categorical_variables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88b2eb48-b741-4c35-961e-5a99a0677de3",
   "metadata": {},
   "source": [
    "Categorical Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01546f54-a765-4130-aa37-22cf33b1b4fa",
   "metadata": {},
   "source": [
    "Count Plots and Bar Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7657e2f-a76d-4e20-914e-d6e61b4925ef",
   "metadata": {},
   "source": [
    "1. catplot() for Categorical Plots: comparisons between groups - count plots and bar plots - kind = \"count\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3022da95-847e-4c81-899b-662ab396187d",
   "metadata": {},
   "source": [
    "2. catplot() for Bar Plots: displays the mean of quantitative variable per category - kind = \"bar\". The confidence interval is automatically set as 95% but can be changed via \"ci\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e905dfce-62fa-4919-8c5a-3f1b172e9376",
   "metadata": {},
   "source": [
    "EXERCISES"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8503fdbe-68c3-4e38-b96c-7f0e50e3c075",
   "metadata": {},
   "source": [
    "1. Count plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36045fcf-9789-4e5c-8644-a81b0a064e4e",
   "metadata": {},
   "source": [
    "We might suspect that young people spend a lot of time on the internet, but how much do they report using the internet each day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3935636b-6fca-490b-9089-7a319b26d209",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2abf5aa-2a91-41d1-8e9f-0e5a017840bd",
   "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>Unnamed: 0</th>\n",
       "      <th>Music</th>\n",
       "      <th>Techno</th>\n",
       "      <th>Movies</th>\n",
       "      <th>History</th>\n",
       "      <th>Mathematics</th>\n",
       "      <th>Pets</th>\n",
       "      <th>Spiders</th>\n",
       "      <th>Loneliness</th>\n",
       "      <th>Parents' advice</th>\n",
       "      <th>Internet usage</th>\n",
       "      <th>Finances</th>\n",
       "      <th>Age</th>\n",
       "      <th>Siblings</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Village - town</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>few hours a day</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "      <td>village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>few hours a day</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>female</td>\n",
       "      <td>city</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>few hours a day</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>female</td>\n",
       "      <td>city</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>most of the day</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "      <td>city</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>few hours a day</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "      <td>village</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Music  Techno  Movies  History  Mathematics  Pets  Spiders  \\\n",
       "0           0    5.0     1.0     5.0      1.0          3.0   4.0      1.0   \n",
       "1           1    4.0     1.0     5.0      1.0          5.0   5.0      1.0   \n",
       "2           2    5.0     1.0     5.0      1.0          5.0   5.0      1.0   \n",
       "3           3    5.0     2.0     5.0      4.0          4.0   1.0      5.0   \n",
       "4           4    5.0     2.0     5.0      3.0          2.0   1.0      1.0   \n",
       "\n",
       "   Loneliness  Parents' advice   Internet usage  Finances   Age  Siblings  \\\n",
       "0         3.0              4.0  few hours a day       3.0  20.0       1.0   \n",
       "1         2.0              2.0  few hours a day       3.0  19.0       2.0   \n",
       "2         5.0              3.0  few hours a day       2.0  20.0       2.0   \n",
       "3         5.0              2.0  most of the day       2.0  22.0       1.0   \n",
       "4         3.0              3.0  few hours a day       4.0  20.0       1.0   \n",
       "\n",
       "   Gender Village - town  \n",
       "0  female        village  \n",
       "1  female           city  \n",
       "2  female           city  \n",
       "3  female           city  \n",
       "4  female        village  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_data = pd.read_csv('young-people-survey-responses.csv')\n",
