Lesson02/Exercise15.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating contour 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": [],
   "source": [
    "# contour plot\n",
    "sns.set_style(\"white\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123b8be3470>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4r/hZ7ZNMvIOJYjL7+SIXZYlU+l56+sle5qmq0ld131yRMYTez2qfJGLgByBsoQ94uxpXdeiLrNUXfZ+svfUZ/JRKGPghEabQT2a1Lzv0ZVX7YQp+Iq8Y+AER6cX6DX1ZSzbN3mv2vmS1edyEPuC+2vcb/KJX7HoNflb75BUDP0CqQ9/s/Wah76avH9bglxH6dsfxE/zx8Rj8FDYM/ICFIfQtj2VT7Ycx+GXure8n+GW0e+LjeAl/Bj+JYuAnQbJC30+1Hz+Gk/j7gwp/p1somj4uOfj7j5n8dg+Dn5ww8JNEReh7+TLX8ng+2jyDj+FmEvAyUfi5YbrX4A9zu4ehT1aELrwiNUQ2WnNzcVb/40Mv0ALuXHoYD+zBF2kBQ3vddhdrxbkJGFWVv9NFSnYXdlldUDXw5zI7tt3FVQP/G9ht0GY2rptxLN/jcO6kJ1b4Sea30vfa4rEa2021P/A4YQgVp4nHacM2u8pbZdXPVg8FhRV+CJhV3ENeY7PZmshWDFbHcFvtA9a9cZGfQzWRDcic9uVvN/ksBh/faow7fpOy2KSt/7jWWzeYjWs2htttG7g5G7HCDxGRK3Jl9PXdVPt+Kv5kBovIpBOv+P1W/SqWdopU/az4yS1W+CETRF/f6hhWFbpTxQ84V/1mx1XNTUU78PzNJjOn6tuu8hep+v30+f1W/ACrfl0w8EPIb+gDJuFsEfrA0L1krMa3Ojbg3O6JHzcuyPB3G2yywt/NF72i7R6R4Dc7vhOGvx4Y+CHlJ/QB674+MHQnSDfVfvzY/cexDn5APPytxlEhyPAXqfpVbdHsZ3tmhn/qYuCHmIrQ73/cXbUPuA9+QKzqHzyO1VgqqAp/N1W/3+C3Gm/w8a3GcMLwTy0M/JBTGfqAebUPqAl+IPXC32vVH3Sf32kMEYP/W3ACiB7pgd/T04N169bh8uXL6O7uxooVKzB27FhUV1cjFoth3Lhx2LhxI9LSuEBIlGjoA+Z7u9u2YEyqfbvjiQS/1VhA9MJ/8DkM5rXq99LukbWsc+AYZuOI4gQQPdID/5133sG3vvUtvPzyy/jiiy8wZ84cTJgwAZWVlSgtLcWGDRtw8OBBPProo7KHTmkioQ/IrfbjxwOsgx8IPvyTtdpn4DmYsQv/INs9TuMNGV9C+ANs/0SB9MCfNWsWKioqEn9OT09Ha2srSkpKAADl5eU4fPgwA98DlaHf/5z74I+fF2AdxE4tH8Bbvz8Za8ndhr9V8APu2z1+gt9qzMGGTOwSqn+Gf3hI76tkZWUhOzsbHR0dWLVqFSorK2EYBmKxWOL569evyx5WG6J/eez+olpdUHX7eZuLqmwu/nK64Co+ru3YLjZaC8PFXX62c3C7eZvfrZoHjun2Dl0y9u3nxV7Jp6SR/tlnn2HJkiWYPXs2nnjiiTv69Z2dncjNzVUxrDZkhD5gH/zxq3Stwj8e/EGEv5OwBH9Qu3aK7t3jJvyDmgAY/sklvaVz7do1LF26FBs2bMC0adMAABMnTkRLSwtKS0vR3NyMqVOnyh5WO27aO4B5KybOrs3T/7x1q0dkDNF+v9/lnVHYy8dpdY/XC7kA5107Afvf3ty2f0TGt30v9/YJXMwwDEPmAevq6vDuu++isLAw8dj69etRV1eHnp4eFBYWoq6uDunp6bbHeXz2HPz2T2/KPLWU5CbcRCoykUrPqXIUGcuxFSLQKnE8hyRXkSJ3CbN8zmlfJYF9l5zYhb+Xc3I7vun7GP6+VS5bhH379pk+Jz3wZWHgi3MbbKK/ioch/Bn8/oIfEA9fNxOA6vBn8HtnF/i88CoFiLZ3Eq+3WcUzkNDqmgEh4dTyAdyv8nFa3ulmD59kBb/IzVkA+Zu2JV4juPLGa/tH5Q1cGPxyMfBThNtQE+ntx4msqe9/nXj42wU/4H0Pn7AGv1PoA/Z35QKc19aLLoWUPQGo3OaBwS8XAz/FeKn244IMfxlVv9fN25K5a6dI6AP2bR6RgHVzFazMCUDVTVwY/HIw8FOQ29BPvM9F1Q8EE/524ZwK7R4rIsEPuAhYk59PZgvIa/gz+IPFwE9RfoLMbdUPuA9/Py2fVNqr34lo8AMerqoV/C1AZALwu9WDl+Bn6LvHwE9xfitYVeEvo+pPtb367bgJ/sR7HL4EH8zLBCD7Ri5ugp/VvnsMfE3IaF0kO/xVtHsA8Qu6BnL7OcoKJafdOR3f72ISEJkAVN3Ipa2ji20eBRj4mpHVsx78l1HWF74qNnCTGf6Dx0sms3OVMQl4WQXkdT9/q6uLBx6TbR55GPiakt2z9rrax0vV7/VLXqFxPYR/mNids+hkIDIB+Llv7+DgZ5snOAx8Smr4y6r6zcby0/IBTLY2juAEMJDX3wi87OUPWLdl2ju7XFf7dsczfS2rfVMMfLqD7C8sZYa/114/IN7ysRobsLgSVqNJQOZe/n6qffb2vWPgky2ZE4CX8A+y6h88tt34iddahWOEJwKRL4adbt1oFvyyqn2u3feOgU+uyJoARMM/iKofEAt/q3MwfZ/dlbIRmgycloPabensptpX3eKxOifdMPDJFxkTgOgVvqqqfkD85zALINFJIHGMCP5WILLBm1m1L9rbtwt9s2PbHcuO7qHPwCep/HwBHHTVbzeOm4nM628BQ44TgS+J7TZ4M6vIZYS+1bEHHgtgi0cEA5+UkRH+sqr+/tfYh7/deH4mALvzsxPWJaJhDH2r49nRsdpn4FMgvIa/rKq//zX+9+5PvM5lK8vvJCB6m8ewchOuDH11GPgUOK9X+8qq+vtf4y78ncb18l2Gm9VAifeEtOofyCmUB3K7esfPMS1fr1HoM/ApacJQ9fe/zjn8B4/rNLbf3wBEJoBkVv1ON2sxIytYRSYUhr45Bj6FQlBVPyAe/v2vTf4E4OZagKDC38uePVa8VPkMfW8Y+BQqqqt+wGWYhmACCFv4O4W9mxuch02qhz4Dn0JLddUPuG+lBDEBqAh/wP8EILOql0FFlZ/qGPgUen6DH5C7f/+dr5c/AXi9CMzrNhAy2QVwVCrnVK7yGfgUGcm8bSOgdgIIuvqXzU8bR3UFzir/NgY+RY7f7Zy9hD/gfwJIZvXfP778SUAk6FO1Wo4iBj5Fmsx79gJqJwDV1T/g/grgwdzeB9eJU9iz8g4WA59SgqybuIRxAlB9Adid5+S/Ghet6J3C3uuFV2SNgU8pR+YdvJI1Aaha/pl4j+T2jpu2jUhVz7BXg4FPKU3WTdsTx5M0Aahc/ilyXsnoq4u2b0TDXvS3EbaNbmPgkxZk37oxcVyPE4Df9k//e9xNAoC7CUoGt2ErO+zpTgx80lLUJ4D+97ibBACxAHY7KfitoN22b9yEvZdzS+VVRQx8IoR/AgDUTQKDBdEC8dKjd1vVs5UzFAOfyETYJgBA7iRw+/0Bbbbm40tYL+0br2GfytU9oDDwT548iW3btqGpqQnnz59HdXU1YrEYxo0bh40bNyItLU3V0ETShXECACTcWCXEq2G89ukZ9taUBP7vf/97vPPOOxgxYgQAoL6+HpWVlSgtLcWGDRtw8OBBPProoyqGJgpEUBMAIGcSAILfbsEtv1/EsoXjTEng5+fno7GxEb/4xS8AAK2trSgpKQEAlJeX4/Dhwwx8SimqJgBAziQAOAdqkBOCzFU2foNeh8o+TkngV1RU4NKlS4k/G4aBWCwGAMjKysL169dVDEsUGionAEDNssuoLXWUUdHrFPZAQF/aDuzXd3Z2Ijc3N4hhiULDLFhkTwJAeNbfqyC7ZaNb2AMBBf7EiRPR0tKC0tJSNDc3Y+rUqUEMSxRqQU0CgH1YhnUyUNWT1zHo4wIJ/KqqKtTW1uLVV19FYWEhKioqghiWKHKCnAQSYyq4GEv2+FLG0Tjo45QF/pgxY7Bnzx4AQEFBAXbu3KlqKKKUZhdUqieDxDlEeAUMg/42XnhFFGFB7YYZNQx5cwx8ohQUht8KgsaQd8bAJ9JMqvxWwIB3j4FPRACcAzRZEwKDXR4GPhEJ8RK8VpMEQzw5GPhEpAyDPVy4ZSURkSYY+EREmmDgExFpgoFPRKQJBj4RkSYY+EREmmDgExFpgoFPRKQJBj4RkSYY+EREmmDgExFpgoFPRKQJBj4RkSYY+EREmmDgExFpgoFPRKQJBj4RkSYY+EREmmDgExFpgoFPRKQJBj4RkSYY+EREmmDgExFpgoFPRKSJjGSfgJXP/+8KKpctSvZpEBFFyuXLly2fixmGYQR4LkRElCRs6RARaYKBT0SkCQY+EZEmGPhERJpg4BMRaYKBT0SkidCuwx/s5MmT2LZtG5qamnD+/HlUV1cjFoth3Lhx2LhxI9LS0vDrX/8a//znP5GRkYF169Zh8uTJlq+Nmp6eHqxbtw6XL19Gd3c3VqxYgbFjx2r1OfT29uKFF17A2bNnkZ6ejvr6ehiGodVnAABtbW2YO3cu/vjHPyIjI0O7nx8AnnrqKeTk5AAAxowZgwULFmDLli1IT09HWVkZVq5cib6+PmzatAn//e9/kZmZibq6OjzwwAM4ceLEkNdqw4iA7du3G48//rgxf/58wzAM46c//anxwQcfGIZhGLW1tcbf/vY349SpU8bixYuNvr4+4/Lly8bcuXMtXxtFe/fuNerq6gzDMIz29nZjxowZ2n0Of//7343q6mrDMAzjgw8+MJ577jntPoPu7m7jZz/7mTFz5kzj008/1e7nNwzD+Prrr43Zs2ff8diTTz5pnD9/3ujr6zOWL19unDp1yjhw4IBRVVVlGIZhHD9+3HjuuecsX6uLSEzv+fn5aGxsTPy5tbUVJSUlAIDy8nIcOXIEx44dQ1lZGWKxGO677z709vaivb3d9LVRNGvWLPz85z9P/Dk9PV27z+GRRx7B5s2bAQBXrlzBqFGjtPsMGhoasHDhQtxzzz0A9Py7cPr0ady8eRNLly7FkiVL8OGHH6K7uxv5+fmIxWIoKyvD0aNHcezYMUyfPh0A8N3vfhenTp1CR0eH6Wt1EYnAr6ioQEbG7e6TYRiIxWIAgKysLFy/fh0dHR3Izs5OvCb+uNlroygrKwvZ2dno6OjAqlWrUFlZqeXnkJGRgaqqKmzevBkVFRVafQb79u1DXl5eIsQAPf8uDB8+HMuWLcMf/vAH/PKXv0RNTQ1GjBiReN7qc0hPT7f8bHQRicAfbGDfsbOzE7m