSn5/JSt/q8fFdDHLS3XhEm2MeZCQ83ZXjXesz//Qz/+yea9Qcv8Fr3\nxjzZIQhbG1MnEFpPZEokNsKGRp6NrB3KVfyc/TiYdNDaYVQ4pf2PaiGESC/hIwMILY8AFaWy+OFg\nHL0/+xuDlKx54Q7G3NfkthJCcb6uDkzs2YzNr3Ymoq4X7/14jJ7zdnA4Ns0s57ekyEuRBLsH42Bb\nsXo3fk5+pOalkqPPsXYoFcpNk4KU0kZK6VbCh6uUUt0+UpQrvvzrHKNWHiQ80IMNL99JeKCHRa4T\n5O3MV0+1Yf7AVqRk5fHo/B3M++0U+gKDRa53u6SUHEs5VuHGE+C/fRUSsxOtHEnFUvH7nopSgUkp\n+WjTCSb/eIwHQv355pm2+LjYW/SaQggeCK3Jr690pkdzf2ZtOknfRbuITql4JRsuZF0gOSeZ0BoV\n78aCWqtQMosmBSFEDyHECSHEaSHEmyUcHyiE+FcIcVgI8bcQooUl41EUc5u9+SRzfztN34hAPunf\nCnvb8qso7+Fkx7wBrZjTL5yTCRk8OHc7m45WrCmWhxIPAdCiRsX71S7sKagZSFezWFIQQtgAnwL3\nA02B/kKIptc0Owd0kVKGApOBRZaKR1HM7YvtZ4sSwvTeodhYaUXyI+G12TiyE3V9nHl+6T6m/3y8\nwtxOOpR0CEdbxwo3yAzg62RcwKbWKlzNkj2FtsBpKeXZKxVWVwKPFG8gpfxbSnn5ype7gAALxqMo\nZvPdnhim/BTJg6E1ef+xUKuvPA70cmLV8A4MaFeHBX+cYdCX/5CcmVf6Ey3sYNJBmvs0x1Zj6iaP\n5cfR1hF3e3fVU7iGJX9StYGYYl/HAu1u0v4Z4GcLxqMoZrH9VBLj1h2mc6MazO4bbrUeAgC56XD2\nd4jZjcOFg7yfFsMklxSy4yQ5sxzJrtkAp6BWUK8rBHcGrWO5hZajz+HkpZMMbT603K5ZVmpfhetV\niPQthOiGMSnceYPjzwPPA9SpU6ccI1OUq51JyuTF5ftp6OvC/IGtsLO1wlwNQwGc/BUOLodTm6Eg\nD2zsoWazBkMbAAAgAElEQVQYBLRB6+yDLiOX/ZFnqXUhntDEL7HZNR8cPKBFP+gwAjwCLR7m0eSj\n6KW+Qo4nFFKrmq9nyaQQBxT/nxdw5bGrCCHCgC+A+6WUKSWdSEq5iCvjDREREWpzH8UqUrPzefbr\nvdjZaPj8yQhc7Mv5byp9Huz/BnbNh0tnwcUfIp6Cpo9A7QiwtStq6gO0TMvhma/2cjY+mU86ZHNv\n/m+wd7Hxo82z0PVNcHC3WLh7EvYgEITXCLfYNW6Xn5Mfh5MOWzuMCsWS/6v3AA2FEMEYk0E/YEDx\nBkKIOsBaYLCU8qQFY1GU21JgkLy84gBxl3P433PtCPQq2z7DUkpkbi4FGRkYMjMxZGRgyMktPAqA\nsLVF4+qGjbsbNu7uaBwdC58Mketh8wS4HAW1W8PjSyCkJ9jc+Fe4prsjq4Z3YNTKgzz3dwLP3Pka\nb4+YiGb7TPhnARz7AR6eCw0tU6hu98XdNPFqgoeDZdZsmIOfkx+X8y6TV5CHvY1lpxJXFhZLClJK\nvRBiBPArYAMsllIeFUIMv3J8ATAB8AbmXxmo00spIywVk6Lcqvm/n2b7qWSmPxZKRF2vEtsYsrLI\nO3uO/LNnyIuKQn8xHl1CPPr4BHQJCcjssq0jsPH2xs7PCztxAQf7OBwb1Mah/7eIJveBiQPbzva2\nLBzcmsk/HuPLv85xOas2Mx6fg7bVEPjhJVjeGzqPga7jQGO+6bQ5+hwOJR1iYMhAs53TEgo320nM\nSiTQzfK31CoDi/Z/pZQbMe7SVvyxBcU+fxZ41pIxKMrt2n3uErO3nOSR8Fr0bWN84yjIzCL3yBFy\n/v2XnH8PkXvsGPoLxYqraTTY+vqi9ffHvnFjXDp3xsbbGxtXFzQurmhcXdA4OsGV93YhBIb8fAwZ\nmRSkp1Fw8Ty6vRvJP3+EzAw70nI8YH8Wmh/fxrnTL7je1Q2Xbt2wcXMrNX4bjeDdh5vi7WzHh5tP\nkpaj49OBrXB4/g/46TX48wO4cBCe+ArsXczyPTuQeACdQUdb/7ZmOZ+lFK5ViM+OV0nhigox0Kwo\n5UafDxkXIecyCA3Yu4Jb7avuxxd3OSufUSsPEOThwIR6BSTP+5Ssv/4i5/BhMBjXAmiD6uDUshX2\nT9THrn597OvXxy4wEGFX8jlvKj8LdsyBk3OhkQGGvIjsOBp9ajY5hw6RtXMXmb/9RsavvyIcHHC7\n7z48+vXFqWXLm55WCMHLdzfEw9mOCT8cYfCX//DFkDa4PzIPareEjWPgm0dg4CpwKrknVBb/XPwH\nW2FLa7/Wt30uSyrcq1mtVfiPSgpK1Zd4HI6shtNbIOEoFFxT4FdjC171IaAN1OsC9bqBSw0Mej1z\nZq2kz65t3JdyjMQlaSAEDmGheA97HqeWLXEIDcXW0/P2YzQUwKEVsHUyZMZDs8fgnongGYQAtI7u\naGvWxK1HD+S7E8g9fJjUdetI3/AjaT/8gFNEBD4vvoBThw43XTMxuH0Qnk5aRn97kMFf/sPSp9vh\n3uZZ46D16qdhyQMwZAO41Litl7Pzwk7CaoThpC3b2Et5U0nhekLKyjWZJyIiQu7du9faYSiVQcwe\n2DYNzmw19goC20NgG/BuCI6egITcNONMnoRjEL0TclPRZdtyObkJiYdzscnIpsDOHs977sb1nrtx\n6tDBPEmgkJTGZLV5AiQeM84i6jENAk277WLIyiJ1zRpSvvgSfWIiTh3a4zduHA6Nbr6C+LfjCQxf\nup/G/q4se6Yd7k5aOPcnLO8DPg1gyI/geGsDxPFZ8dy7+l5GtRrFs6EV/+5w12+70iWwC5PumGTt\nUCxKCLHPlDFb1VNQqp6cy7BpPBxYBk4+cPe7ED4QXP1u+BQpJdn/7OLSok/J3Lkf5CXca+XiEpqH\nW+eO2LTuAI27gJ2Zdj6TEs78Bts/gvN/gWdd4z39pr1MHkQG0Dg74/Xkk3j07Uvqt9+R9OmnnHv0\nMTwHDsB39Oj/ZjBd464mfiwc3JphS/cx6Mt/jIkhuDP0Wwb/6wfLH4fB39/SGMMfMX8YrxF4V5mf\naw2BroHEZMSU3rCaUD0FpWqJ3QvfDobMBLjjZejyxk3fyKWUZP21g+TPPiNn/35svLxw6vUoIzOC\ncHe4zGfhUTgcXwcZF0DrDE0ehNAnoH43uJVN6PMy4Oj3sOdzuHgIXGtCx1cg4ukbjmuUhf7yZZLm\nziV1xUrsgoKoOW0aTq1uPN7w+/FEhi3dRyN/F5Y90w4PJzs4th5WDYGG90G/5WWelTR883BiM2PZ\n0GuD1ct/mOKt7W+xJ2EPmx/fbO1QLMrUnoIqna1UHYdWwpL7jXP3n90C9066aULI2rWLqD59iXnu\nOXQXL+L3znjq/7aVGYF3c6jAmZcGPo7Dg+/D6KMw9CcIewJObYL/PQGzGsG64cbFZMmnjWMCJTEY\nIOUM7F8Kq4Yan7d+BOhyjGsERh2C9sPNkhAAbD09qfnuu9T5+mukTsf5QYNImvsJsqDk+Lo18WXh\nk605GZ/J0CV7yMrTQ9OecP9MOPmz8bZWGWTkZ7A7fjddA7pWioQAEOAaQEJWAvnXjjVVU+r2kVI1\n/LMIfh5jrO/zxNc3nUGTd/YsiTM/IHPbNrS1auE/+T08HnkEYWfHyt3RbDh0gTH3Nf5vPYJGA3Xv\nNH7c/4FxjOLwamOJiUMrjG1s7Iy3gJy8jfWF9HnG21iXo0B3ZX2Ciz+E9THeygpoU6bbRGXl3K4t\nwevXkzBlCsnz55Nz+DC1P5iJjcf14wTdGvsyb0BLXli+n+HL9vHFkAjs2z4HySdh5zzwaQith5p0\n3U1Rm9AZdPQI7mHmV2Q5ga6BSCRxmXEEuwdbOxyrU0lBqfx2zIXN70CTh+DxxWBb8srUgrQ0kuZ+\nwuWVK9E4OuL7+mt4Dh6Mxt7Y/mRCBhM3HOXOBj680KV+ydeytYPG9xs/pDS+ccb8AymnjQPWuWnG\nInW2DuBVz5ikfJtCQITx33L869nGxZma097HsWVLEqZM4Vzvxwn4bH6Jg9Ddm/kzo3cYr686xKvf\nHmJu/5bY3DfN+Jp+es0YuwmD3+vPrCfYPZhm3s0s8ZIsItDVuD4hJiNGJQVUUlAquwPLjQmh2WPw\n2KIS7/NLKcn4+Wfi359GwaVLePbri8+IEdh6/debyMkv4KXl+3Gxt+Wjvi3QmFL5VAio0dj4UUEJ\nIfDs2weHkCbEvjSC8wMHEfDJJzi3v75g8eOtA0jNzmfKT5G4Odry/qOhiN5fwMIuxltfw/4EZ58b\nXis2I5b9ifsZ2XJkpbl1BMbbR4AabL5CjSkoldepzbD+Zah/Fzy6sMSEoIuLI2b4cOJefQ2tnx/B\nq1fhP2HCVQkBYNKGo5xOymR233B8XSvWBvPm4BgWRt1vV2Lr50v0c8+RtuHHEts926keL3Wrz4rd\nMczdeto4dbfvUshKhjXP3njsBFh9cjUaoeGheg9Z6mVYhLeDN462jsRmxFo7lApBJQWlcko4Ct8N\nAb9m0Oeb6wZqpcHApW+Wcuahh8nesxe/t8ZR97tvcWh67eZ/sP7QBVbuieGFLvXp1PD2Fm1VZNpa\ntai7fDlO4eFcGDOGS8uXl9ju9e6N6d0qgNlbTrLuQCzUbAEPfGDct+GPmSU+J1uXzaqTq7i7zt3U\ndKlpyZdhdkIIAlwDVFK4Qt0+Uiqf3DT4dpBxDv3AVcZSFcXoEhK5OG4cWX//jXPnTtScOBFtrVol\nnup8ShZvrT1M6yBPXr234m0ZaW427u4EfvkFcaNfJWHyFKROh/fQoVe1EUIw7bFQ4lKzGbv6MLU9\nnGjb6kmI3gV/zIC6HY1jJcX8cOYH0vPTGdx0cDm+GvMJdAnkfPp5a4dRIaieglK5GAyw7gVIjTbO\nMnL1v+pw+qZNnOvZk+z9+/GfOJHAhQtvmBDy9AW89L/92GgEc/u3xNamevw6aOzsCPh4Nq7du5M4\nfQYpX3xxXRs7Ww0LB0UQ4OXI80v3ci4lGx6cBd71Ye0wyL5U1DZbl82ifxfRyrdVhd474WYCXAOI\nzYylsq3bsoTq8VugVB1/z4UTP8G9kyGoQ9HDBZlZXHj7beJGjkIbEEDw2rV49ut70wHP93+K5Ehc\nOrOeaEFtj/LbprIiEFottT/6ELcHHiBx1ockf/75dW3cnbR8NbQtNkLw1JLdpOntoPcXkJUIP75i\nnH0FfHPsG5JzkhndenSlGmAuLtA1kLyCPJJykqwditWppKBUHhcOwm+TjZvLtH+h6OGcgwc599hj\npK1dh/ewYdRd8T/s6918auHGwxf5eud5nr0zmHub3rj8RVUmbG2p9cFM3B56iKQPP+LyihXXtanj\n7cSiJ1sTl5rDyJUHKPAPh7vGGzfoObicU5dPsejfRXQP6k64b+XsJcDV01KrO5UUlMpBlwNrnwfn\nGvDwHBACqdeT9Mk8ogYOAr2eoKXf4Dv6lVJLVkenZDN29b+EB3rwRo8m5fQCKiZhY0Otae/j0q0b\n8e9NJm3DhuvatA7yYlLP5vxxMokPN52AO0ZB3U5k/jyWsb+PxtXOlbfbv22F6M2njqtx73c1rqCS\nglJZbJkIySeg13xw8iI/OprzAweR/OmnuD34AME/fI9TROmb9hWOIwgBn/RviZ2t+hUQWi21P56N\nU9u2XHhzHBm//X5dmwHt6tC/bR3mbzvDT0cSyHzoQ17xceNs+nmm3TEZL4fb34PBmmq51EKr0RKV\nFmXtUKxOzT5SKr4zvxn3FG47DFmvG2lr1pIwdSrY2lLrw1m4P/igyaea/OMxDselsWhw6zLvs1wV\nZeuyOZB4gBOXT5D9fDPaJEcRPWok+lnjqN+tJy52/1VJndizKcfj0xjz4yrqNNpEvL0dkxMTuePU\ndgjsfJOrVHw2GhuC3II4l3bO2qFYnUoKSsWWlwk/vAw+jdBHvEL8yFFkbN6MU9u21JoxHW1N0+fE\nr9wdzbJd0QzrXI/uzfxLf0IVlpqbysJ/F7Lu9DqydFkA2Agblj+kZ9LyArzemEyfwe+jq1uTQNdA\nfBx8yC3IJaPGcWycL3Ix3ZOP7vqUuw/+D7bPggb3QJ3rV0lXJsHuwZy8fNLaYVidSgpKxfb7+5Ae\nS2aLj7jYux/61FR8x7yO11NPITSm3/rZH32ZCT8cpVNDn2o/jvBHzB+8veNtMvMzuT/4fh6u9zCh\nNUJx0bqgN+i5eM9h0oe8yNR1Bja82YxTBSkcu3QMrUZLqE8zHnIcwrwNLnyndeCuJ6YhorbDuudh\n+F/XrRmpTILdg/kt+jfyC/KxszFP1drKSCUFpeK6cBDDjs9IvNieyytnYVe/PsGLFuIQElKm0ySm\n5zJ86T783R34pH9LbEypa1RFLT6ymNn7ZhPiFcLU+6bS0LPhVce1NlrqNGxF7pdLOD9wEIO+PE/Q\nsqXYuFy92Y5z/lmmboxkSbA3Tz+6CL56AH55Ex75tDxfjlkFuwdTIAuIyYihvscNCiJWA2qUTamY\nDAXkfP4i5zb5cfmvaDwHDiR4zeoyJ4Sc/AKeX7qPzDw9i55sbdxEpppaeGghs/fN5v669/PN/d9c\nlxCKcwgJofbcueSdPk3cyFFIne6q4892CuaeED/e3xjJAdEE7hxt3Onu2HpLvwyLKayQWt3HFVRS\nUCocqdORNO4ZolZewmDrTp3FX+L/zng0DmUrVFdgkIxaeYBDsanM7htOE383C0Vc8a08vpJ5B+fx\ncL2HmdZpGg62pX8vXe7sSM333iPr77+5+M6Eq1b7CiH48IkW+Ls7MOJ/B0ht+yrUDIcNoyAj3pIv\nxWKC3VRSAJUUlAom7/Rpop7oTfIP/+De3IN6GzfhfMcdZT6PlJLJPx5j07EE3n2oKfdV44HlXRd3\nMX33dLoEdGFyx8nYlGF7TY/HHsVnxAjSvv+e5E/mXXXM3UnLpwNakZiRy2trIjE8usi4nuSHl4pW\nO1cmTlon/Jz8VFKwdgCKAsaqpilLvuLcY73RRZ+jdudMai1aiY27+y2d7/PtZ/nq7yievTOYoR2r\n78YpFzIv8Nq21wh2D2ZG5xllSgiFfF56EffHHiN5/nxS16y96liLQA/eeiCErccT+TzSFrpPhtNb\nYM/19ZQqg2D3YJUUrB2AouTHxhL95BASZ8zAObwh9brH4vbkq8btLW/B0p1RvL/xOA+G1uStB8o2\nBlGVGKSB8TvGUyALmHvXXJy1N96v+maEENScNBHnO+7g4rvvkrljx1XHh95Rl/ub+zPz1xPs8+0N\nDe6FTeMh6YQ5Xka5CnYP5lz6uWpdGE8lBcVqpJSkrl7NuZ6PkBsZSc33JhDQ4ii2dUKgw4hbOue3\ne6J554ej3BPiy+y+4abtoFZFLT22lD3xexjbZmxRbZ9bJbRaas+dg339+sSNHEXuif/e8IUQTO8d\nRi0PB0auPEha99lg52zclEeff7svo1zVd69Pli6L+KzKOS5iDiopKFahT0oidvgLXBz/Dg6hodRb\n/wMerocQGRfh4Y9L3EWtNN/uiebNtYfp0qgGnw5sVa1LWJy+fJq5++fSLbAbvRr0Mss5bVxcCFy4\nAI2LCzHPD0MX/98bp7ujlnn9r4wv/JyAfHgOxP8L26aZ5drlpZGXcU+N6ryIzaK/NUKIHkKIE0KI\n00KIN0s43kQIsVMIkSeEeN2SsSgVR/ovv3D24Z5k7dqF31tvUWfJYrQkwO6FEPG0SRvEFyel5LNt\nZxi75jCdG9Zg4eDW2NuW/d55VaEr0DHur3G42Lnwbod3zVrOWuvvT+DCBRgyM4kZNpyCzMyiYy0C\nPXjz/hC2RCawJKU5tHoS/poN5/822/UtrZGnMSmcuFz5bn2Zi8WSghDCBvgUuB9oCvQXQly7F+Il\nYCQwy1JxKBVHQVoaca+PIe6V0WgDAwletxavJwcjpMFYn9+5Btw9oUznNBgk72+MZMYvx+nZohaf\nPxmBg7b6JgSAzw59xvFLx5nYYSLejt5mP79DkybUnjPHuIZh1CtXrWF4umNd7gnxY9rPkRxu/qZx\nXGjtMONueZWAs9aZAJcATlxSScES2gKnpZRnpZT5wErgkeINpJSJUso9gK6kEyhVR+b2vzj7cE/S\nf/kFn5EvX9nzoJ7x4O5FcPEQ9JgOjh4mnzMjV8fzS/fy+fZzPNkhiI/7hlfrW0YABxMP8uWRL3m0\nwaN0q9PNYtcxrmGYRNaOHVycOLFoYFYIwawnwqjhYs+Lq0+Q9dBnkB4HP4+1WCzm1sizkbp9ZCG1\ngeI7VsReeUypRgxZWVycOJGY555D4+ZK3W9XUuPFFxG2VyqspMbAb1OgYXdo9qjJ5z2blEmvT3fw\n+4kkJvVsxqSezar1oDIYK56+9ddb1HSuyRtt3rD49Tx698bnxRdIW7OWlAUL/nvcyY5PBrTkQmou\nY3bZITu9BodWwJG1NzlbxdHYqzHRGdHk6HOsHYpVVIo/q4QQzwsh9goh9iYlqe3yKovs/fs5++hj\npH77HV5PPUXwmjU4Nmv2XwMpYeMYkAZ4YBaYcO9bSsnK3dE89MlfXM7WseyZdgy5o26l3QbSnGbt\nnUVsRixTOk65quS1Jfm8/DLuj/Qkac5c0n74oejx1kFejLmvMRsPx7Pcvg/UjoANr0DKmXKJ63Y0\n9myMQRo4ffm0tUOxCksmhTig+Dy4gCuPlZmUcpGUMkJKGVGjRg2zBKdYjiE/n8QPP+T8oMFgMBD0\nzdf4jX0Djb391Q0j18PJn6HbW+AZVOp5kzLyeH7pPt5ce5gWAR78+PKddKhv/nvmldGfsX+y6uQq\nhjQbQoR/6ZsNmYsQgpqTJ+PUrh0Xxr9D1q5dRcee71SPro1r8N7G05zsPAc0GvjuScjPLrf4bkV1\nn4FkyaSwB2gohAgWQtgB/YDKWy1LMUluZCRRjz9Byudf4NG7N8Hff49TmzbXN8xJhY1vgH8otH/x\npucsMEi+/juKuz7cxh8nkhj/YAjLn21HLQ9HC72KyiUlJ4V3drxDQ8+GjGh5a+s7boewsyPgk7nY\nBdUh9uWR5J06BYBGY6yP5OmsZdiGZHIeXggJR+GnVyt0GYzaLrVx1jpz/NJxa4diFRZLClJKPTAC\n+BWIBL6TUh4VQgwXQgwHEEL4CyFigVeB8UKIWCFE9a1aVolJvZ7khYs416cv+suXCFjwGTUnv4eN\nyw1W0W6dBFmJ8PBcsCm5gruUkm0nEnn4k794d/1RWgR48PMrnXi2U71qP35QSErJxJ0TycjPYHqn\n6djb2Jf+JAuwcXOjzsKFCAd7oocNQ5eYCIC3iz1z+7XkfEoWYw/5Iru8YRxf2PeVVeI0hUZoCPEK\n4WjKUWuHYhUWHVOQUm6UUjaSUtaXUk698tgCKeWCK5/HSykDpJRuUkqPK5+nWzImxfzyo6M5P/hJ\nkmbPxvWeu6m3fj2uXbve+AnRu2DvYmg3HGq3uu6wlJK/zyTTZ+FOhi7ZQ1qOjnkDWrL0mbbUr1E+\n98ori7Wn1rItZhujWo0qmmNvLdratQlcsICC1DRihv+3hqFdPW9G39OI9Ycu8J1Tf6h/N/z8BsTu\ns2q8NxNaI5Tjl46TX1C5VmSbQ6UYaFYqpqIyFb0eJe/0aWp98AEBs2dj6+l54yfp843lld0Dodvb\nVx3K1xtYdyCWnvN2MODzf4i+lM3kXs35/fWuPBRWSw0mX+N8+nlm7JlBO/92DG462NrhAODYrBkB\nH88m7+QpYoe/gCHHOIPnxW4NuLOBDxM2HOdUp4/B1R9W9jfOPquAwnzC0Bl01fIWkkoKyi3Rp6QQ\n+9KIq8pUuD/8UOlP3PExJB2HBz8Ee+Nf/WeSMpn16wk6zviN0d8eIkdXwNRHm/PHmG4Mbh9U7dce\nlCRXn8tr215Dq9Ey5c4paETF+R65dO5MrRnTyd63j9iRo5D5+dhoBLP7huPqoOWFtefIeWKFscz2\nin6Ql2HtkK8T6hMKwOHkw1aOpPxVnP9JSqWR8dvvnO35CFl//YXvm2ONZSpq1iz9ifGH4Y+Z0Owx\nUgO6sXTXeXp9uoO7P/yD+dtO07yWG9883ZbNozszsF1QtV+ZfDNT/5nKicsnmNZpGv7OFW+vCPcH\nH8R/0kSytm8n7o2xyIICarjaM6dfOGeSMnnn7wJ4YgkkRhoL5xkKrB3yVfyc/fB18uXfpH+tHUq5\nU3s0KyYzZGWRMH0GqatWYd+kCbWWLMahkYn3sfX5GNYNR6d1462swWyYupX8AgNN/F15+4EQHgmv\nha9b2XZWq67WnlrL96e/Z1jYMDoHdLZ2ODfk2acPhswsEmfO5KKTEzWnTKZjAx9e7taAub+dpkO9\nFvS+fwZsfB1+fcu4or0C3SIM8wmrlj0FlRQUk2QfOMCFsW+ii4nB+9ln8Bk5Eo1d6fsdSyk5GJNK\n6k8T6ZZwhBH5r7JfX8Cg9kH0bl2bpjXd1FhBGRxMPMjUXVPpULMDL7R4wdrhlMr76acwZGaQPP8z\nNA4O+L0znlH3NOKfc5cY//0RWrzcnwbtz8Ku+eDkDV0svxLbVGE1wtgSvYXknGR8HH2sHU65UUlB\nuSmp05E0fz4pCxeh9fcn6JuvS153cI241BzW7Itl3YE4XFIOs87uG/5x707/B4Yzv2ENtDbqzmVZ\nxaTHMPK3kfg7+zOz88xb2kXNGnxefhlDTi6XlixBGgrwnzCBOf1a8sDc7by0fD/fv/gejjmp8PtU\ncHCHdsOsHTIAEX7GRYB74/fSI7iHlaMpPyopKDeUd/YsF8a8Qe7Ro7j36oXf+LexcbnxlFCDQfLH\nqSSW7zrPb8cTMUjoFOzMPJagEX60e+HzMhW8U/6TmpvKi1tfRCKZf898PBwqz/dRCIHvG2MQtjak\nfP4FFBTgP2kSH/VpwVNf7eH1NYeZ13cuIjfNOFXV3g3C+1s7bEK8Q3DWOrM7frdKCkr1JqXk8vL/\nkfjBB2gcHak9Zw5u93W/YfuMXB0rdkezdNd5Yi7l4ONix4tdG9CvbSABf70F+87AoLUqIdyibF02\no34fRVxmHF90/4Igt9JLglQ0QghqvPoq2NiQsmAhUl9AlymTGdujCdN/Pk5jP1dGPr4Y/tcHvn8B\nZAG0HGTVmG01tkT4RbA7frdV4yhvKikoV9ElJHLx7bfJ+usvnDt1oubUKWh9fUtsm5KZx5IdUXyz\nM4r0XD1tg714474m3NfM3ziN9Og62LcEOo6CBneX7wupIvIK8hj1+ygOJh1kZueZtPK7frFfZSGE\noMaoUQhbLcnz5iHzcnlu2jROxmfw0eaTNPR14f7+K2HlAPjhJeOU1bbPWTXmNv5t+CP2DxKyEvBz\n9rNqLOVFJQWlSPovv3Dx3YnIvDz8JryDZ//+JQ4CJ2fm8envp1mxO5o8vYEezfwZ3qU+LQKL9QQu\nnYP1IyGgDdz1Tjm+iqpDZ9Dx+rbX2XVxF1M6TuG+uvdZO6TbJoSgxoiX0Dg6kPjBLPSXLzPlo485\nl5LFq98dInB4B5r3XwmrhhpnJelz4Y6XrRZvW3/jLoC743fzcP2HrRZHeVKjfQoF6enEvfEGca+M\nxq5OHYLXrsVrwIDrEkJmnp7Zm0/SZebvfLPzPA+F1WLz6C58Nqj11QlBlwurnwIE9P7ylvZbru70\nBj3jto9jW+w2xrcbzyMNHin9SZWI9zPPUHP6NLJ37yH+maf47P4gPJy0PP3VHmIyDNDnG2jaCzaN\nN27QY6V1DI29GuPl4MX2uO1Wub41qJ5CNZe16x8ujBuHPjERn5dewmf4MIT26jdxXYGBZbvOM++3\n06Rk5fNAqD+vdW9cch0iKY1ba144AH2Xm1QSW7markDHG3++wZboLbwe8Tp9m/S1dkgW4dGrF7Ze\nXsSOegXDc0+x5P3Z9NkYx5DFu1k1vAPejy+GTbVh16dw+Tz0/qJoFXx50QgNnQM6s/X8VnQGHVpN\n1f8DR/UUqilDXh4JM2YS/dRTaOzsqLvif9R4ecR1CeGvU8ncP2c7kzYco7G/Kz+81JH5A1vfuDDd\nrt7oDE0AABQJSURBVM+MVTC7joMQE8peKFfJ1ecy6vdRbInewtg2YxnSbIi1Q7Iol86dCfr6KwxZ\nWYiXnuar5gXEpebw9Fd7yNJJ6PG+cQOmU7/CkvuNyaGcdQ3sSoYug/0J+8v92tagkkI1lHvsGFGP\nP8GlJUvw7N+P4HVrcQwLu6pNXGoOLy7fx6Av/yFfb+CLJyNY/my7q28TXev0Vtj0NjR5CDpXnEVI\nlUW2LpsRW0fwV9xfTOgwgUFNrTv7prw4hoVRd9UqtLVq4ThhDF85n+JwbCrDl+0jV1dgHGzuv9KY\nEBZ2hpObyjW+DjU7YG9jz7aYbeV6XWtRSaEaMeTlkTj7Y8490Qd96mUCFy3Ef8IENE5ORW1ydQV8\nsvUUd3+4jf+3d+fxUZX3Hsc/v1kymckOJIAh7CGsCgISFBEQkOKCaL1A9Uq1qXitSH3lWmv7ar23\nfTXYuhW1Ci4oFxCqVFtUSkEWIypLiJQ1ArKEhOxkIzOTmck8948zDQELWSBMhjzv1ytMZubk5HvI\nzPnNec5znmdjTjHpk/qx7vGxTBzY+cJXHudnG7NqJQyE6QuNWba0Jqv2VDNn/Rx2FO3gd2N+xz39\n7gl2pMsqrFsiPVe8S9TEicQueY1lpf9g+4F8fvx/WUZh6HcLzNlsjK777j2w4bdQ570s2RxWB6O7\njmbd8XXUtbExmlqDfue2E65duzh6192ULVpEzB130Ofjj4kce/a4ORsOFDH5xUyeX3+QCf0T2JA+\njrk3Jzc+MF3Zt7D8HnB0gHtXgS2qFbfkylPhriBtXRp7S/fy7Nhn200vl3OZHA4S//ginR6bS9wX\nG1iZvYi8nXvOFIYOvSFtvXH9wufPwVuToeSby5Lt1j63UuwsbhfXLOiicIXzu1wUzX+GY7N+gN/p\nJOmNN7hqfgbmmJj6ZY6X1fDgOzv40ZIsrGZh2Y9G8eq9w0lsynSXFSdg6XRAwX0fQnQTRkvV6hXW\nFDJ77WwOlx9mwYQFTO55/osE2wMxmYh/5BG6L36LCK+TVz5/iej1q3lg8Xaq3V6w2mHan+Ced6D8\nGCy8Eb58pdV7J41PGk+UNYqPvv2oVX9PW6CLwhVKKUX1p59y5NbbOLVkCbEzZ9D7o9VE3jimfhm3\nt44X1h9k0ouZbDtSxi+m9ufv88YyJrmJg3+VH4O3pxrzLd+7Cjr1bZ2NuUIdqTjCfWvuo9hZzMJJ\nC9v0iKeXW8To0fT+61+JGjWKR3d9wC0rniPthbUUVbmNBQZNh0e2Qp8JxnmstyYZPd5aic1s45Ze\nt/Bp7qdUe9re/A+Xki4KVyDP8eOcmDOHvEfnYoqIoMeypXR9+un6cYuUUqzfX8TEFz7jpQ2HmDKo\nCxvSx/HQ2D5Nn9Cm7FujIHiqYfbqfzutpnZ+u4p3cf/a+/H5fbw95W1Gdml8kMH2xtKxI0mvLyLh\nySe57tRhnljxa55//HlyCgIz9kZ1hlkrYPrrUJELr4+HT9LBVd4qeb7f7/u4fC4+OPRBq6y/rRCl\nVLAzNMuIESNUVlZWsGO0SXWnT1P2xpucWrwYCQsj/rG5xP3gB2d1Mz1aWsNvP97PxpxikhMi+d9p\ng7i+TzOHBc7dagxFAHD/36DLkEu4FVe+zLxM0jenE++IZ9GkRSRFJQU7UpvnOXaMw088hezZxa7O\n/Yh78immTE09s4CrAjZlwI43jJFWb0yHkT8G66Wdo+OBtQ+QfzqfNXetwWIKrcu8RGSnUmpEo8vp\nohD6lMdD+co/U/raa9SVlxN9++0kPPHfZ41ZdKrGw0sbDrFs63FsFhM/ndiPH97Qs/lDWO9+zxiX\nJiYJ7n0fOva5xFtz5VJKsfzAcp7NepaUuBRenfhquxqn/2Ipv5/ct5dStmABVm8tR8ZMZfLvf4m9\nQ4Nu0gW74dP/gW83QHSicb3MNbPAfGl24JtyN/HYpsfIGJMRch0CdFFoB5TPR9Unn1Dy8it48/Jw\npKaSkJ6Ofcjg+mXc3jre+fIYf9p4mBqPj5nXdeenE5NJiGrmJyiv25gdK+st6DEGZiw1ehtpTeL1\ne8nYlsGqg6uYkDSB+TfOx2F1NP6D2nc4i0rY+MRv6LV9A87wCCIfTCPlodmYwhu8po9mGsUhfyfE\n9jDGTxp2n3Gi+iL4lZ+ZH8+koraCj6Z/hM1su7iNuYx0UbiC+Wtrqfzwr5S9+SbevDxs/fuTkJ5O\nxJgb6q8lcHvrWLk9l4WfHaGwys3N/RP4+ff6k9y5Bd1FC/fAhw9D0V5jxNMJv9LjGTVDibOEn2X+\njKyiLNKGpDF32FxMok/nXazNH31O6fPPMajwILXRcXR9ZA7xs2ZisgV21ErBN2tgy4uQtwMcnSD1\nYRj+AES0/AhtW8E20talMe/aeaQNSbtEW9P6dFG4AvlKSqhYtYryd1fgKykh/Oqr6fTwHCLHjUMC\nF4vV1PpYsT2XRZlHKKmu5bpeHXh8Yj9G9+nY/F/odcFnv4cvXjKOCu58DZInXeKturJ9kf8Fv9jy\nC5xeJ78e/euQa3Jo607VeHhtwfskf/IuQ8qOUBcdS8K9s4ibNfNM86lScPxLozgcXg/mMBhwB4x4\nAHrc0KJ5oedtnMeW/C28d/t79IkNjSZUXRSuEMrvx7kji4o/r6Rq3Xrw+Yi4/no6/jgNR2pq/ZHB\n0dIaln51nPd3nqDa7eOGvh2ZOyGZ1N4tKAZ1PmP8os3zoSofht4Hk3+rm4uawel18vLXL7PswDL6\nxvbluZueC5mdRyjacrCE5a9/yMisdYwq3I9YzMRMuYWY6dOJSE1FzIELMItzjDk+/rkC3JXQqR8M\n+Q8YfFezzo+Vukq562930TmiM0umLAmJpkBdFEKYUgr3/v1UfbKGqjVr8BUWYoqOJnb6dGJnzsDW\nqxdgHBWs31/EB1/nk3mwBItJmDqkK7Ov78nwHnHN/8VeN+x5H758GUq/gcThMOk30HNM4z+rAcbf\n7rO8z8jYlkFhTSEzUmaQPiKdcMul7QWjfZevzs+K7bmsWr2V0f/cwOT8bOy1TswJnYmddjtRU6YQ\nPnCg8UHK4zQmgfp6KeR+Zayg61AYfDekTDUKRCNHEJl5mczdOJexiWN5cfyLbb43ki4KIcbvclGz\nbRs1mZmc/iwTb34+WCxEjhlD9G23EXXzBEx2O1VuL18cKmXtvkLW7SvC5a3jqphwZozszqzrkkiI\nbsHOp2ifUQyyl4KzFDoPhpuehAG3t+jQur3KLspmQfYCsouz6Rvbl6dHP83QhKHBjtXu1Prq+MvO\nfBZvzKHL/iym5GczrPAAJr8fS5cuRI4fR9S4cdiHj8AcGQGVeUaB2LMKCnYZK4nrCcmToe8k6DH6\nvEO3rMhZQca2DMZ1G8cfbvoDdsvFnchuTW2iKIjIFGABYAbeVEo9c87zEnh+KuAEfqiUuuD4tFdK\nUfCVleHatQtndjau7K9x792L8noRu52I0aOJHHcTUZMm4YmIYndeJTuPl5N5sISdx8vx+RUxdiu3\nXt2VO4cmMqJHHCZTM3bePo9x4u3IJsj5BIr3g5iNQcdS/wt63qiLQRPV1tWy7tg6Vn6zkt0lu+lk\n78Scq+dwd/LdWPXJ+KDy+xVbj5SxfHsuW7MPM+zkfm4qzeGawhysnlowmQgfMADHiBHYh19L+MCB\nWO0e5NsNcGi90YPJ5wIxQedBkJQK3VPhqmFG0TAZTVIrc1aSsS2D3jG9eWbsM/Tv0D+4G34eQS8K\nImIGDgKTgDxgBzBLKbW/wTJTgbkYRWEUsEApNepC6w2loqC8XrxFRXjz8vHmnaD20CFqDx3CffAQ\ndaWlAIjVSvigQdivHYb7mpEc79aPQ+UeDhdXszuvkpzCaur8xt9oQNdoxqXEMz4lgWHdY5t2jYHP\nAxXHoXA3FPwTTu6CvCzw1hgv9qRRxiHzoOkX1SOjvVBKUVBTwM6inWw+sZkt+Vtw+pz0jO7JjJQZ\n3N3v7jb9abG9qnR52ZRTzNq9hXx54CS9Cw8zuOwo11Udo1fJMSw+Y8RViYgkvH8K4SkphCUlYg2v\nJkydxOrch6ko23jfAFgdEN/fKBYJA/hSPPzq2IcM6jSYlya+GsQtPb+2UBRGA/+jlLolcP8pAKXU\n/AbLLAI2K6VWBO5/A4xTShWcb72XsygopcDrxe9243e5US4nfpcLv8uN3+VEud3UVVZRV1FBXXk5\nvvJyvKdO4TtVjq+gAH9JMfj99evz28JxJ3anqkt3SuK78W3HHuyNuIo8Zx0FlW48vjPLxjmsDOga\nzbXd47i2RyzDkuKICxfw1Bi9grzOM7euCqPZp6b0zG1FrjH+fFUeqMB6zWHG0NbdRkDvccYRgf0C\n8yO0Q37lp9pTTUVtBRW1FVTWVpJ/Op/cqlxyq3M5UHaAElcJAB3DOzK++3gm95jMqK6jdDfTEOHx\n+dmTX8nWI2XsOHaKgyfKiMo7Su+qAnpVnqRv1Ul6VhVi97rP+jlfdCwS7cAaacFq82Gz1hCmyrBw\nGpNV4QoDZfUTFxGJKS4BieqARMUbXzEJSHQCYo+CsChjBrmwSAiLMJqmLDYwWY33qMncKkfqTS0K\nrXlmJBE40eB+HsbRQGPLJALnLQotdfrzzynKmI/y14GvDuX3Q10dqq7uO7f/eq7hDr0xXjFTZYug\nKiyCyjAHpfZEipMHU+ToQLEjjkJHB4occajAjiPMbKIL4XSxWBmaFMWUQeF06+AgOSGS5IRIOkY2\nuCjm7anw3jbw+xoPYg4z+mPHJBqHuh16QVwv6DwQ4geAJay5/3XtRpmrjAnvT8Cvvvt3t1vsJEUl\nMbLLSIYmDOWa+GtIiUvBbGpkWHGtzQmzmBjeI+6szhhlp2s5UFDN0dLT5JS7+PRUDadOliAn83CU\nFZFQXUondyWx7mo6FFUTW+sizl2DzW8Bzv5gVQFAZeDr6Nm/XBRiUogZRBQICBj/iKqvBWIynjBZ\nodedyrguSMww8kEY83gr/c8Y2vbp8gAReQh4CKB79+4tWoc5Kgpb/xTEZEYsZmh4azaD2Wz09beY\nEZO5/lYsZsRuxxRux+SwI+HhmOwO/DYbC7/Kxx8ZiT86FlNEBGFWMzaLiTCziWiLiU4WE8MsJmLs\nVqLtVqLDrUTbLUSHW3GEmS88aU1DA6cZO3irHawRgVvHmdvwGIjoaBQDW5Q+H9BC0bZo0oakEWuL\nJdYWS4wthlhbLF0iuhBvj2/630sLOR0jbYxJtv3bEYKVUlTX+iiv8VBW4+G024fTU8dRjxf3aSee\nqmp8VaeNKUVdNZicNYjLhXi94PWAz4vF4yROahnZ1YZyO1FuF8pbazTvemsDH0R94D/7Q6mYgf6D\njAmFlN+4OruV6eYjTdO0dqCpzUet2Qi6A0gWkV4iEgbMBFafs8xq4H4xpAKVFyoImqZpWutqteYj\npZRPRB4F/oHRJXWxUmqfiDwceH4hsAaj59FhjC6pD7RWHk3TNK1xrXpOQSm1BmPH3/CxhQ2+V8BP\nWjODpmma1nS6D52maZpWTxcFTdM0rZ4uCpqmaVo9XRQ0TdO0erooaJqmafVCbuhsESkBjl/i1XYC\nSi/xOi8nnT+4Qjl/KGcHnb85eiil4htbKOSKQmsQkaymXOnXVun8wRXK+UM5O+j8rUE3H2mapmn1\ndFHQNE3T6umiYHg92AEuks4fXKGcP5Szg85/yelzCpqmaVo9faSgaZqm1Wv3RUFEpojINyJyWER+\nHuw8zSEiSSKySUT2i8g+EZkX7EzNJSJmEflaRD4OdpbmEpFYEVklIjkiciAwh0jIEJHHA6+bvSKy\nQkTCg53pQkRksYgUi8jeBo91EJH1InIocBt3oXUE03nyPxt4/ewWkQ9FJOjz47broiAiZuBPwPeA\ngcAsERkY3FTN4gPSlVIDgVTgJyGWH2AecCDYIVpoAbBWKdUfuIYQ2g4RSQQeA0YopQZjDG8/M7ip\nGvUOMOWcx34ObFBKJQMbAvfbqnf4bv71wGCl1NXAQeCpyx3qXO26KADXAYeVUkeUUh5gJTAtyJma\nTClVoJTKDnxfjbFTSgxuqqYTkW7ArcCbwc7SXCISA4wF3gJQSnmUUhXBTdVsFsAuIhbAAZwMcp4L\nUkplAqfOeXgasCTw/RLgzssaqhn+XX6l1Dql1L8mX98KdLvswc7R3otCInCiwf08Qmin2pCI9ASG\nAduCm6RZ/gj8DPAHO0gL9AJKgLcDzV9vikhEsEM1lVIqH3gOyAUKMGY9XBfcVC3SucFsjYVA52CG\nuUgPAn8Pdoj2XhSuCCISCfwF+KlSqirYeZpCRG4DipVSO4OdpYUswLXAa0qpYUANbbvp4iyBtvdp\nGMXtKiBCRO4LbqqLE5i0KyS7U4rILzGag5cHO0t7Lwr5QFKD+90Cj4UMEbFiFITlSqkPgp2nGW4A\n7hCRYxjNdhNEZFlwIzVLHpCnlPrXkdkqjCIRKiYCR5VSJUopL/ABcH2QM7VEkYh0BQjcFgc5T7OJ\nyA+B24B7VRu4RqC9F4UdQLKI9BKRMIwTbauDnKnJREQw2rQPKKVeCHae5lBKPaWU6qaU6onx/75R\nKRUyn1SVUoXACRFJCTx0M7A/iJGaKxdIFRFH4HV0MyF0oryB1cDswPezgb8FMUuzicgUjCbUO5RS\nzmDngXZeFAIneB4F/oHxhnhPKbUvuKma5QbgPzE+Ze8KfE0Ndqh2ZC6wXER2A0OBjCDnabLAEc4q\nIBvYg7EvaHNX1zYkIiuAr4AUEckTkR8BzwCTROQQxtHPM8HMeCHnyf8KEAWsD7x/F15wJZeBvqJZ\n0zRNq9eujxQ0TdO0s+mioGmaptXTRUHTNE2rp4uCpmmaVk8XBU3TNK2eLgqapmlaPV0UNE3TtHq6\nKGiapmn1/h9fd/GqKb6YQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f9a9be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# better way: \n",
    "for column in student_grades.columns[1:5]: \n",
    "    student_grades[column].plot(kind='density', label=column, legend=True)\n",
    "# what conclusion can we draw from this graph?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Interquartile Range (IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAwxGuAEGI9wAgxFugMEIN8BghBtgMP8LRgKJlWep9egAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f753da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visual representation for exam grades:\n",