    "survey_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f4b5d058-ba1f-4bd8-8acc-7b9ce19190c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DWJI6MYAlqRMDWJI6MYAlqRO/BSFued1P9i5hTj3mNet6lyAB9oAlqRsDWJI6MYAlqRMDWJI6MYAlqZOJBXCSdyS5M8lVI237JrkoyQ3tcp+R285Msj7J9UmOn1RdkjRfTLIHfC6wZFrbGcCaqloErGnXSXI4sAw4oi1zdpIFE6xNkrqbWABX1SeAr0xrXgqsbNMrgRNG2s+vqnuq6iZgPXDUpGqTpPlgrs8BH1BVtwO0y/1b+0HArSPzbWht95LklCRrk6zduHHjRIuVpEmaLx/CZYa2mmnGqjqnqhZX1eKFCxdOuCxJmpy5DuA7khwI0C7vbO0bgENG5jsYuG2Oa5OkOTXXAbwaWN6mlwMXjLQvS7JXksOARcBlc1ybJM2piQ3Gk+TdwDHAfkk2AP8LeAOwKsnJwC3AiQBVdXWSVcA1wCbg1KraPKnaJGk+mFgAV9WLZrnpuFnmXwGsmFQ9kjTfzJcP4STpAccAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6mTeBXCSJUmuT7I+yRm965GkSZlXAZxkAfBXwM8AhwMvSnJ436okaTL27F3ANEcB66vqRoAk5wNLgWu6ViVph3z8Wc/uXcKcevYnPr5Ty6Wq7udSdl6SXwKWVNWvtuu/AvxUVf3GyDynAKe0q48Drp/zQrdvP+Cu3kXsAtxP43NfjWe+7qe7qmrJ9Mb51gPODG1bvUJU1TnAOXNTzs5JsraqFveuY75zP43PfTWeXW0/zatzwMAG4JCR6wcDt3WqRZImar4F8OeARUkOS/JgYBmwunNNkjQR8+oURFVtSvIbwIXAAuAdVXV157J2xrw+RTKPuJ/G574azy61n+bVh3CS9EAy305BSNIDhgEsSZ3ssgGc5FVJrk3yrvtpfYcmuer+WNdcSfKtTvd7aJL/uhPLvTHJ1UneOK39mCRPH7l+bvtO+Djr3OY+SPLSJG/ZiVp3uqa51OsY2J6deT4lOWH0l69JXpfkufd/dbPe/2lJfmgHl/nBcZHkkiQ79BW4XTaAgV8Hfraqfrl3IduSZF590Hk/ORTY4QAGXgE8uapOn9Z+DPD0e8/e1THMv5rmRBsSoIcTGIYgAKCqXlNV/zyH938asEMBfJ9V1S73B/wN8F1gHfCbwMOAdzB8je0LwNI23/8FntCmvwC8pk2/HvjVaes8FLgWeCtwNfBR4KHttiOBS4ErgQ8A+7T2S4DFbXo/4OY2/VLgvcCHgI8BBwKfAK4ArgKeOcM2vabVfxXDJ7mZYZ7DgM+0+V4PfKu1PxxYA1ze9snSke18LXAd8DbgTmAt8FzgU8ANwFFt3n2BD7ZtvHRkvz271X1F24ePaLd/vbX95rQaA7yxbcc64IWtfTWwuS3zwmn7/cvAl9ptzwTOBf4S+DRwI/BLI/Of3rb/yrZt35rhtluBu4GPA+8Ebga+CPxb2+YvtLbr23q+Cixs69ij3XbHODVtY98fyizH07T99Xzgs62mfwYOaO1nMRzTl7T7e9Usz4VvASva9l06svyPtLqubJePae3nTtufU8fQMcDFwN8D18xwP3/NcOxcDbx2llp+HviPtu/uAr4JPLTti8uBbwP/3h6XfYDzRvbX04F72rJXAD82Wmt7TP6Q4fhfCzyZ4dtS/wr8t9mOj1nqvNe2AK9iS6ZcPO7zc1qNl9DyYOws6x2mO/vXHpD92vQfAi9u048C/h9DKJ8BnArs3XbehW2ei4HHTVvfocAm4Mh2fdXIOq8Ent2mXwf8xfQdzr0DeAOwb7v+28DvtekFwCNm2J59R6bPA54/wzyrgZe06VPZ8uTZE9h7pI71DEF4aDtgNgFPaAfrFQxP7DCMs/HBttybgf/Vpp8DXNGmPwQc3aYf3u7rGODDszwuvwhc1LbzAOAW4MDRJ/sMy5wF/M7I9XMZXsD2YOgRrW/tz6Md/O22DwPfmXbbge0+P9q24zrg6jbPPsAjGV5svgz8aWv/I+C0kfW8fwdq2ta+n/F4mrbt+7DlyfyrIzWdxRD2e7X13g08aIbli3asAP8b+P2Rx215m375yON8LrMH8LeBw2Z5jKaO5QUMx/0TZpjn2qltZngR/jrwYoZe5TqGF/NFDD+u+ot2faquRzIE9knT9vloAP/3Nv3nDM/JRwALgTu3cXw8a9xtYSRTxn1+ch8DeFc+BTHqecAZSa5g2AkPAR4DfBJ4FvAM4B+Bh7dzPIdW1UxjSNxUVVe06c8DhyZ5JPCoqpoabWNlW+f2XFRVX2nTnwNeluQs4Cer6pszzH9sks8mWccQHEfMMM/RwLvb9Hkj7QH+MMmVDL2ogxh6Qjcz9O5uAx7N0Mu6ElhTwxGzjiEoYNhH5wFU1ceAH27b/ingz5K8qu2HTdvZ7mcA766qzVV1B0Nv56nbWWYmH6yq71fVNQxBDsPj/Ly2HZcDj2fLabSp2z7N8ELxowzvGN4LHJLkj4EXtOsfZ3ihfmGSX2Dopb6kreflDL3mcWuacd+32+51PM2wzoOBC9vjfjpbP+7/WFX3VNVdDO9eDphh+e8yBM30+3gaQ28Whsf1GbNs06jLquqmWW47KcnlDPv+CEZOFQC0Y2VvtmzzeQy93UOBH2Z4PN7CsP/3YQjGjwOPTbI/8CKGdyjf30Z9Uz/KWgd8tqq+WVUbgf9I8ihmPj4W7ei2zGKc5+cO210COMAvVtWR7e8xVXUtQ/AtZngL+QmGHf5rDAfqTO4Zmd7M9n+osokt+/Ah02779tREVX2CIbS/BJyX5CWjMyZ5CHA2wyvpTzIEwvT1/WB1M7T9MkNP4ClVdSTD27ip5d/D0AN5GUPP9/ts2c7vs2UbZxyHo6rewNAzeyhwaZLHz1LXDzZnO7ePa/SxyMjlH408zo9leAx+cBvDKakPVdVjq+rtwEaGfbCO4RTCxraPnw98heG849uAO5I8B/gp4J92oKZt7ftxjqc3A29pNb2CrR/3cZb/Xnsx3dY8sOW4+cExmyTAg0fm+fb0hdp8hwG/AxxXVU9g6MxMPz7T7mO05mr1/Hqr7YkMz8cHjcxzHsM+fBnDu4dtGT1uR+9n6ji+1/HRjoEd3RamLbMjz88dsrsE8IXAK9sBRZInAVTVdxnOB57EcH7skww7/5Pjrriqvg58NckzW9OvMPSgYHjL8pQ2Pesn5El+hOFt0luBtzOcvxo19WDeleTh21jXpxh+ng3DQTvlkW3930tyLMP5vykXMrxVe2qbns0nptaZ5BiG0Zu+keTHqmpdVf0xw3mzxzO8VXzENtbzwiQLkixkeOG5bBv3y3bWN+pC4OVtH5HkILYE4YUMvdd1wDFJjkjy6LZNm6rq7xjexj+6Lf9yhgA+jeEt89uAvwNWVdXmHahpW/t+HI9keGEGWL6Dy27Lp9n6WPmXNn0zW47ZpWwdhrPZmyGcv57kAIbxurdSVV9j2GdTH2KNHp8PYThXfTTD82cBW55D5zI8BjA8V8fZ57O51/HRetfjbstsj/m4z88dtrt8Qv96hnNKV7YQvpnhAwEYwva4qvpOkk8yvOUbO4Cb5cDftNMXNzK8WgP8CbCqDZv5sW0sfwxwepLvMRyIW/WAq+prSd7KEB43M/TcZ/Jq4O+TvJrhPOWUdwEfSrKW4RzvdSO3fY/hgFtVVZvba9RMzgLe2d5Kf4ctYXBaC5bNDOMy/xNDj2NTki8C51bVn4+s5wMMb3+/yNAD+t2q+vJsd9p8CHhfkqXAK2ebqao+muTHgc+07fjWDLd9kOEJvpbhdMttwIlJnta2YRHDqYIfYXiyfZyh17ya4dTD1OmHsWpi2/t+HGcB703yJYZOwmE7uPxsXgW8I8npDO8Cpo7ZtwIXJLmM4cO5GXu9o6rqi0m+wPCh1Y0MHYGZnM6wLZ9h6xf7s4GfY/hQ/N8ZHofXtXXfkeRahsdtHfDWdrprh0NuluPjxQynb8bZlnOAf0pye1UdO7LMuM/PHeZPkXdzSfZgOB92YlXd0Lue+ap9f/PPq+qZ251Z95vWqVnH8PXEr/euZ67tLqcgNIP2pfb1DB+6Gb6zyPC/B98PnNm7lgeS9iOL64A3PxDDF+wBS1I39oAlqRMDWJI6MYAlqRMDWN2NM6LXzoxUtZO1HJnkZyd9PxIYwNp1nMYOjlS1k6N6HQkYwJoTBrDmjTYG7yVJ3pfkuiTvyuBVDGNZXJzk4jbv85J8JsnlSd478uunm5O8Jsm/MPwA4+Ykr23zrZv6KXWShyV5R5LPJflCkqUZ/hHs6xh+yXdFkhdOq2+rsYWTfLjVvCDDuLBXtfv4zXb7r7X1fzHJ+6d68El+LMml7bbXjb4DSHJ6a78yyWsnusPVnQGs+eZJDL3dwxkGcDm6qv6S4Rdtx1bVsUn2A34feG5VPZnhV2+/NbKO/6iqZ1TV+e36XW2+v2b4KTrA7wEfq6qnAscyjN71IIZhB9/TxhJ4z5g1HwkcVFU/0cYKmPo13T9U1VOr6okMI4Wd3NrfBLyp3fdtUytJ8jyGX+od1db5lCTjDPykXZQBrPnmsqraUFXfZ/hp76EzzPPTDAH9qQwj4C1n6zEYpgfnP7TL0dHCZhtBb2fcCPxokjcnWQJ8o7X/RJJPZhhB65fZMoLW0xhGBYMtI5ZN1TTOaF7aTewuY0Fo9zHOCGBhGO7zRbOsY/r4BlPrHF3f1Ah6Ww1LmuSntlHb6Oh30AZpqaqvJnkicDzDOM0nMQz2cy5wQht/4KUMY4Jsy9RoXv9nO/NpN2EPWLuK0ZGqLgWOTvJYGMYTSPKfd3B9M46gx7ZHQbsZODLJHkkOYThVQDslskdVvR/4A7aMdvcI4PYkD2Lr0cEuZRi4HraMWDZV0/ZG89JuxADWrmJqpKqL2yDcLwXe3UZvu5Th7fqOeD3DOd8rM/zzyNe39ouBw2f6EI5h5KybGAaP+ROG0wQwDMJ+STudcS5bxpT4A4Z/N3QRW4+SdhrwW21EsgMZ/nMEVfVRhlMSn2mnLd7HfRueUfOcY0FIc6x9G+Lfq6qSLANeVFVLe9eluec5YGnuPQV4Szv98TWG88V6ALIHLEmdeA5YkjoxgCWpEwNYkjoxgCWpEwNYkjr5/26AhIfUSvPhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create count plot of internet usage\n",
