{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#NotchedBoxedPlots\" data-toc-modified-id=\"NotchedBoxedPlots-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>NotchedBoxedPlots</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NotchedBoxedPlots\n",
    "\n",
    "In this notebook we show how we can make great grahics displaying confidence intervals.\n",
    "\n",
    "Sources:\n",
    "\n",
    "* <https://sites.google.com/site/davidsstatistics/home/notched-box-plots>\n",
    "\n",
    "\n",
    "> The Notch - displays the a confidence interval around the median which is normally based on the median +/- 1.57 x IQR/sqrt of n.  According to Graphical Methods for Data Analysis (Chambers, 1983) although not a formal test the, if two boxes' notches do not overlap there is ‘strong evidence’ (95% confidence) their medians differ.  Note Here are the pages from the book.  Citation Chambers, John M., William S. Cleveland, Beat Kleiner, and Paul A. Tukey. \"Comparing Data Distributions.\" In Graphical Methods for Data Analysis, 62. Belmont, California: Wadsworth International Group;, 1983. ISBN 0-87150-413-8 International ISBN 0-534-98052-X\n",
    "\n",
    "![](https://sites.google.com/site/davidsstatistics/_/rsrc/1472777929924/home/notched-box-plots/Notched%20Box%20Plot%202.jpg?height=235&width=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_dataset = sns.load_dataset(\"titanic\")\n",
    "titanic_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do the ages of the passengers difer significantly by sex? \n",
    "# Most probably no, because the notches overlap\n",
    "sns.boxplot(x='sex', y='age' , notch= True, data=titanic_dataset);"
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# what if we compare by class?\n",
    "sns.boxplot(x='sex', y='age' , hue='class',notch= True, data=titanic_dataset);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SdyK4zElBhJkAbvymLK8XktwKydUCndvRFuYt/jA3tB0TsvOSu4aFGWGLjkHPXj0RExOLmJgYREf7/o2JiUVcXLwKT4hIfSEb+L7/0WM7PC6EQEtLC+rr6847KdT6f66tq0Nt7Rk0nqqE1+u55P6S0QRvWCQ8hvNOBBd9ahCGCID7+VxICEiyE1JbiOvcrZBcDn+Y6+VW6NwtEK5Ltz6QdDr06GFDbHxPxMYkITo6BrGxsYiOjkF0dIw/3E0mM/dRIrqMkA38K5EkCWazGWazGYmJfTv8O6/Xi6ams5d8SvD9XIvqmmqcPPFDB43o4LH2hrjMCUOL9M4mSO6WDq+nxMX3QmJKEmJiYtsCPOa