NPV - Take Home (Day 4) - Questions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"center\" width=100%>\n",
    "    <tr>\n",
    "        <td width=\"15%\">\n",
    "            <img src=\"homework.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"center\">\n",
    "                <font color=\"#21618C\" size=8px>\n",
    "                    <b> Take-Home <br>(Day 4)\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's begin with some hands-on practice exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> 1. Plot a horizontal bar chart and vertical bar chart for the following data.\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "<tr>\n",
    "    <th>Name</th>\n",
    "    <td>Sheldon</td>\n",
    "    <td>Amy</td>\n",
    "    <td>Priya</td>\n",
    "    <td>Raj</td>  \n",
    "</tr>\n",
    "<tr>\n",
    "    <th>Maths</th>\n",
    "    <td>72</td>\n",
    "    <td>34</td>\n",
    "    <td>54</td>\n",
    "    <td>76</td>  \n",
    "</tr>\n",
    "<tr>\n",
    "    <th>Geography</th>\n",
    "    <td>98</td>\n",
    "    <td>98</td>\n",
    "    <td>78</td>\n",
    "    <td>87</td>  \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# type your code here\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Name</td>\n",
       "      <td>Sheldon</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Priya</td>\n",
       "      <td>Raj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Maths</td>\n",
       "      <td>72</td>\n",
       "      <td>34</td>\n",
       "      <td>54</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Geography</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>78</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0        1    2      3    4\n",
       "0       Name  Sheldon  Amy  Priya  Raj\n",
       "1      Maths       72   34     54   76\n",
       "2  Geography       98   98     78   87"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfn=pd.read_html('''\n",
    "<table>\n",
    "<tr>\n",
    "    <th>Name</th>\n",
    "    <td>Sheldon</td>\n",
    "    <td>Amy</td>\n",
    "    <td>Priya</td>\n",
    "    <td>Raj</td>  \n",
    "</tr>\n",
    "<tr>\n",
    "    <th>Maths</th>\n",
    "    <td>72</td>\n",
    "    <td>34</td>\n",
    "    <td>54</td>\n",
    "    <td>76</td>  \n",
    "</tr>\n",
    "<tr>\n",
    "    <th>Geography</th>\n",
    "    <td>98</td>\n",
    "    <td>98</td>\n",
    "    <td>78</td>\n",
    "    <td>87</td>  \n",
    "  </tr>\n",
    "</table>''')[0]\n",
    "dfn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "Maths=[72,34,54,76]\n",
    "Geography=[98,98,78,87]\n",
    "Name=['Sheldon','Amy','Priya','Raj']\n",
    "plt.bar(Name,Maths)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.barh(Name,Maths)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.bar(Name,Geography)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAD4CAYAAAD//dEpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAO7ElEQVR4nO3df7BcZX3H8ffHBISQGoRQigG52kYshZFAVFCKoJ2OiC0y6hRKW2RsM51xFNs6nbT+YR1Hh05ta2nVKQVUpgxoEVsUR7EoFduK3vwoiSBqJSiIIirxBxgwfPvHnozbOzfJE8Puudl9v2Yyd89zfn337Ln7yfOc3XNTVUiStDtP6LsASdK+wcCQJDUxMCRJTQwMSVITA0OS1GRx3wWM0vLly2tmZqbvMiRpn7Fu3boHquqw+eZNdGDMzMwwOzvbdxmStM9IcvfO5jkkJUlqYmBIkpoYGJKkJgaGJKmJgSFJamJgSJKaGBiSpCYGhiSpyUR/cW/TvVuZWXtD32VImnBbLj6r7xLGwh6GJKmJgSFJamJgSJKaGBiSpCYGhiSpiYEhSWpiYEiSmhgYkqQmBoYkqYmBIUlqYmBIkpoYGJKkJgaGJKmJgSFJamJgSJKaGBiSpCYGhiSpyYIIjCTbk2xMsjnJh5Mc3LDOf42jNknSwIIIDODhqjqhqo4Dvgu8ZncrVNXzRl+WJGmHhRIYw/4bWAGQZGmSm5KsT7Ipydk7Fkryw94qlKQptLjvAoYlWQS8CLi8a/oxcE5VfT/JcuCzSa6vqtrFNtYAawAWPemwUZcsSVNjofQwDkyyEfgOcAjwia49wNuS3Ab8O4Oex+G72lBVXVpVq6tq9aIly0ZZsyRNlYUSGA9X1QnA0cD+/PQaxvnAYcBJ3fxvAQf0U6IkTbeFEhgAVNVW4HXAG5LsBywD7q+qR5OcwSBQJEk9WFDXMACqakOS/wHOBa4CPpxkFtgIfHF40T7qk6RptSACo6qWzpn+jaHJU+Yun+RQBh+/lSSNyYIakmqR5CkMPnr79r5rkaRpsiB6GHuiqr4BPKPvOiRp2uxzPQxJUj8MDElSEwNDktTEwJAkNTEwJElNDAxJUhMDQ5LUxMCQJDUxMCRJTQwMSVITA0OS1MTAkCQ1MTAkSU32ubvV7onjVyxj9uKz+i5DkiaCPQxJUhMDQ5LUxMCQJDUxMCRJTQwMSVITA0OS1MTAkCQ1MTAkSU0MDElSk4n+pveme7cys/aGvsuQNCZbvLPDSNnDkCQ1MTAkSU0MDElSEwNDktTEwJAkNTEwJElNDAxJUhMDQ5LUxMCQJDUxMCRJTQwMSVITA0OS1MTAkCQ1MTAkSU0MDElSEwNDktTEwJAkNRl5YCTZnmRjks1J/iXJkp0s99EkB4+6HknSz2YcPYyHq+qEqjoOeAT4w+GZGXhCVb2kqh4cQz2SpJ/BuIekbgF+KclMkjuSvAtYDxyVZEuS5UnekuSiHSskeWuS1yVZmuSmJOuTbEpy9phrl6SpNrbASLIYOBPY1DUdA1xZVauq6u6hRS8HLujWeQJwLnAV8GPgnKo6ETgD+OskmWc/a5LMJpnd/tDW0T0hSZoyi8ewjwOTbOwe38IgEJ4C3F1Vn527cFVtSfKdJKuAw4ENVfWdJPsBb0tyGvAYsKKb/805618KXArwxCNW1qielCRNm3EExsNVdcJwQ9cx+NEu1rkMeBXwC8AVXdv5wGHASVX1aJItwAGPd7GSpPkt1I/Vfgh4MfBs4ONd2zLg/i4szgCO7qs4SZpG4+hh7LGqeiTJp4AHq2p713wV8OEks8BG4Iu9FShJU2jkgVFVS+dp2wIcN6dtZsfj7mL3ycArh+Y/AJwyqjolSbu24IakkhwLfAW4qaq+3Hc9kqSBBTckVVW3A0/vuw5J0v+34HoYkqSFycCQJDUxMCRJTQwMSVITA0OS1MTAkCQ1MTAkSU0MDElSEwNDktTEwJAkNTEwJElNDAxJUhMDQ5LUZMHdrfbxdPyKZcxefFbfZUjSRLCHIUlqYmBIkpoYGJKkJgaGJKmJgSFJamJgSJKaGBiSpCYGhiSpiYEhSWoy0d/03nTvVmbW3tB3GZI0NltGeHcLexiSpCYGhiSpiYEhSWpiYEiSmhgYkqQmBoYkqYmBIUlqYmBIkpoYGJKkJgaGJKmJgSFJamJgSJKaGBiSpCYGhiSpiYEhSWpiYEiSmhgYkqQmYw2MJOckqSTPHOd+JUl7b9w9jPOAzwDnjnm/kqS9NLbASLIUeD7warrASHJ6kv9I8oEkX0pycZLzk3wuyaYkv5jk55LclWS/bp0nJdmyY1qSNB7j7GG8DPhYVX0J+G6SE7v2ZwEXAccDvws8o6qeA1wGvLaqfgDcDOz4y+bnAh+sqkfn20mSNUlmk8xuf2jr6J6NJE2ZcQbGecA13eNrummAz1fVfVW1Dfhf4MaufRMw0z2+DLiwe3wh8J6d7aSqLq2q1VW1etGSZY9j+ZI03RaPYydJDgVeCByXpIBFQAEfBbYNLfrY0PRjO+qrqv9MMpPkBcCiqto8jrolST81rh7GK4Arq+roqpqpqqOAu4BT92AbVwJXs4vehSRpdMYVGOcBH5rT9kHgt/dgG1cBT2YQGpKkMRvLkFRVnT5P2yXAJTtbrqpuZnCxe4dTgWur6sFR1ChJ2rWxBMbeSvL3wJnAS/quRZKm1T4RGFX12r5rkKRp572kJElNDAxJUhMDQ5LUxMCQJDUxMCRJTQwMSVITA0OS1MTAkCQ1MTAkSU0MDElSEwNDktTEwJAkNTEwJElN9om71f6sjl+xjNmLz+q7DEmaCPYwJElNDAxJUhMDQ5LUxMCQJDUxMCRJTQwMSVITA0OS1MTAkCQ1MTAkSU0m+pvem+7dyszaG/ouQ5LGZssI725hD0OS1MTAkCQ1MTAkSU0MDElSEwNDktTEwJAkNTEwJElNDAxJUhMDQ5LUxMCQJDUxMCRJTQwMSVITA0OS1MTAkCQ1MTAkSU0MDElSEwNDktSkKTCSvDHJF5LclmRjkucm2ZJkeeuOkpye5CM7mbdH25Ikjd9u/0RrklOAlwInVtW27o19/5FXJklaUFp6GEcAD1TVNoCqeqCqvtHNe22S9Uk2JXkmQJKDklyR5PNJNiQ5e+4Gkxya5MZu/j8CGZr3x0k2d/9e37XNJLkjyT91PZ0bkxy4t09ektSuJTBuBI5K8qUk70rygqF5D1TVicC7gTd0bW8EPllVzwbOAP4qyUFztvkm4DNVtQq4HngqQJKTgAuB5wInA3+QZFW3zkrgnVX1K8CDwMvnKzbJmiSzSWa3P7S14elJklrsNjCq6ofAScAa4NvA+5O8qpt9XfdzHTDTPf51YG2SjcDNwAF0gTDkNOCfu+3fAHyvaz8V+FBV/ajb73XAr3bz7qqqjfPsb269l1bV6qpavWjJst09PUlSo91ewwCoqu0M3vxvTrIJuKCbta37uX1oWwFeXlV3Dm8jyeFzNzvPrjJP2w7bhh5vBxySkqQx2m0PI8kxSVYONZ0A3L2LVT7O4NpGuvVXzbPMp4Hzu/lnAk8ean9ZkiXdMNY5wC27fRaSpJFruYaxFHhfktuT3AYcC/zFLpZ/C7AfcFuSzd30XG8GTkuynsEQ1tcAqmo98F7gc8CtwGVVtaHtqUiSRilV840MTYYnHrGyjrjgHX2XIUljs+Xis/Zq/STrqmr1fPP8prckqYmBIUlqYmBIkpoYGJKkJgaGJKmJgSFJamJgSJKaGBiSpCYGhiSpiYEhSWpiYEiSmhgYkqQmBoYkqYmBIUlqYmBIkpo0/YnWfdXxK5Yxu5f3hpckDdjDkCQ1MTAkSU0MDElSEwNDktTEwJAkNTEwJElNDAxJUhMDQ5LUxMCQJDVJVfVdw8gk+QFwZ9919Gw58EDfRfTMY+AxAI8BtB2Do6vqsPlmTPStQYA7q2p130X0Kcmsx8Bj4DHwGMDeHwOHpCRJTQwMSVKTSQ+MS/suYAHwGHgMwGMAHgPYy2Mw0Re9JUmPn0nvYUiSHicGhiSpyUQGRpIXJ7kzyVeSrO27nnFIclSSTyW5I8kXklzUtR+S5BNJvtz9fHLftY5akkVJNiT5SDf9tCS3dsfg/Un277vGUUpycJJrk3yxOx9OmbbzIMkfdb8Hm5NcneSAaTgPklyR5P4km4fa5n3tM3BJ9z55W5ITd7f9iQuMJIuAdwJnAscC5yU5tt+qxuInwJ9U1S8DJwOv6Z73WuCmqloJ3NRNT7qLgDuGpv8S+NvuGHwPeHUvVY3P3wEfq6pnAs9icCym5jxIsgJ4HbC6qo4DFgHnMh3nwXuBF89p29lrfyawsvu3Bnj37jY+cYEBPAf4SlV9taoeAa4Bzu65ppGrqvuqan33+AcM3iRWMHju7+sWex/wsn4qHI8kRwJnAZd10wFeCFzbLTLRxyDJk4DTgMsBquqRqnqQKTsPGHwp+cAki4ElwH1MwXlQVZ8GvjuneWev/dnAlTXwWeDgJEfsavuTGBgrgK8PTd/TtU2NJDPAKuBW4PCqug8GoQL8fH+VjcU7gD8FHuumDwUerKqfdNOTfj48Hfg28J5uWO6yJAcxRedBVd0LvB34GoOg2AqsY7rOg2E7e+33+L1yEgMj87RNzWeHkywFPgi8vqq+33c945TkpcD9VbVuuHmeRSf5fFgMnAi8u6pWAT9igoef5tON0Z8NPA14CnAQg+GXuSb5PGixx78bkxgY9wBHDU0fCXyjp1rGKsl+DMLiqqq6rmv+1o5uZvfz/r7qG4PnA7+ZZAuDocgXMuhxHNwNTcDknw/3APdU1a3d9LUMAmSazoNfA+6qqm9X1aPAdcDzmK7zYNjOXvs9fq+cxMD4PLCy+0TE/gwudl3fc00j143VXw7cUVV/MzTreuCC7vEFwL+Nu7Zxqao/q6ojq2qGwev+yao6H/gU8IpusUk/Bt8Evp7kmK7pRcDtTNF5wGAo6uQkS7rfix3HYGrOgzl29tpfD/xe92mpk4GtO4audmYiv+md5CUM/me5CLiiqt7ac0kjl+RU4BZgEz8dv/9zBtcxPgA8lcEv0iurau5FsYmT5HTgDVX10iRPZ9DjOATYAPxOVW3rs75RSnICg4v++wNfBS5k8J/DqTkPkrwZ+C0Gnx7cAPw+g/H5iT4PklwNnM7gNubfAt4E/CvzvPZdmP4Dg09VPQRcWFWzu9z+JAaGJOnxN4lDUpKkETAwJElNDAxJUhMDQ5LUxMCQJDUxMCRJTQwMSVKT/wPoPLVM1FXb5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(Name,Geography)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,2,1)\n",