5ucjOzkZnZ+cdj+fk5Ji+Nqo+++wzLFmyBLNnz8YTTzyh7efQ0NCAAwcOoLa2Fl1dXYnHU/0zePvtt3HkyBEsXrwYH3/8MaqqqtDe3p54PtV//riCggI8+eSTiMViKCgoQE5ODr788svE81afQ19fn+lnE9XPwYtIBv7EiRPR0tICAGhubkZxcTEeeughHDp0CH19fbhy5Qr6+vqQl5dn+toounbtGpYuXYrnn38e8+bNA6Df57B//368/vrrAIARI0YgFoth0qRJ2nwGb7zxBnbu3ImmpiZ8+9vfRkNDA8rLy7X5+eP27t2LF198EQBw9epV3Lx5EyNHjsSFCxdgGAYOHTqU+Byam5sBACdOnMD48eORnZ2Nb3zjG0Neq4vIbJ526dIlrFmzBnv27MHZs2dRW1uLnp4eFBYWoq6uDunp6WhsbERzczP6+vpQU1OD4uJiy9dGTV1dHd59910UFhYmHlu/fj3q6uq0+Rxu3LiBmpoaXLt2Dbdu3cIzzzyDoqIi7f5fAIDFixdj06ZNSEtL0+7n7+7uRk1NDa5cuYJYLIa1a9ciLS0NW7duRW9vL8rKyrB69erEKp1PPvkEhmFg69atKCoqwokTJ4a8VheRCXwiIvInki0dIiJyj4FPRKQJBj4RkSYY+EREmmDgExFpgoFPJGj79u346KOPLJ9fvHgxzpw5M+TxnTt3qjwtImEMfCJBzz77LCZPnuz6fa+99pqCsyFyj4FP2pozZw7a2trQ09ODhx56CP/5z38Sj//5z3/GggULsHDhQuzYsQMAUF1djebmZnz99ddYtWoVFi5ciNWrV6OsrCxxzN/85jdYsmQJ5s+fj4sXL+K1117DV199hU2bNiXjRyS6AwOftPXwww/jX//6F44dO4YxY8bg8OHD+PTTT5Gfn4/33nsPu3btwq5du/D++25x4j8AAAF/SURBVO/jf//7X+J9f/nLXzBmzBi8+eabWLlyJdra2hLPzZgxAzt27EB5eTnee+89rFixAt/85jcZ+BQKkbkBCpFsM2fOxO9+9zuMHj0aq1evRlNTEwzDQEVFBRoaGvCTn/wEAPDVV1/hwoULifedOXMG5eXlAICioiLk5eUlnps0aRIAYNSoUbh27VpwPwyRAFb4pK3x48fj0qVL+OijjzBjxgzcuHEDBw8eRGFhIcaOHYsdO3agqakJc+fOxfjx4+943/HjxwEAFy5cwBdffGE7DncvobBg4JPWpkyZgry8PKSlpSX+fcKECZg2bRoWLVqEuXPn4ty5c7j33nsT75k3bx4uX76Mp59+Go2NjRg2bJjtGEVFRVi7dq3qH4XIETdPI3Lp3//+N27cuIGysjKcO3cOy5cvx/vvv5/s0yJyxMAncunzzz/HmjVr0NPTg1u3bmHVqlWJnj5RmDHwiYg0wR4+EZEmGPhERJpg4BMRaYKBT0SkCQY+EZEm/h8aWo3e/DQrtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate KDE plot: first two parameters are arrays of X and Y coordinates of data points\n",
    "# parameter shade is set to True so that the contours are filled with a color gradient based on number of data points\n",
    "sns.kdeplot(mpg_df.weight, mpg_df.mpg, shade=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}