    "student_grades['exam'].plot(kind='box');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.125"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q1 (25% percentile)\n",
    "student_grades['exam'].quantile(0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25    5.125\n",
       "0.50    6.050\n",
       "0.75    7.325\n",
       "1.00    9.200\n",
       "Name: exam, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q1 (25%), Q2 (50%), Q3 (75%), Q4 (100%)\n",
    "student_grades['exam'].quantile([0.25, 0.50, 0.75, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# calculate IQR (distance between Q3 and Q1) in one line of code\n",
    "iqr = student_grades['exam'].quantile(0.75) - student_grades['exam'].quantile(0.25) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.825000000000001 10.624999999999998\n"
     ]
    }
   ],
   "source": [
    "# lower limit IQR = Q1 - 1.5 * IQR\n",
    "lower_limit = student_grades['exam'].quantile(0.25) - 1.5 * iqr\n",
    "# upper limit IQR = Q3 + 1.5 * IQR\n",
    "upper_limit = student_grades['exam'].quantile(0.75) + 1.5 * iqr\n",
    "print(lower_limit, upper_limit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Masks / Filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>student</th>\n",
       "      <th>assignment1</th>\n",
       "      <th>assignment2</th>\n",
       "      <th>homework</th>\n",
       "      <th>exam</th>\n",
       "      <th>resit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sem</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tess</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  student  assignment1  assignment2  homework  exam  resit\n",
       "0     Sem          6.7          7.5       9.5   7.8    NaN\n",
       "1   Lucas          8.2          8.2      10.0   6.9    NaN\n",
       "2    Tess          9.0          9.8      10.0   9.2    NaN\n",
       "3    Adam          5.0          4.9      10.0   3.4    5.8\n",
       "4   James          3.4          5.3       8.0   4.9    6.2"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_grades.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2      True\n",
       "3     False\n",
       "4     False\n",
       "5      True\n",
       "6      True\n",
       "7      True\n",
       "8      True\n",
       "9     False\n",
       "10     True\n",
       "11     True\n",
       "12     True\n",
       "13    False\n",
       "14     True\n",
       "15     True\n",
       "16     True\n",
       "17    False\n",
       "18     True\n",
       "19     True\n",
       "Name: resit, dtype: bool"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average grade of all students who didn't take the resit? \n",
    "student_grades['resit'].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>student</th>\n",
       "      <th>assignment1</th>\n",
       "      <th>assignment2</th>\n",
       "      <th>homework</th>\n",
       "      <th>exam</th>\n",
       "      <th>resit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sem</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tess</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Max</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fleur</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Zoë</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Luuk</td>\n",
       "      <td>8.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ilja</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dirk</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pieter</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sandra</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Anne</td>\n",
       "      <td>6.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Arjan</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pim</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Tom</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student  assignment1  assignment2  homework  exam  resit\n",
       "0      Sem          6.7          7.5       9.5   7.8    NaN\n",
       "1    Lucas          8.2          8.2      10.0   6.9    NaN\n",
       "2     Tess          9.0          9.8      10.0   9.2    NaN\n",
       "5      Max          4.5          4.4      10.0   5.6    NaN\n",
       "6    Fleur          7.0          8.6      10.0   5.9    NaN\n",
       "7      Zoë          3.4          7.4      10.0   7.3    NaN\n",
       "8     Luuk          8.4          5.4      10.0   6.1    NaN\n",
       "10    Ilja          6.3          6.6       7.0   5.4    NaN\n",
       "11    Dirk          8.4          7.3      10.0   6.0    NaN\n",
       "12  Pieter          7.1          5.3      10.0   8.4    NaN\n",
       "14  Sandra          5.9          8.3      10.0   7.1    NaN\n",
       "15    Anne          6.1          7.0      10.0   7.4    NaN\n",
       "16   Arjan          6.4          5.3       9.0   5.2    NaN\n",
       "18     Pim          8.0          8.2      10.0   7.3    NaN\n",
       "19     Tom          7.3          7.9      10.0   8.1    NaN"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assign filter to variable\n",
