Lesson02/Exercise20.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating  a box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpg_df = sns.load_dataset(\"mpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e45d2a9320>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot: mpg(mileage) vs model_year\n",
    "sns.boxplot(x='model_year', y='mpg', data=mpg_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "      <th>model_decade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford mustang gl</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>europe</td>\n",
       "      <td>vw pickup</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>dodge rampage</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford ranger</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>chevy s-10</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "393  27.0          4         140.0        86.0    2790          15.6   \n",
       "394  44.0          4          97.0        52.0    2130          24.6   \n",
       "395  32.0          4         135.0        84.0    2295          11.6   \n",
       "396  28.0          4         120.0        79.0    2625          18.6   \n",
       "397  31.0          4         119.0        82.0    2720          19.4   \n",
       "\n",
       "     model_year  origin             name  model_decade  \n",
       "393          82     usa  ford mustang gl            80  \n",
       "394          82  europe        vw pickup            80  \n",
       "395          82     usa    dodge rampage            80  \n",
       "396          82     usa      ford ranger            80  \n",
       "397          82     usa       chevy s-10            80  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a new feature 'model_decade'\n",
    "import numpy as np\n",
    "mpg_df['model_decade'] = np.floor(mpg_df.model_year/10)*10\n",
    "mpg_df['model_decade'] = mpg_df['model_decade'].astype(int)\n",
    "mpg_df.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e45e9faa58>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a boxplot with multiple classes\n",
