03_boxplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fcfdea04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class', ylabel='fare'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import library\n",
    "import seaborn\n",
    "seaborn.set(style=\"whitegrid\")\n",
    "\n",
    "kashti = seaborn.load_dataset(\"titanic\")\n",
    "\n",
    "seaborn.boxplot(x=\"class\",y=\"fare\",data=kashti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a1039ce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='tip'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEJCAYAAACdePCvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXA0lEQVR4nO3dbXBU5cHG8WvzioQouwoFn/JQoFSsWDpSoOmAQBI0vEmbAF36iDPOAIVS7dhaG6gjtaIEp5XxdagM0w8MdgsIQhlhDCwwpjA4QgcmxIolQEDQoLu8BsNu9jwfUlbBBEL25d7N/f99IQnZ3YvD2b3OvXvOfbscx3EEALBOhukAAAAzKAAAsBQFAACWogAAwFIUAABYKst0gLaKRCK6cOGCsrOz5XK5TMcBgLTgOI5CoZDy8vKUkXHlMX/aFMCFCxd08OBB0zEAIC195zvfUX5+/hU/S5sCyM7OltT8j8jJyTGcBgDSw6VLl3Tw4MHoa+hXpU0BXH7bJycnR7m5uYbTAEB6aemtcz4EBgBLUQAAYCkKAAAsldACOH/+vCZMmKDjx49Lknbu3KmJEyfqvvvu05IlSxL50ACuEggEVF5ermAwaDoKUkTCCmDfvn2aNm2ajhw5Ikn64osvNH/+fL322mt6++23VV1drR07diTq4QFcxefzqaamRj6fz3QUpIiEFcCqVau0YMECde/eXZK0f/9+9e7dW7169VJWVpYmTpyozZs3J+rhAXxFIBDQ1q1b5TiOtmzZwigAkhJYAM8++6x+8IMfRL+vr69Xt27dot93795dn376aaIeHsBX+Hw+RSIRSc1X1TMKgJTE6wAikcgV56E6jtOuKR2qq6vjGQuwgt/vVzgcliSFw2Ft3bpVQ4cONZwKpiWtAHr06KFTp05Fvz916lT07aEbMXDgQC4EA25QYWGhKisrFQ6HlZWVpaKiIg0ePNh0LCRBY2NjqwfOSTsNdNCgQTp8+LCOHj2qpqYmbdy4Uffee2+yHh6wmtfrjU4ElpGRIa/XazgRUkHSCiA3N1cVFRV65JFHNG7cOPXt21clJSXJenjAah6PR0VFRXK5XCouLpbb7TYdCSkg4W8B+f3+6NcFBQXasGFDoh8SQAu8Xq/q6uo4+kdU2kwGByA2Ho9HFRUVpmMghTAVBABYigIAAEtRAABgKQoAACxFAQCApSgAALAUBQAAlqIAAMBSFAAAWIoCAABLUQAAYCkKAAAsRQEAlggEAiovL2c9YERRAIAlfD6fampqWA8YURQAYIFAIKCtW7fKcRxt2bKFUQAkUQCAFXw+nyKRiCQpEokwCoAkCgCwwvbt2xUOhyVJ4XBY27ZtM5wIqYACACwwatQoZWU1LwCYlZWl0aNHG06EVEABABbwer3KyGh+umdkZLAuMCRRAIAVPB6PioqK5HK5VFxcLLfbbToSUgCLwgOW8Hq9qqur4+gfURQAYAmPx6OKigrTMZBCeAsIACxFAQCApSgAALAUBQAAlqIAAMBSFAAAWIoCAABLUQAAYCkKAAAsRQEAgKUoAACwFAUAAJYyUgDr16/X+PHjNX78eC1evNhEBACwXtIL4OLFi3r22We1YsUKrV+/Xu+//7527tyZ7BgAYL2kF0BTU5MikYguXryocDiscDis3NzcZMcAAOslfT2ALl266Fe/+pXGjh2rm266SUOGDNE999yT7BgAYL2kF8C///1vvfnmm9q2bZvy8/P1+OOPa/ny5ZoxY0abbl9dXZ3ghABgh6QXQFVVlQoKCnTrrbdKkkpLS/XGG2+0uQAGDhzIW0YA0EaNjY2tHjgn/TOAAQMGaOfOnWpoaJDjOPL7/br77ruTHQMArJf0EcDw4cNVU1Oj0tJSZWdn6+6779asWbOSHQMArGdkUfhZs2bxog8AhnElMGCJQCCg8vJyBYNB01GQIigAwBI+n081NTXy+XymoyBFUACABQKBgLZu3SrHcbRlyxZGAZBEAQBW8Pl8ikQikqRIJMIoAJIoAMAK27dvVzgcliSFw2Ft27bNcCKkAgoAsMCoUaOUldV80l9WVpZGjx5tOBFSAQUAWMDr9Sojo/npnpGRIa/XazgRUgEFAFjA4/GoqKhILpdLxcXFcrvdpiMhBRi5EAxA8nm9XtXV1XH0jygKALCEx+NRRUWF6RhIIbwFBACWogAAwFIUAABYigIAAEtRAABgKQoAACxFAQCApSgAALAUBQAAlqIA4oxl95Cq2DdxNQogzlh2D6mKfRNXowDiiGX3kKrYN9ESCiCOWHYPqYp9Ey2hAOKIZfeQqtg30RIKII5Ydg+patSoUcrMzJQkZWZmsm9CEgUQVyy7h1Tl9XrlOI4kyXEc9k1IogDiimX3AKQTVgSLM5bdQyry+XzKyMhQJBJRRkaGfD6f5syZYzqWMX6/X5WVlTHdx+nTpyVJXbt2jel+xowZo8LCwpjuo70YAcTZ5WX3OPpHKuFD4PgLBAIKBAKmY8SEEQBggVGjRqmyslLhcJgTFCQVFhbGfNQ9b948SdKiRYviEckIRgCABThBAS2hAAALcIICWsJbQIAlOEEBV6MAAEtcPkEBuIy3gADAUkYKwO/3q7S0VGPHjtXChQtNRAAA6yW9AI4dO6YFCxbotdde04YNG1RTU6MdO3YkOwZgHRaEwdWSXgCVlZUaN26cevTooezsbC1ZskSDBg1KdgzAOiwIg6slvQCOHj2qpqYmzZ49W5MmTdIbb7yhW265JdkxEoajLKQiFoRBS5J+FlBTU5Pef/99rVixQp07d9acOXO0bt06lZaWtun21dXVCU4Ym40bN6qmpkYvvfSSJkyYYDoOIKl5v2xqapLU/Bxk/4zduXPnJEl79uwxnKT9kl4At912mwoKCuTxeCRJxcXF2r9/f5sLYODAgcrNzU1kxHYLBALav3+/HMfR/v379eijj3LBDVLC4sWLryiAAwcOaMGCBYZTpbc1a9ZIkgYPHmw4ybU1Nja2euCc9LeARo8eraqqKp09e1ZNTU169913dddddyU7RkKw7B5SFYsVoSVJL4BBgwZpxowZ+tnPfqZx48bp9ttvV1lZWbJjJAQzLiJVMRcQWmLkSuDJkydr8uTJJh46oZhxEanq8lxAmzdvZi4gRHElcBxxlIVU5vV69d3vfpf9ElFtLoAzZ87o/PnzicyS9phxEamMxYpwtesWQG1trcrKylRQUKBhw4bpwQcf1IkTJ5KRLS1xlAUgXVy3AObNm6cpU6Zo3759+te//qX7779fv//975ORLS1xlAUgXVy3AC5evCiv16vs7Gzl5ORo+vTp+uyzz5KRDQCQQNctgL59+2rv3r3R7w8ePKhvfvObCQ0FAEi8654GeuLECU2fPl133HGHsrKyVFNTo27dumnixImSpH/84x8JDwkAiL/rFsDjjz+ejBwAgCRrtQAOHTqkfv36KS8vr8W/7yjTNwCArVotgOeff15/+ctfNGXKFPXs2VOO40T/7uLFi9q1a1dSAgIAEqPVAli8eLFOnz6tfv36acWKFXIcRy6XS6FQSA8++GAyMwIAEqDVAvjNb36jf/7zn3K5XCooKIj+PDMzU/fff39SwgEAEqfVAli+fLmk5gvBFi1alLRAJvn9flVWVsZ0H6dPn5Ykde3aNab7GTNmjAoLC2O6D3Qsse6f7Ju42nXPArLlxT9eAoGApNifZEC8sW/iakamg05VhYWFMR/ZzJs3TxLFifiLdf9k38TVmA4aACxFAQCApSgAALAUBQAAlqIAAMBSFAAAWIrTQJEwqXJhHRcuAS2jAJDSuHgJSBwKAAnDhXVAauMzAACwFAUAAJaiAADAUhQAAFiKAgAAS1EAAGApCgAALEUBAIClKAAAsBQFAACWogAAwFJGC2Dx4sUqLy83GQEArGWsAHbt2qV169aZengAsJ6RAjh9+rSWLFmi2bNnm3h4AIAMTQf91FNP6bHHHtPJkydNPDyANLZs2TLV1taajhHNcHnKclP69u2rmTNntuu2SS+A1atXq2fPniooKNDatWtv+PbV1dUJSBU/586dkyTt2bPHcJKOge0ZPx1lW+7bt0+fHD2q7nIZzdFJjiTpTPUBYxnq5ejcuXPt/j9NegG8/fbbOnXqlCZNmqQzZ86ooaFBzz33nObPn9+m2w8cOFC5ubkJTtl+a9askSQNHjzYcJKOge0ZPx1lW65Zs0YRufR/Gdmmoxi3MhJSfn7+Nf9PGxsbWz1wTnoB/PWvf41+vXbtWr333nttfvEHAMQP1wEAgKWMrglcWlqq0tJSkxEAwFqMAADAUhQAAFiKAgAAS1EAAGApCgAALEUBAIClKAAAsJTR6wCQuphw60qxTLglpcb2TJVtKcW+PREfHaYAUuEJJqXOkyzWJ1htba0OfVCt/+lsdsKtLpHmCbe+OGpuwq2PG5yY76O2tlbVHx5Qhtvc/DVOZpMkqab+oLEMkhQJhow+Pr7UYQqgtrZW1TUHpVyP2SDh5id49aHPzGVoDMTlbv6ns0uP3MWEWy8fiM8LVoY7W7n3dY/LfaWzxnfqTUfAf3WYApDU/OLfa4LpFOYd22g6AYA0wIfAAGCpjjUCANDhBYNBfSZHKyN8llAvR5FgsN23ZwQAAJZiBAAgrbjdbmV8fIIVwdS8Itgtbne7b08BoEXBYFCfNzhxOwMmnR1vcHRrDMNsIFXxFhAAWIoRAFrkdrt109kTXAeg5usAOsUwzAZSFSMAALAUBQAAlqIAAMBSfAYAJEEwGFQkGGIeHDVPBhfM5qyqVMAIAAAsxQgASAK3262ToVPMBqrm2UDdnFWVEhgBAIClKAAAsBQFAACWogAAwFIUAABYigIAAEtRAABgqQ5zHUAwGJQaP2dBdElq/FzBYKbpFABSHCMAALBUhxkBuN1ufRxoknpNMB3FvGMbudISwHUxAgAASxkZAbzyyivatGmTJGnkyJF64oknTMQAAKslfQSwc+dOVVVVad26dXrrrbd04MABVVZWJjsGAFgv6SOAbt26qby8XDk5OZKkfv366cSJE/G588aA+bOAwheb/8y6yVyGxoCk28w9Plpkej0A52KTJMl1k9kzxCLBkBTjpKj1crQyEopPoHa6IEeSlCeXsQz1cnRLDLdPegH0798/+vWRI0e0adMm/e1vf2vz7aurq1v8eV5ennr36hlzvlh98knzQhc9unc1mKKn8vLytGfPnnbfw7lz51Tf4OjlA2afZGdDzU+ym7PNPck+bnDU/dy5mLZnXl6eet/+v3FMdeM+OfOJJKmHp4fRHLpJMe2feXl56tG7d5xD3bgLnzRvz/we5rZnD8W2LY2dBfTRRx/p5z//uZ544gl961vfavPtBg4cqNzc3K/9fPDgwXFM137z5s2TJC1atMhwktjs3btXtfn5pmPoRG2tJKl7777GMvST1Ldv35j2sVTYPzvKvpkK21JKn+3Z2NjY6oGzkQLYs2ePHn30Uc2fP1/jx483EQHXMXPmTNMRJKXPkwxIR0kvgJMnT2ru3LlasmSJCgoKkv3wAID/SnoBLF++XI2NjaqoqIj+zOv1atq0acmOAgBWS3oBPPnkk3ryySeT/bAAgKtwJTAAWIoCAABLUQAAYCkKAAAsRQEAgKUoAACwFAUAAJaiAADAUhQAAFiKAgAAS3WYReHjwe/3x7w6We1/py++PItle40ZM0aFhYUx3QcAXAsFEGcej8d0BABoEwrgKwoLCznqBmANPgMAAEtRAABgKZfjOI7pEG1xeV3L1tYERuqJ54fqffu2f03gjvKBeqzbMx7bUuoY2zNV9k0p8dvzWq+dfAaAlMaH6vHDtoyvjrA9GQEAQAd2rddOPgMAAEtRAABgKQoAACxFAQCApSgAALAUBQAAlqIAAMBSaXMh2OXLFS5dumQ4CQCkj8uvmS1d8pU2BRAKhSRJBw8eNJwEANJPKBRSp06drvhZ2lwJHIlEdOHCBWVnZ8vlcpmOAwBpwXEchUIh5eXlKSPjynf906YAAADxxYfAAGApCgAALEUBAIClKAAAsBQFAACWogAAwFIUAABYKm2uBDbt6aef1t69exUKhVRXV6d+/fpJks6ePavS0lI98sgjhhOmv+PHj6ukpCS6bS9bunSpevbsGf1+5syZWrhwob7xjW8kO2Ja2Lx5s15//XWFw2E5jqNJkyZpxowZrf7+qlWr1LlzZ02YMCGJKdPDjW7LdEMBtNGCBQskNb9IPfTQQ1q/fr0k6eWXXzYZq8Pp3r17dNu2ZtmyZUlKk34+/fRTLV68WGvXrpXb7daFCxc0ffp09enTR0VFRS3eZu/evRo6dGiSk6a+9mzLdMNbQHGwf/9+eb1ejR49OloIa9euVXl5efR3pk+frt27d2v37t2aPHmySktL9bvf/c5U5LRSXl6u2bNna+zYsfL7/SosLNTx48dNx0pJwWBQoVBIX3zxhSQpLy9PFRUV+va3v61NmzZp6tSpeuCBB1RSUqK9e/dq586d8vv9eumll/Tuu+8aTp9arrUtv7oP7t69W9OnT5fU/Dx//vnn9dOf/lRjxozRjh07jOVvC0YAcfD555/L5/Pp/PnzKiws1MMPP3zN3z9y5Ii2bdum/Pz8JCVMH/X19Zo0aVL0+4kTJ0qSunbtqqVLl0qSFi5caCRbOhgwYICKiopUXFysO++8U8OGDdPEiRPVq1cvPfXUU1q6dKk8Ho/WrFmj119/XUuXLlVhYaGGDh2qESNGmI6fUlrblr17977m7UKhkP7+97/L7/frxRdf1MiRI5OU+MZRAHEwYsQI5eTkyOPxyO1268yZM9f8/T59+vDi34qW3gIqLy/X9773PUOJ0s/TTz+tX/ziF6qqqlJVVZWmTp2qP/3pT3r11Vfl9/t1+PBhvffee1+bGAxf19q2vJbLRdq/f3+dPn06CSnbjwKIg6ysLzejy+WS4zjRPy+7PJ21pK9NyYrrY5u1zfbt29XQ0KBx48aprKxMZWVlWrVqlVauXKkXXnhBDzzwgIYMGaI77rhDK1euNB03pbW2LdesWSPpy/n1w+HwFbfLzc2VpLSYtZhDgARxu906dOiQHMfRsWPH9OGHH5qOBAt06tRJf/7zn6PvTzuOow8++EA5OTlyuVyaPXu2hg0bpsrKSjU1NUmSMjMzo1/jS61tyzvvvFNut1v/+c9/JElbt241GTMmjAAS5Ec/+pHefPNNlZSUqE+fPho8eLDpSLDAD3/4Q/3yl7/U7Nmzo6POESNG6NVXX1V5