    "sns.barplot(y=Name,x=Maths,orient='h')\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.barplot(y=Name,x=Maths,orient='h')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "sns.barplot(x=Name,y=Geography,orient='v')\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "sns.barplot(x=Name,y=Geography,orient='v')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> 2. Following is data on 10 people, information on the number of dependents on each of them and their income is given. Draw a dual-axis plot.\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "<tr>\n",
    "    <th>Person</th>\n",
    "    <td>No. of Dependents</td>\n",
    "    <td>Income</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <th>1</th>\n",
    "    <td>1</td>\n",
    "    <td>60000</td>  \n",
    "</tr>\n",
    "<tr>\n",
    "    <th>2</th>\n",
    "    <td>2</td>\n",
    "    <td>52000</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <th>3</th>\n",
    "    <td>0</td>\n",
    "    <td>50000</td>  \n",
    "</tr>\n",
    "<tr>\n",
    "    <th>4</th>\n",
    "    <td>3</td>\n",
    "    <td>65000</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <th>5</th>\n",
    "    <td>0</td>\n",
    "    <td>45500</td>  \n",
    "</tr>\n",
    "<tr>\n",
    "    <th>6</th>\n",
    "    <td>1</td>\n",
    "    <td>43050</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <th>7</th>\n",
    "    <td>0</td>\n",
    "    <td>54300</td>  \n",
    "</tr> \n",
    "<tr>\n",
    "    <th>8</th>\n",
    "    <td>0</td>\n",
    "    <td>38000</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <th>9</th>\n",
    "    <td>2</td>\n",
    "    <td>20000</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <th>10</th>\n",
    "    <td>1</td>\n",
    "    <td>47500</td>  \n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Person</td>\n",
       "      <td>No. of Dependents</td>\n",
       "      <td>Income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>45500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>43050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>54300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>38000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>47500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0                  1       2\n",
       "0   Person  No. of Dependents  Income\n",
       "1        1                  1   60000\n",
       "2        2                  2   52000\n",
       "3        3                  0   50000\n",
       "4        4                  3   65000\n",
       "5        5                  0   45500\n",
       "6        6                  1   43050\n",
       "7        7                  0   54300\n",
       "8        8                  0   38000\n",
       "9        9                  2   20000\n",
       "10      10                  1   47500"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type your code here\n",
    "dfp=pd.read_html('''\n",
    "<table>\n",
    "<tr>\n",
    "    <th>Person</th>\n",
    "    <td>No. of Dependents</td>\n",
    "    <td>Income</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <th>1</th>\n",
    "    <td>1</td>\n",
    "    <td>60000</td>  \n",
    "</tr>\n",
    "<tr>\n",
    "    <th>2</th>\n",
    "    <td>2</td>\n",