    "filter = student_grades['resit'].isnull()\n",
    "student_grades.loc[filter] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>student</th>\n",
       "      <th>assignment1</th>\n",
       "      <th>assignment2</th>\n",
       "      <th>homework</th>\n",
       "      <th>exam</th>\n",
       "      <th>resit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sem</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tess</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Max</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fleur</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Zoë</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Luuk</td>\n",
       "      <td>8.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ilja</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dirk</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pieter</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sandra</td>\n",
       "      <td>5.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Anne</td>\n",
       "      <td>6.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Arjan</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pim</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Tom</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student  assignment1  assignment2  homework  exam  resit\n",
       "0      Sem          6.7          7.5       9.5   7.8    NaN\n",
       "1    Lucas          8.2          8.2      10.0   6.9    NaN\n",
       "2     Tess          9.0          9.8      10.0   9.2    NaN\n",
       "5      Max          4.5          4.4      10.0   5.6    NaN\n",
       "6    Fleur          7.0          8.6      10.0   5.9    NaN\n",
       "7      Zoë          3.4          7.4      10.0   7.3    NaN\n",
       "8     Luuk          8.4          5.4      10.0   6.1    NaN\n",
       "10    Ilja          6.3          6.6       7.0   5.4    NaN\n",
       "11    Dirk          8.4          7.3      10.0   6.0    NaN\n",
       "12  Pieter          7.1          5.3      10.0   8.4    NaN\n",
       "14  Sandra          5.9          8.3      10.0   7.1    NaN\n",
       "15    Anne          6.1          7.0      10.0   7.4    NaN\n",
       "16   Arjan          6.4          5.3       9.0   5.2    NaN\n",
       "18     Pim          8.0          8.2      10.0   7.3    NaN\n",
       "19     Tom          7.3          7.9      10.0   8.1    NaN"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or in one go: \n",
    "student_grades.loc[student_grades['resit'].isnull()] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.913333333333332"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the mean exam grade for this subset of students\n",
    "student_grades.loc[student_grades['resit'].isnull(), 'exam'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.16"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verify result with overall mean\n",
    "student_grades.loc[:, 'exam'].mean()\n",
    "# does this value make sense compared to the previous block? Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# BONUS - what is the highest grade for the first exam attempt of all students who took the resit\n",
    "# Try to find the answer without visually observing the data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    NaN\n",
       "2    NaN\n",
       "3    5.8\n",
       "4    6.2\n",
       "Name: resit, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# investigate resit column\n",
    "student_grades['resit'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "4    False\n",
       "Name: resit, dtype: bool"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the filter we used in the previous exercise\n",
    "student_grades['resit'].isnull().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4     True\n",
       "Name: resit, dtype: bool"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# negate the filter \n",
    "~student_grades['resit'].isnull().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     3.4\n",
       "4     4.9\n",
       "9     4.3\n",
       "13    2.9\n",
       "17    4.0\n",
       "Name: exam, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply filter and select column to only get students who took the resit\n",
    "student_grades.loc[~student_grades['resit'].isnull(), 'exam']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.9000000000000004"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take the maximum value of the exam (not the resit!) column\n",
    "student_grades.loc[~student_grades['resit'].isnull(), 'exam'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Sorting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     6.7\n",
       "1     8.2\n",
       "2     9.0\n",
       "3     5.0\n",
       "4     3.4\n",
       "5     4.5\n",
       "6     7.0\n",
       "7     3.4\n",
       "8     8.4\n",
       "9     5.7\n",
       "10    6.3\n",
       "11    8.4\n",