ebnGjh0rl8ul4cOHa8+ePZKa99UXXnhB+fn5KikpMRk/pbS2LefOnat77rlHzzzzjF555RUNHz7ccNL2Yz0AALAUbwEBgKUoAACwFAUAAJaiAADAUhQAAFiKAgBu0ObNm6NzvwDpjAIAAEtRAEAbvPjiiyouLtbkyZNVWVkpSTp8+LAefvhhTZ06VaNHj9acOXPU2NioDRs2yOv1Rm974sQJDR8+XJcuXTIVH2gRBQBcx5YtW/TOO+/orbfeis76KjUvpPLjH/9Yq1at0jvvvKPjx49r+/btKikpUV1dnT766CNJ0urVq/WTn/xEOTk5Jv8ZwNdQAMB17Nq1S2PGjFGXLl2UlZWlsrIySdJvf/tbeTweLVu2TH/4wx9UX1+vhoYG5eTkaMqUKVq9erWampq0bt06TZ061fC/Avg65gIC2uCrM6ZkZmZKkn7961+rqalJY8eO1ahRo3Ty5Mno73m9Xk2ePFlDhw5V//791atXLyO5gWthBABcx7333qvNmzfr7NmzikQi0fUKqqqqNHfuXI0bN06StG/fvuismj179tT3v/99Pffcc5o2bZqx7MC1MAIArmPkyJH68MMPVVZWpptvvlkDBgxQMBjUY489prlz56pz587q0qWLhgwZorq6uujtSktL9cwzz6T0ilCwG7OBAgkQiUT0xz/+UbfffrtmzZplOg7QIt4CAuLs/PnzGjZsmE6ePKmHHnrIdBygVYwAAMBSjAAAwFIUAABYigIAAEtRAABgKQoAACxFAQCApf4fkF3H8UInbtgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import library\n",
    "import seaborn\n",
    "from numpy import mean\n",
    "import matplotlib.pyplot as plt\n",
    "seaborn.set(style=\"whitegrid\")\n",
    "\n",
    "tip = seaborn.load_dataset(\"tips\")\n",
    "tip\n",
    "seaborn.boxplot(x=\"day\",y=\"tip\",data=tip,saturation=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e496b2fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total_bill'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HVVZWJo/Ho7feektZWVlJmg64cHy1IwAY4k4Xl6QNGzZo69atQ557/PHHNX/+fOMdAReGO10AMMQP0gDAENEFAENEFwAMEV0AMER0AcDQfwAeGWiZ2BR+8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import library\n",
    "import seaborn as sns\n",
    "import pandas as pf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "tip = sns.load_dataset(\"tips\")\n",
    "tip.describe()\n",
    "sns.boxplot(x=tip['total_bill'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4badcde1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tip', ylabel='day'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import library\n",
    "import seaborn as sns\n",
    "import pandas as pf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "tip = sns.load_dataset(\"tips\")\n",
    "\n",
    "sns.boxplot(x=\"tip\",y=\"day\",hue=\"smoker\",palette=\"Set2\",dodge=True,data=tip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5a4da085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tip', ylabel='day'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import library\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "tip = sns.load_dataset(\"tips\")\n",
    "sns.boxplot(x='tip',y='day',data=tip, palette=[\"C0\", \"C1\", \"C2\", \"k\"])\n",
    "#how to manage individual colors for each hue color\n",