    "    <td>52000</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <th>3</th>\n",
    "    <td>0</td>\n",
    "    <td>50000</td>  \n",
    "</tr>\n",
    "<tr>\n",
    "    <th>4</th>\n",
    "    <td>3</td>\n",
    "    <td>65000</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <th>5</th>\n",
    "    <td>0</td>\n",
    "    <td>45500</td>  \n",
    "</tr>\n",
    "<tr>\n",
    "    <th>6</th>\n",
    "    <td>1</td>\n",
    "    <td>43050</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <th>7</th>\n",
    "    <td>0</td>\n",
    "    <td>54300</td>  \n",
    "</tr> \n",
    "<tr>\n",
    "    <th>8</th>\n",
    "    <td>0</td>\n",
    "    <td>38000</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <th>9</th>\n",
    "    <td>2</td>\n",
    "    <td>20000</td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <th>10</th>\n",
    "    <td>1</td>\n",
    "    <td>47500</td>  \n",
    "    </tr>\n",
    "</table>''')[0]\n",
    "dfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "person=[1,2,3,4,5,6,7,8,9,10]\n",
    "Number_of_dependents=[1,2,0,3,0,1,0,0,2,1]\n",
    "Income=[60000,52000,50000,65000,45500,43050,54300,38000,20000,47500]\n",
    "\n",
    "\n",
    "fig,ax1 = plt.subplots()\n",
    "ax1.plot(person,Number_of_dependents,color='blue')\n",
    "plt.xlabel('Person')\n",
    "plt.ylabel('Number os dependents on each person')\n",
    "plt.yticks(color='blue')\n",
    "plt.xticks(color='black')\n",
    "plt.title('Inceome of a person VS Number os Dependents on a person')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "ax2.plot(person,Income,color='orange')\n",
    "plt.ylabel('Income of person')\n",
    "plt.yticks(color='orange')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> 3. Use the following data to plot a pie chart and annotate the percentage sales.\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "  <tr>\n",
    "  <th>Location</th>\n",
    "  <th>Sale</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>Delhi</td>\n",
    "  <td>42500</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>Bangalore</td>\n",
    "  <td>44500</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>Chennai</td>\n",
    "  <td>48300</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>Pune</td>\n",
    "  <td>52400</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>Kolkata</td>\n",
    "  <td>61800</td>\n",
    "  </tr> \n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Location</th>\n",
       "      <th>Sale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>42500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>44500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>48300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Pune</td>\n",
       "      <td>52400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>61800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Location   Sale\n",
       "0      Delhi  42500\n",
       "1  Bangalore  44500\n",
       "2    Chennai  48300\n",
       "3       Pune  52400\n",
       "4    Kolkata  61800"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type your code here\n",
    "dfl=pd.read_html('''\n",
    "<table>\n",
    "  <tr>\n",
    "  <th>Location</th>\n",
    "  <th>Sale</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>Delhi</td>\n",
    "  <td>42500</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>Bangalore</td>\n",
    "  <td>44500</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>Chennai</td>\n",
    "  <td>48300</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>Pune</td>\n",
    "  <td>52400</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>Kolkata</td>\n",
    "  <td>61800</td>\n",
    "  </tr> \n",
    "</table>''')[0]\n",
    "dfl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44500    1\n",
       "61800    1\n",