    "sns.catplot(x= \"Internet usage\", data=survey_data, kind=\"count\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1fdee50b-f3a5-4028-9ec6-6dfb3c181c74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the orientation of the plot\n",
    "sns.catplot(y=\"Internet usage\", data=survey_data,\n",
    "            kind=\"count\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "695b5ee3-4cac-47ae-95f5-671eaed23ae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Separate into column subplots based on gender\n",
    "sns.catplot(y=\"Internet usage\", data=survey_data,\n",
    "            kind=\"count\", col=\"Gender\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73f676fd-f417-47a4-bd01-7ea031f37e2c",
   "metadata": {},
   "source": [
    "It looks like most young people use the internet for a few hours every day, regardless of their age."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84dcc9c5-070e-4d29-a52a-724d1bd7ddeb",
   "metadata": {},
   "source": [
    "2. Customizing bar plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc4027cd-d40b-42ac-bb4e-332a0336a655",
   "metadata": {},
   "source": [
    "Do students who report higher amounts of studying tend to get better final grades? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "daf09069-a5b2-4025-9295-6b515cc553ac",
   "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>Unnamed: 0</th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>failures</th>\n",
       "      <th>...</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>location</th>\n",
       "      <th>study_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2 to 5 hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2 to 5 hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2 to 5 hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>Urban</td>\n",
       "      <td>5 to 10 hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2 to 5 hours</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 school sex  age famsize Pstatus  Medu  Fedu  traveltime  \\\n",
       "0           0     GP   F   18     GT3       A     4     4           2   \n",
       "1           1     GP   F   17     GT3       T     1     1           1   \n",
       "2           2     GP   F   15     LE3       T     1     1           1   \n",
       "3           3     GP   F   15     GT3       T     4     2           1   \n",
       "4           4     GP   F   16     GT3       T     3     3           1   \n",
       "\n",
       "   failures  ... goout Dalc Walc health absences  G1  G2  G3  location  \\\n",
       "0         0  ...     4    1    1      3        6   5   6   6     Urban   \n",
       "1         0  ...     3    1    1      3        4   5   5   6     Urban   \n",
       "2         3  ...     2    2    3      3       10   7   8  10     Urban   \n",
       "3         0  ...     2    1    1      5        2  15  14  15     Urban   \n",
       "4         0  ...     2    1    2      5        4   6  10  10     Urban   \n",
       "\n",
       "      study_time  \n",
       "0   2 to 5 hours  \n",
       "1   2 to 5 hours  \n",
       "2   2 to 5 hours  \n",
       "3  5 to 10 hours  \n",
       "4   2 to 5 hours  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_data = pd.read_csv('student-alcohol-consumption.csv')\n",
    "student_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1f121f83-b348-4315-a982-3289c49cbed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar plot of average final grade in each study category\n",
    "sns.catplot(x=\"study_time\", y=\"G3\", data=student_data, kind=\"bar\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b00082e0-3e4e-4c77-a381-a564d129e43a",
   "metadata": {},
   "source": [
    "Rearrange the bars so that they are in order from lowest study time to highest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b5d44737-f505-4715-a6d1-25f396d95e62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of categories from lowest to highest\n",
    "category_order = [\"<2 hours\", \n",
    "                  \"2 to 5 hours\", \n",
    "                  \"5 to 10 hours\", \n",
    "                  \">10 hours\"]\n",
    "\n",
    "# Rearrange the categories\n",
    "sns.catplot(x=\"study_time\", y=\"G3\",\n",