       "12    7.1\n",
       "13    7.3\n",
       "14    5.9\n",
       "15    6.1\n",
       "16    6.4\n",
       "17    6.1\n",
       "18    8.0\n",
       "19    7.3\n",
       "Name: assignment1, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# who are the top 3 performing students for assignment 1?\n",
    "# grades assignment 1 (unsorted)\n",
    "student_grades['assignment1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     9.0\n",
       "8     8.4\n",
       "11    8.4\n",
       "1     8.2\n",
       "18    8.0\n",
       "19    7.3\n",
       "13    7.3\n",
       "12    7.1\n",
       "6     7.0\n",
       "0     6.7\n",
       "16    6.4\n",
       "10    6.3\n",
       "15    6.1\n",
       "17    6.1\n",
       "14    5.9\n",
       "9     5.7\n",
       "3     5.0\n",
       "5     4.5\n",
       "7     3.4\n",
       "4     3.4\n",
       "Name: assignment1, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort grades \n",
    "student_grades['assignment1'].sort_values(ascending=False)\n",
    "# tip: use SHIFT + TAB (to view common function parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     9.0\n",
       "8     8.4\n",
       "11    8.4\n",
       "1     8.2\n",
       "18    8.0\n",
       "19    7.3\n",
       "13    7.3\n",
       "12    7.1\n",
       "6     7.0\n",
       "0     6.7\n",
       "16    6.4\n",
       "10    6.3\n",
       "15    6.1\n",
       "17    6.1\n",
       "14    5.9\n",
       "9     5.7\n",
       "3     5.0\n",
       "5     4.5\n",
       "7     3.4\n",
       "4     3.4\n",
       "Name: assignment1, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change to descending order (from high to low)\n",
    "student_grades['assignment1'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     9.0\n",
       "8     8.4\n",
       "11    8.4\n",
       "Name: assignment1, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select only the first 3 records (index 0, 1 and 2)\n",
    "student_grades['assignment1'].sort_values(ascending=False)[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# So what is our final answer? Is that the answer we are after? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# No! We want to know the student names; not just the row indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>student</th>\n",
       "      <th>assignment1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sem</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lucas</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tess</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adam</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Max</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fleur</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Zoë</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Luuk</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Noor</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ilja</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dirk</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pieter</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dieuwke</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sandra</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Anne</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Arjan</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Davey</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pim</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Tom</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    student  assignment1\n",
       "0       Sem          6.7\n",
       "1     Lucas          8.2\n",
       "2      Tess          9.0\n",
       "3      Adam          5.0\n",
       "4     James          3.4\n",
       "5       Max          4.5\n",
       "6     Fleur          7.0\n",
       "7       Zoë          3.4\n",
       "8      Luuk          8.4\n",
       "9      Noor          5.7\n",
       "10     Ilja          6.3\n",
       "11     Dirk          8.4\n",
       "12   Pieter          7.1\n",
       "13  Dieuwke          7.3\n",
       "14   Sandra          5.9\n",
       "15     Anne          6.1\n",
       "16    Arjan          6.4\n",
       "17    Davey          6.1\n",
       "18      Pim          8.0\n",
       "19      Tom          7.3"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select student name and assignment1 grade\n",
    "student_grades[['student', 'assignment1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>student</th>\n",
       "      <th>assignment1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tess</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dirk</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Luuk</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student  assignment1\n",
       "2     Tess          9.0\n",
       "11    Dirk          8.4\n",
       "8     Luuk          8.4"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_grades[['student', 'assignment1']].sort_values(by='assignment1', ascending=False)[:3]\n",