       "42500    1\n",
       "48300    1\n",
       "52400    1\n",
       "Name: Sale, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc=dfl.Sale.value_counts()\n",
    "vc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(vc,autopct='% .1f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> 4. Write a program to plot a bar plot of seven students representing their marks in Mathematics using plotly. \n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "        marks = [76,98,78,98,89,56,69,76]\n",
    "        students = ['Sheldon','Leonard','Raj','Howard','Amy','Penny','Bernadette']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hoverlabel": {
          "namelength": 0
         },
         "hovertemplate": "x=%{x}",
         "legendgroup": "",
         "marker": {
          "color": "#636efa"
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "type": "bar",
         "x": [
          76,
          98,
          78,
          98,
          89,
          56,
          69,
          76
         ],
         "xaxis": "x",
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "marks = [76,98,78,98,89,56,69,76]\n",
    "students = ['Sheldon','Leonard','Raj','Howard','Amy','Penny','Bernadette']\n",
    "import plotly.express as px\n",
    "\n",
    "px.bar(students,marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> 5. Import the flights data set and plot a line plot for the variable 'passengers'.\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1949</td>\n",
       "      <td>January</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1949</td>\n",
       "      <td>February</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1949</td>\n",
       "      <td>March</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1949</td>\n",
       "      <td>April</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1949</td>\n",
       "      <td>May</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     month  passengers\n",
       "0  1949   January         112\n",
       "1  1949  February         118\n",
       "2  1949     March         132\n",
       "3  1949     April         129\n",
       "4  1949       May         121"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type your code here\n",
    "data=sns.load_dataset('flights')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(data['passengers'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> 6. Import the iris data set and plot a heatmap of the correlation between its numeric variables.\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type your code here\n",
    "import seaborn as sns\n",
    "df = sns.load_dataset('iris')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>sepal_length</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117570</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sepal_width</td>\n",
       "      <td>-0.117570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>-0.366126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>petal_length</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>petal_width</td>\n",
       "      <td>0.817941</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.117570      0.871754     0.817941\n",
       "sepal_width      -0.117570     1.000000     -0.428440    -0.366126\n",
       "petal_length      0.871754    -0.428440      1.000000     0.962865\n",
       "petal_width       0.817941    -0.366126      0.962865     1.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmatrix=df.corr()\n",
    "cmatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cmatrix,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> 7. Using the iris data set, plot boxplot of its numeric features.\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type your code here\n",
    "import seaborn as sns\n",