    "            data=student_data,\n",
    "            kind=\"bar\", order=category_order)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68460193-6dc9-48fb-8e23-bf24c930bd2f",
   "metadata": {},
   "source": [
    "Update the plot so that it no longer displays confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f557c9e7-c7d1-4d8e-846e-075f40b272e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of categories from lowest to highest\n",
    "category_order = [\"<2 hours\", \n",
    "                  \"2 to 5 hours\", \n",
    "                  \"5 to 10 hours\", \n",
    "                  \">10 hours\"]\n",
    "\n",
    "# Turn off the confidence intervals\n",
    "sns.catplot(x=\"study_time\", y=\"G3\",\n",
    "            data=student_data,\n",
    "            kind=\"bar\",\n",
    "            order=category_order, ci=None)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2241fa2c-fd2c-4d68-b07e-5f3be24168f6",
   "metadata": {},
   "source": [
    "Students in our sample who studied more have a slightly higher average grade, but it's not a strong relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c71a642a-7bd2-4a8c-aed0-25894d33dbad",
   "metadata": {},
   "source": [
    "Box Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea0d669-0163-4cb6-a246-63fbb3391875",
   "metadata": {},
   "source": [
    "A box plot shows the distribution of quantitative data. The color box represents the 25th to 75th percentile and the line in the middle of the box represents the median. The whiskers give a sense of the spread of the distribution and the floating points, the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b94871a0-5e98-45a0-8f7b-6b7b65949745",
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e3662e7a-09d6-4fb7-8d69-7e4ad49fbba1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x=\"time\", y=\"total_bill\", data=tips, kind=\"box\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "336b20bc-9ddd-4235-af53-49dd1c79354e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# You can use the order parameter to change the order of the categories\n",
    "g = sns.catplot(x=\"time\", y=\"total_bill\", data=tips, kind=\"box\", order=[\"Dinner\", \"Lunch\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4ceef447-99c2-4cc5-a805-afcced4f102f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the sym parameter to omit the outliers\n",
    "g = sns.catplot(x=\"time\", y=\"total_bill\", data=tips, kind=\"box\", sym=\"\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c2c99311-1e7c-4bad-ab92-12a697013b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the whis parameter to change the whiskers - to extend to 2.0 * IQR (interquartile range)\n",
    "g = sns.catplot(x=\"time\", y=\"total_bill\", data=tips, kind=\"box\", sym=\"\", whis=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c72f2a1a-755e-41e9-9df5-367dcb0dfd41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the whis parameter to change the whiskers - to show 5th to 95th percentiles\n",
    "g = sns.catplot(x=\"time\", y=\"total_bill\", data=tips, kind=\"box\", whis=[5,95])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cb87f1a1-0a48-44f1-a2dd-97425a5e922d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the whis parameter to change the whiskers - to show min and max values.\n",
    "# There are no outliers because the whiskers cover the entire data.\n",
    "g = sns.catplot(x=\"time\", y=\"total_bill\", data=tips, kind=\"box\", whis=[0,100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd12fcd-2b76-482c-b118-9cba03f6e999",
   "metadata": {},
   "source": [
    "EXERCISE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5913697-664e-475d-87e3-f879cc9782bc",
   "metadata": {},
   "source": [
    "1. Create and interpret a box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d0b0865b-00ad-4976-9375-0af7c3e13e36",
   "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>Unnamed: 0</th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>failures</th>\n",
       "      <th>...</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>location</th>\n",
       "      <th>study_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2 to 5 hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2 to 5 hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2 to 5 hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>Urban</td>\n",
       "      <td>5 to 10 hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2 to 5 hours</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 school sex  age famsize Pstatus  Medu  Fedu  traveltime  \\\n",
       "0           0     GP   F   18     GT3       A     4     4           2   \n",