8n2NhsVih12t88xmi66DZwL8St9uNhoZ6f7BfflioDs5zrZfcV9KHQRhN8BhM8BgioXM1q/AMgo9X0gPhVuh1UttwzIVDaqdPnURjYyOio2PQMzbW/ynt/N57e28+LIzXWoiuluYC3+v14uzZxsuO7dfV1aG2bWinuenspXeWdJDCzfAYIuEJM0FYkyF6XjzOb+aF367yuC8Yl9e5HHC5W+BwtqCm6iQM5Xbf0M5lPiGZoyyIiYlBz9ie/pOAb3jnpxOE1cpPBETnC9nA/+qrgygvL0NdXfuYvC/IGxsarjwmH9EbwpLKMXml6cMg9D3giejR8d/4x/wvvGjrcjnQeLYFVbVV0Lu/63DM32q1nfdpwXciSE5Owe23q79531++tyDN6kKSRUaSxYN+UTLCeX7SDIdbwtFmPexNBlQ1GXD4rFHxNkM28D/6aCsOHPj0olk30fD26nPhBVbOuglukgQRFgERFgEgBh1eDRFeSO72WT0t/gvBLncrak87oD9+xncx2O3EsGHDVQ38xMR+ePjhx1BZWY4vysvwyXEHAF9fIsHsRZLZjf4WGf0tMvpZPDAZuIbkRnfWJbUFux5VzQbYm8Nx6rw+Sky0DclDB2J8Siqs1k46QNcpZAMfAIQpBk03P6J2GRQIkg7CaIYwmuHt5M9M330YsJI6EhcXjyeemAnAN1vszJnTsNsrUFFRjsrKcnxXXoZ9Jxv8f9/LJJAU5UJSlIz+Fg/6W2RYjDwJBCMhgHqXDlVNvp67vcmAqmYj6s799De94uKQPGwA7ktJRXJyKpKTU2CzRQekvpAOfKJLBdeQnCRJiIuLR1xcPH72s7v8t9fX18NuL/efBCoryvB/FWf8x2MjgSSzC/0tMpLaPg3YjIIjjgEkBHD6nM7fc7c3GWBvNqKpbS6CJAEJvRMwdMQApLSFe//+KYiKilKtZgY+URCKjo5GdPSIC4aempqaYLdXoLKy7URQUYYv7cf9s1yt4UB/s6vtBOD7JNAzwsuTQDfwCuB4i943JNNkgL3ZgKrmMLS4fcf1Oh0SE/tixPAB/l57UlIyIiMj1S38Igx8ohuExWLBsGHDMWzYcP9tra0tsNsrfZ8CKitQWVGGrcd+hNfrG9gyhQH9o9y+k0CU79PATSYvdDwJdEj2AjWO9h67HlVNYahqNsDVdgEpzGBAv6T++PmINCQnpyIlJRV9+ybBaFT+ouv1YuAT3cAiI00YMuRmDBlys/82p9OJY8eqzvskcATbj9ohy77ECjcA/cwyjDov3F6J4Q/A7QWMOoFzXj2ONesht10ICg83Irl/Ku5LSfMPy/TpkwiD4caMzhuzaiLqUHh4ONLSBiItbSDOnTuHqqpKlJX9gD17PkF5+RE4ZaCs0fe/fprVzcAH4PZKKD974fqZW265FXffPRapqWno2zcpJBb7MfCJQoTD0Qy7vdJ/obei/DBqjtf4x/gtRmBYTPuFXt8YfxzH+AFcOEbvm1ljQPn3X+Gbb74CAOj1evTr2xfJKe1j9Kno378/wsMjVK786nR74Lvdbrz44ouorq6Gy+XC7NmzkZaWhnnz5kGSJAwYMAAFBQXQcd470TVrbGxsG7dvG7YpP4yTp0/7j8dEAElRLozsL/sDPtrIcO+ITgL6mD3oY/bg7pt802y8AjjdqvPNm28ywH62DAf22LFz53YAvhlWfRISkJxy4Swcs9ms5lPpVLcH/gcffACbzYYVK1agoaEBjzzyCAYPHoycnByMGjUK+fn5KCoqwoQJE7q76UvJ5xB2olT5doKczuELAq85TuVK1Ce5HGqXcFWEEKirq0VFRflPc/XLD6O2vt7/N/EmIMnsRHrKTz13K+fpXzedBPQyedHL5MLIeN9JQAigzqmDvW22TlVzJb45UI3du//rv99NvXq1fRJI8Z8IlFxMdTW6PfDvv/9+TJw4EYDvzarX61FaWoqRI0cCANLT07F3794rBr5eL8FmM11zHUajHpKrBRHHSq75MUJOXYXaFQQFg0F/Xe8tpQghcOLEcZSVlaGsrAxHjpSh7PAPaDzbBMC3gqB3lMAgswtJaW0rcaM8MIcx3ANFkoDYCC9iI7y4M87tv73RJfmmazYZUNV0FEcOncT+/Xv8x+N6xmLAwEFISxuAAQMGIC1tAGJjYwO+jbckhDLf/dfc3IzZs2djypQpWL58Ofbs8T35/fv3Y/PmzVi5cmWn93e7PWhouHR/lK5qbW2Fx8NtiQFg2bI/AgDmzctXuZLgoNfrEBmpbuB7PB4cP15z4bBMRRlaWn1LMvUS0CfKi6Qol39OfV+zjAhedbthONyS75pA808rbk84JLQHbg+LBcmpA/xTO5OTUxEf3+u6TwJxcZYOjyny9jl+/DieeeYZTJ8+HZMmTcKKFSv8xxwOB6xWqxLNXiDYFjyoqX3HSDVX+NFPvv32GyxZXABn2zeuhemAvlEejIp2o38/31z5RLMHRm6kdkMzhwkMjZExNOanr9c8JwNH208ATedgL2/EV4e+gLftLGA2mbBy1Rvo2VOZ4dduD/wzZ87gySefRH5+PkaPHg0AGDp0KEpKSjBq1CgUFxfjrrvuusKjEIUup/McnC4XHk1uwZ1xLvQ2eWDgHAZNiDAAA20yBtraTwIOuDzAjw49PqmJwK4aQJblTh/jenT72+ytt97C2bNn8eabbyI7OxvZ2dnIycnB6tWrMXXqVLjdbv8YP5GWDYtxo28Uw17rjHogxerBwB7uK//xder2Hn5eXh7y8vIuuX3dunXd3RQREV0F9i2IiDSCgU9EpBEMfCIijWDgExFpBAOfiEgjGPhERBrBwCci0ggGPhGRRjDwiYg0gnvvEalkU0UkLBrf2vh0q6/PGRfpVbkS9Z0+p3z/m4FPFGDh4RHo3bs36oRAndrFqKz2XC0AoMUcq3IlQcAE9Dbr/LvbKoGBTxRgQ4fegtdff1vtMoJCQcF8AMDChUtVrkQbOIZPRKQRDHwiIo1g4BMRaQQDn4hIIxj4REQawcAnItIIBj4RkUYoFviHDh1CdnY2AKCqqgrTpk3D9OnTUVBQAK+Xq+qIiAJNkcBfu3Yt8vLy4HQ6AQBLly5FTk4O1q9fDyEEioqKlGiWiIg6ochK2379+mH16tV44YUXAAClpaUYOXIkACA9PR179+7FhAkTOn0MvV6CzWZSojzNMRh8S7X5elKw4XszsBQJ/IkTJ+LHH3/0/y6EgCRJAACz2YympqYrPobHI9DQ0KJEeZojyx4A4OtJQYfvze4XF2fp8FhALtrqdD8143A4YLVaA9EsERGdJyCBP3ToUJSUlAAAiouLMWLEiEA0S0RE5wlI4Ofm5mL16tWYOnUq3G43Jk6cGIhmiYjoPIptj5yYmIiNGzcCAJKTk7Fu3TqlmiIioi7gwisiIo1g4BMRaQQDn4hIIxj4REQawcAnItIIBj4RkUYw8ImINIKBT0SkEQx8IiKNYOATEWkEA5+ISCMY+EREGsHAJyLSCAY+EZFGMPCJiDSCgU9EpBEMfA1wu92w2ytQX1+vdilEpCLFvvGKfD75ZCd27tyuag0VFUfg8XiQm5uD3r0TVK3l3nsnYNy4e1WtgUirAhb4Xq8XCxYswA8//ACj0YhFixYhKSkpUM1rltvthsfjAQA0NNQjLi4OBkOYylVRMAiGzojdXgEAKCiYr2odgDY6IwEL/B07dsDlcuEf//gHDh48iGXLluHPf/5zoJpXzbhx96r6Jlq79k1UVpZDlmXo9XokJ6fiqadmq1YP0flsthi1S9CUgAX+559/jrFjxwIAbrvtNnzzzTeBalrTdu/+L2RZBgDIsozi4l0MfAKgfmeEAi9ggd/c3IyoqCj/73q9HrIsw2C4fAl6vQSbzRSo8kJWRkYGtm3b5n+tMzIm8HUl0qiABX5UVBQcDof/d6/X22HYA4DHI9DQ0BKI0kLapEmZ+Oij/wUA6HQ6TJqUydeVKITFxVk6PBawaZl33HEHiouLAQAHDx7EwIEDA9W0pkVHx2D8+PsgSRLGj89AdHS02iURkUoC1sOfMGEC9u7di6ysLAghsGTJkkA1rXmZmVk4duwoHnssS+1SiEhFkhBCqF3E5bjdHg49EBFdpaAY0iEiInUx8ImINIKBT0SkEQx8IiKNCNqLtkRE1L3Ywyci0ggGPhGRRjDwiYg0goFPRKQRDHwiIo1g4BMRaQQDn4hIIxj4GrRlyxasXLlS7TIohMiyjOzsbGRlZaGxsbHbHvfnP/95tz0WBXB7ZCIKXadOnYLD4cCWLVvULoU6wcC/wW3ZsgW7du3CuXPncPr0acyYMQNFRUUoKyvDCy+8gBMnTuDjjz9Ga2sroqOj8cYbb1xw/8LCQnz44YeQJAkPPPAAZsyYodIzoRtZQUEB7HY75s+fD4fDgfr6egBAXl4eBg0ahAkTJuD222+H3W7H6NGj0dTUhK+++grJyclYsWIFDh8+jGXLlsHj8aC+vh4LFizAHXfc4X/8H374AYsWLQIA2Gw2LFmyBBZLx9sAUwcE3dA2b