    "df = sns.load_dataset('iris')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.boxplot(data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> 8. Import the built-in 'diamonds' data set from seaborn. For the variable 'cut', plot a count plot.\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53935</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53936</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53937</td>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53938</td>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53939</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type your code here\n",
    "import seaborn as sns\n",
    "df = sns.load_dataset('diamonds')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.countplot(x='cut',data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> 9. Use the iris data set, to plot a joint plot of sepal length against sepal width.\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type your code here\n",
    "import seaborn as sns\n",
    "df = sns.load_dataset('iris')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='sepal_length',y='sepal_width',data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> 10. Jake's monthly expenditure is given below. Draw a pie chart and explode the wedge with highest cost\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <th> Item  </th>\n",
    "        <th> Expenditure </th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th> Food </th>\n",
    "        <th> 15</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th> Clothing </th>\n",
    "        <th> 10</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th> Rent </th>\n",
    "        <th> 40 </th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th> Education </th>\n",
    "        <th> 25</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th> Unforseen Events </th>\n",
    "        <th> 5</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th> Medicine </th>\n",
    "        <th>5</th>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Item</th>\n",
       "      <th>Expenditure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Food</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Clothing</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Rent</th>\n",
       "      <th>40</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Education</th>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Unforseen Events</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Medicine</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [(Item, Food, Clothing, Rent, Education, Unforseen Events, Medicine), (Expenditure, 15, 10, 40, 25, 5, 5)]\n",
       "Index: []"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# type your code here\n",
    "dfm=pd.read_html('''\n",
    "<table>\n",
    "    <tr>\n",
    "        <th> Item  </th>\n",
    "        <th> Expenditure </th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th> Food </th>\n",
    "        <th> 15</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th> Clothing </th>\n",
    "        <th> 10</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th> Rent </th>\n",
    "        <th> 40 </th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th> Education </th>\n",
    "        <th> 25</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th> Unforseen Events </th>\n",
    "        <th> 5</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <th> Medicine </th>\n",
    "        <th>5</th>\n",
    "    </tr>\n",
    "</table>''')[0]\n",
    "dfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i=['Food','Clothing','Rent','Education','Unforseen Events','Medicine']\n",
    "v=[15,10,40,25,5,5]\n",
    "plt.pie(v,labels=i,autopct='% .1f',explode=[0,0,0.1,0,0,0])\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}