    "# with palette we can change it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0ffdef3",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "kashti = sns.load_dataset(\"titanic\")\n",
    "kashti.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "74ed3640",
   "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": [
    "p1=sns.boxplot(x=\"survived\",y=\"age\",showmeans=True,data=kashti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bff37513",
   "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(x=\"survived\",y=\"age\",showmeans=True,meanprops={\"marker\":\"+\",\"markersize\":\"12\",\"markeredgecolor\":\"red\"},data=kashti)\n",
    "plt.xlabel=(\"How many survived\",size=14),\n",
    "plt.ylabel=(\"Age {years}\",size=14),\n",
    "plt.title=(\"Box plot of kitnay dooby or kitnay bach gaye\",size=14,weight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d67583c2",
   "metadata": {},
   "source": [
    "# Facet wrap and facet grid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a8146911",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'seaborn' has no attribute 'FacetWrap'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ANONYM~1\\AppData\\Local\\Temp/ipykernel_9576/1642394807.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#sns.FacetGrid(kashti,col =\"sex\",  hue =\"fare\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m#facet_wrap() creates a collection of plots (facets)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFacetWrap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'FacetWrap'"
     ]
    }
   ],
   "source": [
    "#FacetGrid class helps in visualizing distribution of one variable as well as\n",
    "#the relationship between multiple variables separately within subsets of your\n",
    "#dataset using multiple panels.\n",
    "#sns.FacetGrid(kashti,col =\"sex\",  hue =\"fare\")\n",
    "#facet_wrap() creates a collection of plots (facets)\n",
    "sns.FacetWrap('class')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "40458f94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Age (years)-Float/Int'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import library\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "tip = pd.read_csv(\"Data.csv\")\n",
    "#print(tip)\n",
    "sns.boxplot(x='Age',y='Age (years)-Float/Int',data=tip)\n",
    "#how to manage individual colors for each hue color\n",
    "# with palette we can change it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf8387b8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}