    "# why do we need to include the \"by-parameter\" in this case?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Working with Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>dates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-02-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-02-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dates\n",
       "0  2018-02-05\n",
       "1  2018-02-07\n",
       "2  2018-02-19\n",
       "3  2018-02-21\n",
       "4  2018-02-26"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how do I only select the dates in February\n",
    "lab_session_dates = pd.read_csv(\"http://bit.ly/dae_lab_session_dates\")\n",
    "lab_session_dates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert to a date/time datatype\n",
    "lab_session_dates['dates'] = pd.to_datetime(lab_session_dates['dates'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "1     2\n",
       "2     2\n",
       "3     2\n",
       "4     2\n",
       "5     2\n",
       "6     3\n",
       "7     3\n",
       "8     3\n",
       "9     3\n",
       "10    3\n",
       "11    3\n",
       "12    3\n",
       "13    3\n",
       "Name: dates, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract month \n",
    "lab_session_dates['dates'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filter = lab_session_dates['dates'].dt.month == 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>dates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-02-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-02-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-02-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dates\n",
       "0 2018-02-05\n",
       "1 2018-02-07\n",
       "2 2018-02-19\n",
       "3 2018-02-21\n",
       "4 2018-02-26\n",
       "5 2018-02-28"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use filter to select dates in February\n",
    "lab_session_dates.loc[lab_session_dates['dates'].dt.month == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a new column \"on_monday\" that indicates True if the specific date\n",
    "# was on Monday, otherwise it should be False\n",
    "# you may need to look up pandas documentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Monday\n",
       "1     Wednesday\n",
       "2        Monday\n",
       "3     Wednesday\n",
       "4        Monday\n",
       "5     Wednesday\n",
       "6        Monday\n",
       "7     Wednesday\n",
       "8        Monday\n",
       "9     Wednesday\n",
       "10       Monday\n",
       "11    Wednesday\n",
       "12       Monday\n",
       "13    Wednesday\n",
       "Name: dates, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lab_session_dates['dates'].dt.weekday_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1     False\n",
       "2      True\n",
       "3     False\n",
       "4      True\n",
       "5     False\n",
       "6      True\n",
       "7     False\n",
       "8      True\n",
       "9     False\n",
       "10     True\n",
       "11    False\n",
       "12     True\n",
       "13    False\n",
       "Name: dates, dtype: bool"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create filter\n",
    "lab_session_dates['dates'].dt.weekday_name == \"Monday\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# rows of which the date is equal to Monday get assigned True in a new column called 'on_monday'\n",
    "lab_session_dates.loc[lab_session_dates['dates'].dt.weekday_name == \"Monday\", 'on_monday'] = True\n",
    "# rows of which the date is not equal to Monday (e.g. Wednesday but also Sunday etc.) get assigned False in a new column called 'on_monday'\n",
    "lab_session_dates.loc[lab_session_dates['dates'].dt.weekday_name != \"Monday\", 'on_monday'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>dates</th>\n",
       "      <th>on_monday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-02-07</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-02-26</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dates  on_monday\n",
       "0 2018-02-05       True\n",
       "1 2018-02-07      False\n",
       "2 2018-02-19       True\n",
       "3 2018-02-21      False\n",
       "4 2018-02-26       True"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verify result\n",
    "lab_session_dates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where can I find these Jupyter Notebooks? \n",
    "https://github.com/RoyKlaasseBos/Data-Analytics-for-Engineers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What am I supposed to do now? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1. Theory Exercises VIS (Homework)\n",
    "# 2. Programming Exercises VIS (Homework)\n",
    "# 3. Lab Exercises VIS \n",
    "# 4. Graded Assignment 1 Module 2 (and 1..)\n",
    "\n",
    "# or have a look at the YouTube videos posted on AllAnswers"
   ]
  }
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