       "1           1     GP   F   17     GT3       T     1     1           1   \n",
       "2           2     GP   F   15     LE3       T     1     1           1   \n",
       "3           3     GP   F   15     GT3       T     4     2           1   \n",
       "4           4     GP   F   16     GT3       T     3     3           1   \n",
       "\n",
       "   failures  ... goout Dalc Walc health absences  G1  G2  G3  location  \\\n",
       "0         0  ...     4    1    1      3        6   5   6   6     Urban   \n",
       "1         0  ...     3    1    1      3        4   5   5   6     Urban   \n",
       "2         3  ...     2    2    3      3       10   7   8  10     Urban   \n",
       "3         0  ...     2    1    1      5        2  15  14  15     Urban   \n",
       "4         0  ...     2    1    2      5        4   6  10  10     Urban   \n",
       "\n",
       "      study_time  \n",
       "0   2 to 5 hours  \n",
       "1   2 to 5 hours  \n",
       "2   2 to 5 hours  \n",
       "3  5 to 10 hours  \n",
       "4   2 to 5 hours  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_data = pd.read_csv('student-alcohol-consumption.csv')\n",
    "student_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "20d458c9-0820-4af4-aad9-6b684494ab05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Specify the category ordering\n",
    "study_time_order = [\"<2 hours\", \"2 to 5 hours\", \n",
    "                    \"5 to 10 hours\", \">10 hours\"]\n",
    "\n",
    "# Create a box plot and set the order of the categories\n",
    "sns.catplot(x=\"study_time\", y=\"G3\", data=student_data, kind=\"box\",order=study_time_order)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0374c471-2a2c-474d-b185-9fc1b94c0286",
   "metadata": {},
   "source": [
    "2. Omitting outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "32fe7841-6c6b-406d-9d3a-e97faadb44c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ4AAAFgCAYAAACL5B9mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcSElEQVR4nO3dfbRddX3n8feH8BTAKTJECgFEDZVSl0S8UpVKQZQVMlZsqyNMq/gwTXUkjdNqtQ+j1U6ts2w71ejApMqgHYvaKpW2KUIVi7SiXDA8CZRbCpiEQoCRhyZAE77zx9nXHi735vGe38m99/1a666z92//9t7fy7orH3577/PbqSokSWplj2EXIEmaWwweSVJTBo8kqSmDR5LUlMEjSWpqz2EXMJ2WLFlSl1xyybDLkKQMu4Dd2awa8dx3333DLkGStA2zKngkSbs/g0eS1JTBI0lqyuCRJDVl8EiSmjJ4JElNGTySpKYMHklSUwaPJKmpgQVPkiOSXJ7k5iQ3JVnRtR+U5LIkt3WfT59i/yVJbk0yluS9g6pTktTWIEc8m4FfqaofBV4MvCPJscB7ga9W1dHAV7v1J0kyD/gEcDpwLHBWt68kaYYb2CShVXU3cHe3/HCSm4GFwBnAyV23TwNfB94zYfcTgLGquh0gyee6/b47qHqlYVm5ciVjY2M7vN+6desAWLhw4Q7vu2jRIpYvX77D+0nTock9niRHAS8AvgUc0oXSeDg9Y5JdFgLf61tf27VNduxlSUaTjG7YsGFa65Z2Z5s2bWLTpk3DLkPaYQN/LUKSA4AvAu+sqoeS7ZotfLJONVnHqloFrAIYGRmZtI+0O9vZkceKFSsA+OhHPzqd5UgDN9ART5K96IXOZ6vqS13zPUkO7bYfCtw7ya5rgSP61g8H1g+yVklSG4N8qi3Ap4Cbq+oP+jZdDJzdLZ8NfHmS3a8Gjk7yrCR7A2d2+0mSZrhBjnhOBN4AvDzJmu5nKfBh4JVJbgNe2a2T5LAkqwGqajNwDvAV4GbgC1V10wBrlSQ1Msin2q5k6te/njpJ//XA0r711cDqwVQnSRoWZy6QJDVl8EiSmjJ4JElNGTySpKYMHklSUwaPJKkpg0eS1NTA52qTJBjOLNzgTNy7I4NH0m7NGbhnH4NHUhPOwq1x3uORJDVl8EiSmjJ4JElNGTySpKYMHklSUwaPJKkpg0eS1JTBI0lqyuCRJDVl8EiSmjJ4JElNOVfbLpgrs+3u7O8Ju/a7OquwNDsZPEMwl2bbnUu/q6TtY/Dsgrky2+6ujDpm2u8qafC8xyNJasrgkSQ1NbBLbUnOB14F3FtVz+vaPg88t+tyIPD9qlo8yb53AA8DW4DNVTUyqDolSW0N8h7PBcDHgc+MN1TV68eXk/w+8OBW9j+lqu4bWHWSpKEYWPBU1RVJjppsW5IA/xF4+aDOL0naPQ3rHs/LgHuq6rYpthdwaZJrkizb2oGSLEsymmR0w4YN016oJGl6DSt4zgIu3Mr2E6vqeOB04B1JTpqqY1WtqqqRqhpZsGDBdNcpSZpmzYMnyZ7AzwCfn6pPVa3vPu8FLgJOaFOdJGnQhjHieQVwS1WtnWxjkv2TPG18GTgNuLFhfZKkARpY8CS5EPgm8Nwka5O8tdt0JhMusyU5LMnqbvUQ4Mok1wHfBv6qqi4ZVJ2SpLYG+VTbWVO0v2mStvXA0m75duC4QdUlSRouZy6QJDVl8EiSmjJ4JElNGTySpKYMHklSUwaPJKkpg0eS1JTBI0lqyuCRJDVl8EiSmjJ4JElNGTySpKYMHklSUwaPJKkpg0eS1JTBI0lqyuCRJDVl8EiSmjJ4JElNGTySpKYMHklSUwaPJKkpg0eS1JTBI0lqyuCRJDVl8EiSmhpY8CQ5P8m9SW7sa/utJOuSrOl+lk6x75IktyYZS/LeQdUoSWpvkCOeC4Alk7T/z6pa3P2snrgxyTzgE8DpwLHAWUmOHWCdkqSGBhY8VXUF8MBO7HoCMFZVt1fV48DngDOmtThJ0tAM4x7POUmu7y7FPX2S7QuB7/Wtr+3aJpVkWZLRJKMbNmyY7lolSdOsdfCcCzwHWAzcDfz+JH0ySVtNdcCqWlVVI1U1smDBgmkpUpI0OE2Dp6ruqaotVfUE8Ef0LqtNtBY4om/9cGB9i/okSYPXNHiSHNq3+tPAjZN0uxo4OsmzkuwNnAlc3KI+SdLg7TmoAye5EDgZODjJWuD9wMlJFtO7dHYH8Itd38OAT1bV0qranOQc4CvAPOD8qrppUHVKktoaWPBU1VmTNH9qir7rgaV966uBpzxqLUma+Zy5QJLUlMEjSWrK4JEkNWXwSJKaMngkSU0ZPJKkpgweSVJTA/sejzTXrFy5krGxsWbnGz/XihUrmp0TYNGiRSxfvrzpOTW7GDzSNBkbG2PNjTezZb+Dmpxvj8d7c+dec/s9Tc4HMG/jzrzpRHoyg0eaRlv2O4hNx0z6Yt1ZYf4tTiiiXec9HklSUwaPJKkpg0eS1JTBI0lqyuCRJDVl8EiSmjJ4JElNGTySpKYMHklSUwaPJKkpg0eS1JTBI0lqyuCRJDVl8EiSmjJ4JElNGTySpKYGFjxJzk9yb5Ib+9o+kuSWJNcnuSjJgVPse0eSG5KsSTI6qBolSe0NcsRzAbBkQttlwPOq6vnAPwC/tpX9T6mqxVU1MqD6JElDMLDgqaorgAcmtF1aVZu71auAwwd1fknS7mmY93jeAvz1FNsKuDTJNUmWbe0gSZYlGU0yumHDhmkvUpI0vYYSPEl+A9gMfHaKLidW1fHA6cA7kpw01bGqalVVjVTVyIIFCwZQrSRpOjUPniRnA68Cfq6qarI+VbW++7wXuAg4oV2FkqRB2rPlyZIsAd4D/GRVbZyiz/7AHlX1cLd8GvDBhmXOWitXrmRsbKzpOcfPt2LFimbnXLRoEcuXL292Pkk7ZmDBk+RC4GTg4CRrgffTe4ptH+CyJABXVdXbkhwGfLKqlgKHABd12/cE/qSqLhlUnXPJ2NgYt930HY48YEuzc+79r71B9WN3tnkq/q5H5jU5j6SdN7DgqaqzJmn+1BR91wNLu+XbgeMGVddcd+QBW/j14x8adhkD86Fr/92wS5C0DU0vtUma+datW9f00ukwLteCl2wHyeCRtEM2bdrU9JJt68u14CXbQTN4JO0wL9lqVzhJqCSpKUc8tH/MeFjXrNetW8fBTc8oSU9l8NALgjU33syW/Q5qcr49Hu99b/aa2+9pcj6AeRsf4IB994K9mp1SkiZl8HS27HcQm45ZOuwyBmb+LavhiYeHXYYkeY9HktSWwSNJasrgkSQ1ZfBIkpoyeCRJTRk8kqSmDB5JUlN+j0eaJuvWrWPexgd735mapeZtvJ/HUn4RWbvEEY8kqSlHPNI0WbhwIf/82J6zfgaMA554GNg07FI0gznikSQ1ZfBIkpoyeCRJTRk8kqSmfLhgDnnssce489F5s/q1vnc+PI/9160bdhmStsIRjySpKUc8c8g+++zDEXtt4tePf2jYpQzMh679d+yzcOGwy5C0FY54JElNbTN4kuyRZI9uee8kxyc5aPClSdLclOSRaT7ea5Ic27f+wSSvmM5z7IitBk+S1wB3A+uSnAF8A/g94PokP7WNfc9Pcm+SG/vaDkpyWZLbus+nT7HvkiS3JhlL8t4d/aUkSU/yGuAHwVNV76uqvxlWMdsa8bwfOA54KfDHwBur6uXAid22rbkAWDKh7b3AV6vqaOCr3fqTJJkHfAI4nd5/qLP6k1qS5or0fCTJjUluSPL6vm2/2rVdl+TDXdsvJLm6a/tikv2SvBR4NfCRJGuSPCfJBUle2+1zapLvdMc6P8k+XfsdST6Q5Npu2zHT9Xtt8+GCqvrnroi7qurWru3O8ctvW9nviiRHTWg+Azi5W/408HXgPRP6nACMVdXt3Xk/1+333W3VurOcVVjafj6W39TPAIvpDQAOBq5OckXX9hrgx6tqY9/tjy9V1R8BJPnvwFuramWSi4G/rKo/67bRfe5Lb5BwalX9Q5LPAG8H/rA73n1VdXyS/wK8C/jP0/FLbdc9nm7xLX1t84C9d+J8h1TV3QDd5zMm6bMQ+F7f+tqubar6liUZTTK6YcOGnShJknZbPwFcWFVbquoe4G+BFwGvAP5PVW0EqKoHuv7PS/KNJDcAPwf82DaO/1zgn6rqH7r1TwMn9W3/Uvd5DXDUrv4y47Y14llGL2Aerapv97UfDnx4uoqYIJO01VSdq2oVsApgZGRkyn5b46zC0vbzsfymJvv3cLx9sn/vLgBeU1XXJXkT/3aFaUePP+6x7nML0/j1m22NeA4D3jq+kuRbSW4HLu8raEfck+TQ7liHAvdO0mctcETf+uHA+p04lyTNdFcAr08yL8kCeqORbwOXAm9Jsh/0Htzq+j8NuDvJXvRGPOMe7rZNdAtwVJJF3fob6I2qBmpbwfOrwMV96/vQG+adDLxtJ853MXB2t3w28OVJ+lwNHJ3kWUn2Bs6cUIMkzRUXAdcD1wFfA361qv65qi6h9+/iaJI19O6/APw34FvAZfRCZdzngHd3DxE8Z7yxqh4F3gz8aXd57gngvMH+StseOu1dVf33W66sqvuB+5Psv7Udk1xIL6AOTrKW3lNwHwa+kOStwF3A67q+hwGfrKqlVbU5yTnAV4B5wPlVddNO/G6SNCNV1QHdZwHv7n4m9vkwE255VNW5wLmT9P07+h6nBt7Ut+2rwAsm2eeovuVRtn3ZbrttK3ie9D2bqjqnb3XB1nasqrOm2HTqJH3XA0v71lcDs/cRM0maw7Z1qe1bSX5hYmOSX6R3nVGSpB2yrRHPfwX+PMl/Aq7t2l5I717PawZYlyRpltpq8FTVvcBLk7ycf3se/K+q6msDr0ySNCtt13PZXdAYNpKkXeZrESRJTRk8kjSLJTmq/y0BXdtvJXnXJH1/MHnoIPkGUklq5KRXLr2CPfY8ctoO+MTmu664bPVJ2+64bUma5YHBI02jeRsfaDbL+R6P9uZKe2LfdrNEz9v4AOzrFOc7bY89j/yX57/2mdN1uP2v/7Nd2j/J14G/p/eqm/EZYl6RZAVwCPDLVfWX3ZsG/hgYnzjgnKr6+yQnA78F3Ac8j95koj/fffF1SgaPNE0WLVq07U7TaGzs4d55n31Iw7Mewrp162Dz9xueUwN2YFX9JPQutdGbhfongecAl3fzuN0LvLKqHk1yNHAhMNLt/wJ6Tz2vB/6OXohdubUTGjzSNFm+fHnT861YsQKAj370o83P+9iddzc9p3bJVKOP8fbPT2j/QlU9AdzWTQp9DPBPwMeTLKY3U/WP9PX/dlWtBejmjTsKg0eS5rT7mTD9GXAQvTAB+JcJ2yYGVdGbTOAeei+k2wN4tG97/5sKtuv1CT7VJkmzWFU9Qu9VCafCD16hsISpRyWvS7JHN4v1s4FbgR8C7u5GQm+gN4HzTnPEI0mz3xuBTyT5/W79A1X1j+OvwJ7gVnrv5DkEeFt3X+d/AV9M8jp672ObOEraIQaPJLXyxOa7dvVJtInH255uVfVd4JRJ2k+esP6mKfa/DXh+X9Ovde1fB77e1+8ctoPBI0mNTNd3bmY67/FIkpoyeCRJTRk8kqSmDB5JUlMGjySpKYNHkmaxJFuSrElyY5K/SHLgNB77kZ3Zz8epJamRnz7tZVfsPY9pey3C41u466JLv7GtR7Q3VdVigCSfBt4B/M72HD/JnlW1edeqfCqDR5Ia2XseR37kJd+fttcivPubB+7oLt+k+yJo90qEd1XVaJKDgdGqOirJm4D/AOwL7J/k1cCX6c33thfwm1X15V2p2+CRpDkgyTzgVOBT29H9JcDzq+qB7gVxP11VD3UBdVWSi7f1zp2t8R6PJM1u87vXFdxPb1bqy7Zjn8uq6oFuOcCHklwP/A2wkN48bjvNEc8cc9cj8/jQte3eWHnPxt7/2xyy3xNNznfXI/M4usmZpBljU1UtTvJDwF/Su8fzMWAz/zb42HfCPv2TgP4csAB4YVX9a5I7Jum/Q5oHT5Ln8uQXDz0beF9V/WFfn5PpXVMcf1/El6rqg41KnLXmz5/PwsZvyXx8bAyAfZ7Z5rxH0/5NoNJMUFUPJvkl4MtJzgXuAF4IfBt47VZ2/SHg3i50TgF2+R5V8+CpqluBxfCDa47rgIsm6fqNqnpVw9JmvYULFw7lbZXQ/i2Zkp6qqr6T5DrgTOD3gC8keQPwta3s9lngL5KMAmuAW3a1jmFfajsV+MequnPIdUjSwD2+hbt24km0rR5vW32q6oAJ6z/Vt9r/qoPf7LZfAFzQ1/8+eg8bbPPY22vYwXMmcOEU217SJfN6eo/83TRZpyTLgGUARx45bY/HS9K0247v3MwJQ3uqLcnewKuBP51k87XAM6vqOGAl8OdTHaeqVlXVSFWNLFiwYCC1SpKmzzAfpz4duLaq7pm4oaoe6t4TTlWtBvbqnh+XJM1ww7zUdhZTXGZL8sPAPVVVSU6gF5D3D7KYeRsfYP4tqwd5ih/Y49GHAHhi33aPNc/b+AC7+Oi9JE2LoQRPkv2AVwK/2Nf2NoCqOo/eo31vT7IZ2AScuSvfkt2W1o/fjo093Dvvs1sGwSE+ZixptzCU4KmqjcC/n9B2Xt/yx4GPt6pn+fLlrU4F+IixpLlt2E+1SZqBWs6A0Xr2C3AGjEEzeCTtkNaXbFvPfgHOgDFoBo+kHeKlae0qZ6eWJDVl8EiSmjJ4JElNGTySpKYMHklSUwaPJKkpg0eS1JTBI0lqyuCRJDVl8EiSmjJ4JElNOVfbLli5ciVj3QSGO2J8n/E5qHbUokWLms+XJUnTxeAZgvnz5w+7BEkaGoNnFzjqkKQd5z0eSVJTBo8kqSmDR5LUlMEjSWrK4JEkNWXwSJKaMngkSU0ZPJKkpgweSVJTQwmeJHckuSHJmiSjk2xPko8lGUtyfZLjh1GnJGn6DXPKnFOq6r4ptp0OHN39/DhwbvcpSZrhdte52s4APlNVBVyV5MAkh1bV3cMubC7a2Vm4Yddm4nYWbml2GtY9ngIuTXJNkmWTbF8IfK9vfW3X9hRJliUZTTK6YcOGAZSqXTF//nxn45b0JMMa8ZxYVeuTPAO4LMktVXVF3/ZMsk9NdqCqWgWsAhgZGZm0j3aNow5J02koI56qWt993gtcBJwwocta4Ii+9cOB9W2qkyQNUvPgSbJ/kqeNLwOnATdO6HYx8Mbu6bYXAw96f0eSZodhXGo7BLgoyfj5/6SqLknyNoCqOg9YDSwFxoCNwJuHUKckaQCaB09V3Q4cN0n7eX3LBbyjZV2SpDacuUCS1JTBI0lqyuCRJDVl8EiSmjJ4JElNGTySpKYMHklSU7vr7NTSnLGzs38787dmKoNHmqGc9VszlcEjDZkjD8013uORJDVl8EiSmjJ4JElNGTySpKYMHklSUwaPJKkpg0eS1JTBI0lqyuCRJDVl8EiSmjJ4JElNGTySpKYMHklSUwaPJKkpg0eS1JTBI0lqqnnwJDkiyeVJbk5yU5KnvLc3yclJHkyypvt5X+s6JUmDMYw3kG4GfqWqrk3yNOCaJJdV1Xcn9PtGVb1qCPVJkgao+Yinqu6uqmu75YeBm4GFreuQJA3HUO/xJDkKeAHwrUk2vyTJdUn+OsmPbeUYy5KMJhndsGHDoEqVJE2ToQVPkgOALwLvrKqHJmy+FnhmVR0HrAT+fKrjVNWqqhqpqpEFCxYMrF5J0vQYSvAk2Yte6Hy2qr40cXtVPVRVj3TLq4G9khzcuExJ0gAM46m2AJ8Cbq6qP5iizw93/UhyAr06729XpSRpUIbxVNuJwBuAG5Ks6dp+HTgSoKrOA14LvD3JZmATcGZV1RBqlSRNs+bBU1VXAtlGn48DH29TkSSpJWcukCQ1ZfBIkpoyeCRJTRk8kqSmDB5JUlMGjySpKYNHktSUwSNJasrgkSQ1ZfBIkpoyeCRJTRk8kqSmDB5JUlMGjySpKYNHktSUwSNJasrgkSQ1ZfBIkpoyeCRJTRk8kqSmDB5JUlMGjySpKYNHktSUwSNJasrgkSQ1ZfBIkpoaSvAkWZLk1iRjSd47yfYk+Vi3/fokxw+jTknS9GsePEnmAZ8ATgeOBc5KcuyEbqcDR3c/y4BzmxYpSRqYPYdwzhOAsaq6HSDJ54AzgO/29TkD+ExVFXBVkgOTHFpVd7cvV9J0WLlyJWNjYzu83/g+K1as2KnzLlq0iOXLl+/UvhqMYVxqWwh8r299bde2o30ASLIsyWiS0Q0bNkxroZKGb/78+cyfP3/YZWgaDWPEk0naaif69BqrVgGrAEZGRibtI2n4HHVo3DBGPGuBI/rWDwfW70QfSdIMNIzguRo4OsmzkuwNnAlcPKHPxcAbu6fbXgw86P0dSZodml9qq6rNSc4BvgLMA86vqpuSvK3bfh6wGlgKjAEbgTe3rlOSNBjpPTg2O4yMjNTo6Oiwy5Ckye5Tq+PMBZKkpgweSVJTBo8kqSmDR5LUlMEjSWrK4JEkNWXwSJKaMngkSU3Nqi+QJtkA3DnsOnZjBwP3DbsIzXj+HW3bfVW1ZNhF7K5mVfBo65KMVtXIsOvQzObfkXaVl9okSU0ZPJKkpgyeuWXVsAvQrODfkXaJ93gkSU054pEkNWXwSJKaMngkSU0ZPJKkpgyeWSrJUUluTvJHSW5KcmmS+UkWJ7kqyfVJLkry9GHXqt1Lkt9OsqJv/XeS/FKSdye5uvvb+UC3bf8kf5XkuiQ3Jnn98CrXTGHwzG5HA5+oqh8Dvg/8LPAZ4D1V9XzgBuD9wytPu6lPAWcDJNkDOBO4h97f0wnAYuCFSU4ClgDrq+q4qnoecMlQKtaMYvDMbv9UVWu65WuA5wAHVtXfdm2fBk4aRmHafVXVHcD9SV4AnAZ8B3hR3/K1wDH0gugG4BVJ/keSl1XVg8OpWjPJnsMuQAP1WN/yFuDAIdWhmeeTwJuAHwbOB04Ffreq/vfEjkleCCwFfjfJpVX1wZaFauZxxDO3PAj8vyQv69bfAPztVvpr7rqI3mW0FwFf6X7ekuQAgCQLkzwjyWHAxqr6v8DvAccPq2DNHI545p6zgfOS7AfcDrx5yPVoN1RVjye5HPh+VW0BLk3yo8A3kwA8Avw8sAj4SJIngH8F3j6smjVzOGWOpKfoHiq4FnhdVd027Ho0u3ipTdKTJDkWGAO+auhoEBzxSJKacsQjSWrK4JEkNWXwSJKaMng0KyT5++3o887uMfJB17I4ydJBn0eaqQwezQpV9dLt6PZOYIeCJ8m8nShnMb1v8kuahMGjWSHJI93nyUm+nuTPktyS5LPp+SXgMODy7ouRJDktyTeTXJvkT/u+lX9HkvcluRJ4Xbf+ga7fDUmO6frtn+T8bsbm7yQ5I8newAeB1ydZ42zN0lMZPJqNXkBvdHMs8GzgxKr6GLAeOKWqTklyMPCbwCuq6nhgFPjlvmM8WlU/UVWf69bv6/qdC7yra/sN4GtV9SLgFOAjwF7A+4DPV9Xiqvr8IH9RaSZyyhzNRt+uqrUASdYARwFXTujzYnrB9HfdFDB7A9/s2z4xML7UfV4D/Ey3fBrw6iTjQbQvcOSuly/NbgaPZqOJs3JP9nce4LKqOmuKY/zLFMfsP16An62qW5904OTHd6xcaW7xUpvmkoeBp3XLVwEnJlkEkGS/JD+yg8f7CrA83ZCpe3/NxPNImsDg0VyyCvjrJJdX1QZ675u5MMn19ILomB083m/Tu6dzfZIbu3WAy4FjfbhAmpxztUmSmnLEI0lqyuCRJDVl8EiSmjJ4JElNGTySpKYMHklSUwaPJKmp/w/LiuxsJgrINwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 426.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box plot with subgroups and omit the outliers\n",
    "# Add subgroups so each box plot is colored based on \"location\"\n",
    "sns.catplot(x=\"internet\", y=\"G3\", data=student_data, hue=\"location\", kind=\"box\", sym=\"\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da597ebb-9725-4337-8d82-7e451003c8cf",
   "metadata": {},
   "source": [
    "The median grades are quite similar between each group, but the spread of the distribution looks larger among students who have internet access."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fc19f43-2064-4a6b-b210-afa0a4192245",
   "metadata": {},
   "source": [
    "3. Adjusting the whiskers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba58c753-36cb-4aec-a324-9abda511da71",
   "metadata": {},
   "source": [
    "We will use the database to compare the distribution of final grades (\"G3\") between students who are in a romantic relationship and those that are not. Let's create a box plot to look at this relationship and try different ways to define the whiskers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a90780ec-a077-45ca-9abf-64d3ef915b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the whiskers to 0.5 * IQR\n",
    "sns.catplot(x=\"romantic\", y=\"G3\",\n",
    "            data=student_data,\n",
    "            kind=\"box\", whis=0.5)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0cca6b8a-9061-41c3-b790-888d206e074e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extend the whiskers to the 5th and 95th percentile\n",
    "sns.catplot(x=\"romantic\", y=\"G3\",\n",
    "            data=student_data,\n",
    "            kind=\"box\",\n",
    "            whis=[5,95])\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "214e0409-aa74-4b0f-a2f3-374804a9b175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the whiskers at the min and max values\n",
    "sns.catplot(x=\"romantic\", y=\"G3\",\n",
    "            data=student_data,\n",
    "            kind=\"box\",\n",
    "            whis=[0, 100])\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73f5450f-1910-4fdf-84f7-99a282d16063",
   "metadata": {},
   "source": [