94sfvOb3wghhPjwww9FZmam8Hq9Yv/+/eL3v/+9WL16tfB4PEIIIZ588knx2Wefic2bN4sVK1aIsrIykZWVJWRZFrIsi+zsbFFeXq7m06Eb1LFjx8TkyZPFK6+8It59910hhBCVlZUiKytLCCHEkCFDRHV1tXC5XOK2224TZWVlwuv1ivHjx4vGxkaxdetW8f333wshhPjggw/ESy+9JIQQ4u677xZCCDF58mRRVlYmhBBi48aN4tVXXw30UwwJ7OGHgCFDhgAALBYLUlNTIUkSevToAbfbjbCwMDz33HMwmUw4ceIEZFn23+/w4cOoqanBzJkzAQCNjY2oqqpCSkqKGk+DQsDhw4fx6aefYtu2bQDgH8+32WxISEgAAJhMJqSlpQHwvWedTifi4+Px5ptvIiIiAg6HA1FRURc8bnl5ORYuXAgAcLvd6N+/f4CeUWhh4IcASZIue7vb7caOHTvw/vvvo7W1FY8++ijEeXvlpaSkIC0tDX/5y18gSRL+/ve/Y9CgQYEqm0JQSkoKHnroIUyaNAm1tbV4//33AXT8Hm23ePFirFy5EqmpqXj99ddRXV19wfHk5GQsX74cCQkJ+Pzzz3H69GnFnkMoY+CHMIPBgMjISGRl+b7LNi4uDqdOnfIfHzx4MEaPHo1p06bB5XLh1ltvRa9evdQql0LArFmz8NJLL2Hjxo1obm7GnDlzunS/hx56CH/4wx9gtVpx0003+a8BtFuwYAFyc3MhyzIkScLixYuVKD/kcXtkIiKN4Dx8IiKNYOATEWkEA5+ISCMY+EREGsHAJyLSCAY+EZFGMPCJiDSCgU90GZWVlcjKysITTzyB6dOn4/jx41i1ahWmTZuGqVOnYtu2bZBlGVlZWdi9ezfOnDmDBx98EMePH1e7dKIOceEV0WW8++67qKqqwvPPP4/PPvsMR44cwRdffIHXXnsNTqcTU6ZMQWFhIc6ePYtZs2YhLi4OM2fOxLhx49QunahD3FqB6DIyMzOxdu1aPPXUU7BYLBg8eDBKS0uRnZ0NwPeFH9XV1RgyZAjuuOMOHDx4EOnp6SpXTdQ5DukQXUZRURHuvPNOvPPOO7j//vuxZcsWjBo1CoWFhXjnnXfwy1/+En379sXBgwdRVlaGn/3sZ/jrX/+qdtlEneKQDtFlHD16FLm5uQgLC4PX68W8efPw73//G19//TVaWlqQkZGBX//618jKysIbb7yBhIQETI0mAYAAAABBSURBVJ48GYsXL8awYcPULp/oshj4REQawSEdIiKNYOATEWkEA5+ISCMY+EREGsHAJyLSCAY+EZFGMPCJiDTi/wFR5jSiu1DMHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# There seems to be a difference in the thirdclass, let's focus on that.\n",
    "# No, it was spurious\n",
    "\n",
    "Thirdclass=titanic_dataset[titanic_dataset['class']=='Third']\n",
    "\n",
    "sns.boxplot(x='sex', y='age'   ,notch= True, data=Thirdclass);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we see a significant difference\n",
    "# Elder people have more money\n",
    "\n",
    "sns.boxplot(x='class',y ='age'   ,notch= True, data= titanic_dataset);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we see a significant difference\n",
    "# Elder people have more money\n",
    "\n",
    "sns.boxplot(x='class',y ='age'   ,notch= True, width=.2,data= titanic_dataset);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='alive', y='age', hue='sex', notch= True, data=titanic_dataset);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}