    "sns.boxplot(x='model_decade', y='mpg', data=mpg_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e45ea844e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vABYAlJSUaLUv6bGGDx+e8jorKxOzqI/4u1Qn6aNDiRcgHo9TUVFBPB5n3rx5odxDEkJN/GaWSyLpP976lK+7b2t3/iEg86ckFAlRGN0hrXWWlZWlvO4w1NbWsmTJEtydJUuWEIvF1OoPUWhP25iZAb8CNrafr9/MjmlX7AISE7+JSITF43FaWhJDgffu3Us8Hk9zRD1bmC3+04BvAG+Y2frg2C3AJWY2jkRXTxVwZYgxZLXN9alfiGVbQ+KzvrBvasfb79mrKZml65YtW0Zzc2Ke/+bmZpYuXarunhCFOapnFYkHvfanMftJCKsf9dNgrHRe8YiU1ptfWUm9luCSLjrjjDP4/e9/37Y/ZcqUNEbT83XLqB7pvLCGwYXV9zt37lzWbVmX0jpFJBxZMqOWiPRkr7zyyj77K1euTFMk0aDELyJpd/bZZ9O7d6IDonfv3syYMSPNEfVsSvwiknaxWIxevRLpKCcnh1gsluaIejYlfhFJu4KCAmbOnImZMXPmTI3hD5m+3BWRjBCLxaiqqlJrvxso8UvqhLECV+sI0dQ+zgA70ZSBGaagoIAHHngg3WFEghK/pERYzx20ztE+YkhqnztgSHgxi2Q6JX5JiWx77kAkyvTlrohIxCjxi4hEjBK/iEjEKPGLiESMEr+ISMREYlRPWVkZmzZtSnm9rUMNwxjRMnz4cC1ULSKhiETi37RpE+veeIuWvgNTWq99mlgKeM27f01pvYkFrUVEwhGJxA/Q0ncgjSeem+4wkpL3lpYhFpHwhLnm7nFmttzMNprZm2b23eD4QDNbZmaVwfuXwopBREQ+L8wvd5uBG9z9BGAycI2ZnQj8AHjJ3UcALwX7IiLSTUJL/O6+1d3XBtsfAxtJTIs1C4gHxeLA7LBiEBGRz+uWPn4zKwbGA68Bhe6+FRIfDmZ21AGumQPMARg6dGh3hCkiGa6pqYnq6moaGxvTHUpGycvLo6ioiNzc3KTKh574zaw/sAi43t3/ZmZJXefuC4AFACUlJR5ehCKSLaqrqzn88MMpLi4m2VzS07k7dXV1VFdXM2zYsKSuCfUBLjPLJZH0H3f3p4PD28zsmOD8McD2MGMQkZ6jsbGRQYMGKem3Y2YMGjSoU38FhTmqx4BfARvd/d52p34LtC6xEwOeCysGEel5lPQ/r7P/JmF29ZwGfAN4w8zWB8duAe4GfmNm3wI2AxeHGIOIiOwnzFE9q9zd3H2su48LXovdvc7dz3T3EcG7HlMVkYz39a9/nZ07dx60zG233caLL77YTRF1XWSe3BUR6Qp3x91ZvHjxF5a98847uyGiQ6fZOUUk8u69915Gjx7N6NGjuf/++6mqquKEE07g6quvZsKECXzwwQcUFxdTW1sLwF133cWoUaM4++yzueSSS7jnnnsAuOKKK1i4cCEAxcXF3H777UyYMIExY8bwl7/8JW0/3/6U+EUk0tasWcMjjzzCa6+9xquvvspDDz3ERx99xNtvv83ll1/OunXrOP7449vKl5eXs2jRItatW8fTTz9NeXn5AesuKChg7dq1XHXVVW0fDplAiV9EIm3VqlVccMEF9OvXj/79+3PhhRfyyiuvcPzxxzN58uQOy8+aNYv8/HwOP/xwzjvvvAPWfeGFFwJw8sknU1VVFdaP0GmR6OOvrq6mV8OurJn1sldDHdXVzUmX78x6A51dQ0DrAkhP597x86H9+vXrVPmO9OnTB4CcnByam5P/fzpsavFHTH5+Pvn5+ekOQyRjTJkyhWeffZaGhgY++eQTnnnmGc4444wDlj/99NP53e9+R2NjI/X19bzwwgvdGG1qRKLFX1RUxLY9vbNqPv6ioqOTLq8WeTToL7twTJgwgSuuuIJJkyYB8O1vf5svfenAs8VPnDiR888/n5NOOonjjz+ekpISjjjiiO4KNyUikfhFokZ/1XXOvHnzmDdv3j7HNmzYsM9++z76G2+8kdLSUhoaGpgyZQo33HADAI8++miH5UtKSlixYkWqw+4yJX6RLBHVFnkmmjNnDm+99RaNjY3EYjEmTJiQ7pA6RYlfRKSTfv3rX6c7hEOiL3dFRCJGiV9EJGKU+EVEIkZ9/NLtNCxRJL2U+CWjaViiHMw119/IttrUzexeWDCQn92fOXPqhEWJX7qdWuSSKttqd/DeMVNTV+HWFamrK4Mp8YuIdEJVVRXnnntu2wNe99xzD/X19QwcOJAHH3yQ3r17c+KJJ/Lkk0+yevVqrr/+enbv3k1+fj6PPPIIX/7yl9P8E4SY+M3sYeBcYLu7jw6OlQLfAWqCYre4+xevbiAikuHuvvtu3nvvPfr06dO2UteoUaNYuXIlvXv35sUXX+SWW25h0aJFaY403Bb/o8BPgX/f7/h97t7zO9FEJFLGjh3LpZdeyuzZs5k9ezYAu3btIhaLUVlZiZnR1NSU5igTwlxzdyWg9XRFpEfp3bs3LS0tbfuNjY0AvPDCC1xzzTWsWbOGk08+mebmZn74wx8ybdo0NmzY0DajZyZIxzj+a82swsweNrMDToFnZnPMrNzMymtqag5UTESkWxUWFrJ9+3bq6urYs2cPzz//PC0tLXzwwQdMmzaNH//4x+zcuZP6+np27drFkCFDgH0ncEu37v5y9xfAXYAH7z8B/qGjgu6+AFgAUFJSkvzKByISGYUFA1M6EqewYOAXlsnNzeW2227jlFNOYdiwYYwaNYq9e/dy2WWXsWvXLtyd733vexx55JF8//vfJxaLce+99zJ9+vSUxXmoujXxu/u21m0zewjotiWxejXsSPkKXNb4NwA8b0BK6+3VsANIfj5+kahK15j7uXPnJjUs+dRTT+Wdd95p27/rrrvCDCtp3Zr4zewYd98a7F4AbDhY+VQZPnx4KPVWVn4MwIi/S3WSPjq0mEVEwhzO+QQwFSgws2rgdmCqmY0j0dVTBVwZ1v3bC+uBodZ6y8rKQqlfRCQMoSV+d7+kg8O/Cut+IiKSHM3OKSISMUr8IiIRo8QvIhIxmqRNRLLWzd+7hl11f01ZfUcMOpof3fezLyz3la98hT/+8Y8pu293U+IXkay1q+6v/GD4O19cMEl3J7c+UFYnfVBXj4hIp/Xv35/6+nrOPPNMJkyYwJgxY3juueeAxLTNo0aNIhaLMXbsWC666CIaGhoAuPPOO5k4cSKjR49mzpw5uCcmJZg6dSo33XQTkyZNYuTIkbzyyiuhxq/ELyLSBXl5eTzzzDOsXbuW5cuXc8MNN7Ql8rfffps5c+ZQUVHBgAED+PnPfw7Atddey+uvv86GDRvYvXs3zz//2WwCzc3NrF69mvvvv5877rgj1NiV+EVEusDdueWWWxg7dixnnXUWW7ZsYdu2xKw0xx13HKeddhoAl112GatWrQJg+fLlnHLKKYwZM4aXX36ZN998s62+Cy+8EICTTz6ZqqqqUGNXH7+ISBc8/vjj1NTUsGbNGnJzcykuLm6bdtnM9ilrZjQ2NnL11VdTXl7OcccdR2lp6T7TNPfp0weAnJwcmpubQ41dLX4RkS7YtWsXRx11FLm5uSxfvpz333+/7dzmzZv505/+BMATTzzB6aef3pbkCwoKqK+vZ+HChWmJG9TiF5EsdsSgo5MeiZNsfckwMy699FLOO+88SkpKGDduHKNGjWo7f8IJJxCPx7nyyisZMWIEV111FX379uU73/kOY8aMobi4mIkTJ6Yu8E5S4heRrJXMmPtUq6urY+DAgRQUFLS16turqqqiV69ePPjgg587N3/+fObPn/+54ytWrGjbLigoCL2PX109IiJJ+vDDDzn11FO58cYb0x3KIVGLX0QkSccee+w+C6t0pLi4mA0bumWpkS5Ti19EJGKU+EVEIkaJX0QkYkJL/Gb2sJltN7MN7Y4NNLNlZlYZvH8prPuLiEjHwvxy91Hgp8C/tzv2A+Ald7/bzH4Q7N8UYgwi0oNde8O1bKvblrL6CgcV8tOf/DRl9WWqMNfcXWlmxfsdnkViAXaAOLACJX4R6aJtddv48OQPU1fhmtRVtb/m5mZ6986MgZTd3cdf6O5bAYL3o7r5/iIih+Sxxx5j0qRJjBs3jiuvvJK9e/fSv3//tvMLFy7kiiuuAOCKK65g3rx5TJs2jZtuuokdO3Ywe/Zsxo4dy+TJk6moqACgtLSUb3zjG0yfPp0RI0bw0EMPtdX3r//6r0ycOJGxY8dy++23p+RnyIyPnw6Y2RxgDsDQoUPTHI2ICGzcuJGnnnqKP/zhD+Tm5nL11Vfz+OOPH/Sad955hxdffJGcnByuu+46xo8fz7PPPsvLL7/M5Zdfzvr16wGoqKjg1Vdf5ZNPPmH8+PGcc845bNiwgcrKSlavXo27c/7557Ny5UqmTJlySD9Hdyf+bWZ2jLtvNbNjgO0HKujuC4AFACUlJd5dAYqIHMhLL73EmjVr2ubZ2b17N0cddfCOi4svvpicnBwAVq1axaJFiwCYPn06dXV17Nq1C4BZs2aRn59Pfn4+06ZNY/Xq1axatYqlS5cyfvx4AOrr66msrMy6xP9bIAbcHbw/1833FxHpMncnFovxox/9aJ/jP/nJT9q220+1DNCvX799rt9f6xTOHU3l7O7cfPPNXHnllYcce3thDud8AvgT8GUzqzazb5FI+GebWSVwdrAvIpIVzjzzTBYuXMj27YnOih07dvD+++9TWFjIxo0baWlp4Zlnnjng9VOmTGnrGlqxYgUFBQUMGDAAgOeee47Gxkbq6upYsWIFEydO5Ktf/SoPP/ww9fX1AGzZsqXt3ocizFE9lxzg1Jlh3TMVysrK2LQpuXleKysrAZg7d25S5YcPH550WRH5YoWDClM6EqdwUOFBz5944onMnz+fGTNm0NLSQm5uLj/72c+4++67OffccznuuOMYPXp0W6LeX2lpKd/85jcZO3Ysffv