    "The median grade is the same between these two groups, but the max grade is higher among students who are not in a romantic relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f865374f-5740-4c7a-b218-2b83dcc640ba",
   "metadata": {},
   "source": [
    "Point Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5401cd6-b3b9-41bb-897e-aaf0b940d11d",
   "metadata": {},
   "source": [
    "They show the mean of a quantitative variable for the observations in each category, plotted as a single point. The vertical lines show the interval of confidence. One of the axis is a categorical variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c5bd36-a78b-4e98-aa63-4b2aa830f7cf",
   "metadata": {},
   "source": [
    "EXERCISE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdf4df6b-260b-4104-bc48-46ebb36c105f",
   "metadata": {},
   "source": [
    "1. Customizing point plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b06330e-90fe-4196-9e49-986798803432",
   "metadata": {},
   "source": [
    "Let's continue to look at data from students in secondary school, this time using a point plot to answer the question: does the quality of the student's family relationship influence the number of absences the student has in school?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3be6051d-7912-4c77-9d5e-6633cb1aaa4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a point plot of family relationship vs. absences\n",
    "sns.catplot(x=\"famrel\", y=\"absences\", data=student_data,kind=\"point\")\n",
    "            \n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6d38b8e0-a487-45e2-bf8a-1f622b58b10c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add caps to the confidence interval\n",
    "sns.catplot(x=\"famrel\", y=\"absences\",\n",
    "            data=student_data,\n",
    "            kind=\"point\", capsize=0.2)\n",
    "        \n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7825871b-18b8-4693-8fec-9343f02332d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove the lines joining the points\n",
    "sns.catplot(x=\"famrel\", y=\"absences\",\n",
    "            data=student_data,\n",
    "            kind=\"point\",\n",
    "            capsize=0.2, join=False)\n",
    "            \n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9faa6737-b461-466c-96a8-dd7b95b81e32",
   "metadata": {},
   "source": [
    "While the average number of absences is slightly smaller among students with higher-quality family relationships, the large confidence intervals tell us that we can't be sure there is an actual association here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6f2576-d167-4fa0-af93-f79766f5c2cc",
   "metadata": {},
   "source": [
    "2. Point plots with subgroups"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d945a32-8f86-4532-97aa-2ee73fd6fb0a",
   "metadata": {},
   "source": [
    "This time, we'll ask the question: is being in a romantic relationship associated with higher or lower school attendance?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "130c28d3-51b9-4ff4-9cf8-0caaebbca4ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 410.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a point plot that uses color to create subgroups\n",
    "sns.catplot(x=\"romantic\", y=\"absences\", data=student_data, kind=\"point\", hue=\"school\")\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6bcd15d1-10d1-43bc-a6b2-3aabf68e77d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 410.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Turn off the confidence intervals for this plot\n",
    "sns.catplot(x=\"romantic\", y=\"absences\",\n",
    "            data=student_data,\n",
    "            kind=\"point\",\n",
    "            hue=\"school\", ci=None)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f8bd832f-6c66-432e-a5c3-cc091d799647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 410.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import median function from numpy\n",
    "from numpy import median\n",
    "\n",
    "# Plot the median number of absences instead of the mean\n",
    "sns.catplot(x=\"romantic\", y=\"absences\",\n",
    "            data=student_data,\n",
    "            kind=\"point\",\n",
    "            hue=\"school\",\n",
    "            ci=None, estimator=median)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7c171b-2f50-4584-ab1c-363839d07ac4",
   "metadata": {},
   "source": [
    "It looks like students in romantic relationships have a higher average and median number of absences in the GP school, but this association does not hold for the MS school."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98b9673d-d68c-47b2-8283-4fbcaa9ecc0d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}