2JR6Pt52bNGkS55xzDps3b+aHP/whxx57LMceeywbN27k1FNPBRJr/T722GNf2L30RayjPz0yTUlJiZeXl3fLvTqT+KurqwEoKipKqrwSv8ih2bhxIyeccEK6w0i50tJS+vfvf0izfnb0b2Nma9y9ZP+yGTuqJ12UmEWkp1PiFxFJs9LS0m69nyZpE5Gskg3d092ts/8mSvwikjXy8vKoq6tT8m/H3amrqyMvLy/pa9TVIyJZo6ioiOrqampqatIdSkbJy8tLepAJKPGLSBbJzc1l2LBh6Q4j66mrR0QkYpT4RUQiRolfRCRisuLJXTOrAd5Pdxw9SAFQm+4gRDqg383UOt7dB+9/MCsSv6SWmZV39Bi3SLrpd7N7qKtHRCRilPhFRCJGiT+aFqQ7AJED0O9mN1Afv4hIxKjFLyISMUr8IiIRo8Tfg5nZl81sfbvX38zsejMbaGbLzKwyeP9SumOV6DGz75nZm2a2wcyeMLM8MxtmZq8Fv5tPmdlh6Y6zJ1Iff0SYWQ6wBTgFuAbY4e53m9kPgC+5+01pDVAixcyGAKuAE919t5n9BlgMfB142t2fNLMHgT+7+y/SGWtPpBZ/dJwJvOvu7wOzgNZVnuPA7LRFJVHWG8g3s95AX2ArMB1YGJzX72ZIlPij4++BJ4LtQnffChC8H5W2qCSS3H0LcA+wmUTC3wWsAXa6e3NQrBoYkp4IezYl/ggI+knPB/4j3bGIAATfK80ChgHHAv2AmR0UVV90CJT4o2EmsNbdtwX728zsGIDgfXvaIpOoOgt4z91r3L0JeBr4CnBk0PUDUAR8mK4AezIl/mi4hM+6eQB+C8SC7RjwXLdHJFG3GZhsZn3NzEh8B/UWsBy4KCij382QaFRPD2dmfYEPgP/m7ruCY4OA3wBDSfwPeLG770hflBJFZnYH8D+BZmAd8G0SffpPAgODY5e5+560BdlDKfGLiESMunpERCJGiV9EJGKU+EVEIkaJX0QkYpT4RUQiRolfRCRilPgl0sysyswKDrVMu7KlZnZjaqLrsP4rzOynYdUv0aDELyISMUr8knXMrNjM/mJmvwwW8XjczM4ysz8EC3hMChabedbMKszsVTMbG1w7yMyWmtk6M/s3wNrVe5mZrQ4Wrfm3YA2DZOL532b2tpm9CHy53fG/M7P/NLM1ZvaKmY0Kjhea2TNm9ufg9ZXg+PA0gC0AAAKJSURBVLNB2TfNbE67er5pZu+Y2f8DTmt3fLCZLTKz14PXaYgkw9310iurXkAxicf8x5BovKwBHiaRxGcBzwIPALcH5acD64PtMuC2YPscErM/FgAnAL8DcoNzPwcuD7argIIDxHIy8AaJ+eQHAJuAG4NzLwEjgu1TgJeD7aeA64PtHOCIYHtg8J4PbAAGAceQmFZjMHAY8Afgp0G5XwOnB9tDgY3p/m+jV3a8WmfBE8k277n7GwBm9ibwkru7mb1B4oPheOB/ALj7y0FL/whgCnBhcPwFM/soqO9MEkn89cScYeST3KylZwDPuHtDEMtvg/f+JGab/I+gPoA+wft04PIghr0k5qIHmGtmFwTbxwEjgKOBFe5eE9T7FDAyKHMWcGK7+geY2eHu/nEScUuEKfFLtmo/cVdLu/0WEr/XzZ+74rO53TuaoMqAuLvf3IVYOqqvF4lFRcYlU4GZTSWRyE919wYzWwHkHaT+1nuc6u67OxeuRJ36+KWnWglcCm1Jtdbd/7bf8ZlA60LzLwEXmdlRwbmBZnZ8kve5wMzyzexw4DyA4F7vmdnFQX1mZie1u9dVwfEcMxsAHAF8FCT9UcDkoOxrwNTgL5Zc4OJ2914KXNu6Y2ZJfciIKPFLT1UKlJhZBXA3n60/cAcwxczWAjNI9J/j7m8BtwJLg2uWkehfPyh3X0uiz349sAh4pd3pS4FvmdmfgTdJfP8A8F1gWtAttQb478B/Ar2De98FvBrUvzX4Wf4EvAisbVf/3Naf0czeAv4xmX8YEU3LLCISMWrxi4hEjL7cFUlCsGrZSx2cOtPd67o7HpFDoa4eEZGIUVePiEjEKPGLiESMEr+ISMQo8YuIRMz/B2Dz2+sa47vOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot: mpg (mileage) vs model_decade\n",
    "\n",
    "# parameter hue is used to group by a specific feature, in this case 'origin'\n",
    "sns.boxplot(x='model_decade', y='mpg', data=mpg_df, hue='origin')\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}