ch03.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn import (\n",
    "    ensemble,\n",
    "    model_selection,    \n",
    "    preprocessing,\n",
    "    tree,\n",
    ")\n",
    "from sklearn.metrics import (\n",
    "    auc,\n",
    "    confusion_matrix,\n",
    "    roc_auc_score,\n",
    "    roc_curve,\n",
    ")\n",
    "from sklearn.model_selection import (\n",
    "    train_test_split,\n",
    "    StratifiedKFold,\n",
    ")\n",
    "from yellowbrick.classifier import (\n",
    "    ConfusionMatrix,\n",
    "    ROCAUC,\n",
    ")\n",
    "from yellowbrick.model_selection import (\n",
    "    LearningCurve,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = (\n",
    "    \"http://biostat.mc.vanderbilt.edu/\"\n",
    "    \"wiki/pub/Main/DataSets/titanic3.xls\"\n",
    ")\n",
    "df = pd.read_excel(url)\n",
    "orig_df = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass         int64\n",
       "survived       int64\n",
       "name          object\n",
       "sex           object\n",
       "age          float64\n",
       "sibsp          int64\n",
       "parch          int64\n",
       "ticket        object\n",
       "fare         float64\n",
       "cabin         object\n",
       "embarked      object\n",
       "boat          object\n",
       "body         float64\n",
       "home.dest     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/pandas_profiling/describe.py:404: FutureWarning: The join_axes-keyword is deprecated. Use .reindex or .reindex_like on the result to achieve the same functionality.\n",
      "  variable_stats = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1)\n"
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       "\n",
       "<div class=\"container pandas-profiling\">\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Overview</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Dataset info</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Number of variables</th>\n",
       "                <td>14 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Number of observations</th>\n",
       "                <td>1309 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total Missing (%)</th>\n",
       "                <td>21.0% </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Total size in memory</th>\n",
       "                <td>143.3 KiB </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Average record size in memory</th>\n",
       "                <td>112.1 B </td>\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"col-md-6 namecol\">\n",
       "        <p class=\"h4\">Variables types</p>\n",
       "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <th>Numeric</th>\n",
       "                <td>6 </td>\n",
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       "            <tr>\n",
       "                <th>Categorical</th>\n",
       "                <td>7 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Boolean</th>\n",
       "                <td>1 </td>\n",
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       "            <tr>\n",
       "                <th>Date</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Text (Unique)</th>\n",
       "                <td>0 </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <th>Rejected</th>\n",
       "                <td>0 </td>\n",
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       "                <th>Unsupported</th>\n",
       "                <td>0 </td>\n",
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       "    </div>\n",
       "    <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
       "        \n",
       "        <p class=\"h4\">Warnings</p>\n",
       "        <ul class=\"list-unstyled\"><li><a href=\"#pp_var_name\"><code>name</code></a> has a high cardinality: 1307 distinct values  <span class=\"label label-warning\">Warning</span></li><li><a href=\"#pp_var_age\"><code>age</code></a> has 263 / 20.1% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_sibsp\"><code>sibsp</code></a> has 891 / 68.1% zeros <span class=\"label label-info\">Zeros</span></li><li><a href=\"#pp_var_parch\"><code>parch</code></a> has 1002 / 76.5% zeros <span class=\"label label-info\">Zeros</span></li><li><a href=\"#pp_var_ticket\"><code>ticket</code></a> has a high cardinality: 939 distinct values  <span class=\"label label-warning\">Warning</span></li><li><a href=\"#pp_var_fare\"><code>fare</code></a> has 17 / 1.3% zeros <span class=\"label label-info\">Zeros</span></li><li><a href=\"#pp_var_cabin\"><code>cabin</code></a> has 1014 / 77.5% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_cabin\"><code>cabin</code></a> has a high cardinality: 187 distinct values  <span class=\"label label-warning\">Warning</span></li><li><a href=\"#pp_var_boat\"><code>boat</code></a> has 823 / 62.9% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_body\"><code>body</code></a> has 1188 / 90.8% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_home.dest\"><code>home.dest</code></a> has 564 / 43.1% missing values <span class=\"label label-default\">Missing</span></li><li><a href=\"#pp_var_home.dest\"><code>home.dest</code></a> has a high cardinality: 370 distinct values  <span class=\"label label-warning\">Warning</span></li> </ul>\n",
       "    </div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Variables</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_pclass\">pclass<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>3</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.2%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>2.2949</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>1</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>3</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
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       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram2537098389377086444\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAAAQtJREFUeJzt3MEJAkEQAEEVQzIIc/JtTgZhTmMC0qAgt5xV/4X5NDOvPc7MHIC3TlsPACs7bz0A%2B3W5PT5%2B87xffzDJ92wQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCMv9rLiH3/jYDxsEgkAgLHdi/Tsn5lpsEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIx5mZrYeAVdkgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEF7/3RGT0dEIxAAAAABJRU5ErkJggg%3D%3D\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives2537098389377086444,#minihistogram2537098389377086444\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives2537098389377086444\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles2537098389377086444\"\n",
       "                                                  aria-controls=\"quantiles2537098389377086444\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram2537098389377086444\" aria-controls=\"histogram2537098389377086444\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common2537098389377086444\" aria-controls=\"common2537098389377086444\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme2537098389377086444\" aria-controls=\"extreme2537098389377086444\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles2537098389377086444\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>3</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>0.83784</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.36509</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-1.3151</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>2.2949</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>0.76383</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>-0.59865</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>3004</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>0.70197</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>10.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram2537098389377086444\">\n",
       "            <img 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fr9Y3t3LlT5513nlJTU/XZZ5/JGCPp%2BPu1tm7dKpfLJUlyuVwqKiryPW7fvn3at2%2Bfbx4AAKA5jixYycnJioqK0u9//3vt3r1b7733nubPn6/bbrtNY8aMUXV1tebNm6fS0lLNmzdPXq9XY8eOlSRlZWVp7dq1WrlypXbt2qVZs2Zp5MiR6t69e5BTAQAAu3BkwWrfvr2ee%2B45HThwQJMmTVJubq4yMzN12223qX379lq6dKmKioqUkZEht9utZcuWKSYmRtLxcjZ79mwVFBQoKytLHTp0UF5eXpATAQAAO3Hse7B%2B8Ytf6Pnnnz/l3IABA7RmzZrTPjYjI0MZGRmttTQAAOBwjryCBQAAEEwULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACwWcgVr8uTJevnll3Xo0KFgLwUAAOBHCbmCNWTIED3zzDMaNmyY7rrrLn344YcyxgR7WQAAAAELuYJ1991365133tGSJUsUHh6uGTNmaOTIkVq0aJG%2B/vrrYC8PAACgWRHBXsCphIWFaejQoRo6dKi8Xq9efPFFLVmyRMuWLVNKSopuuukmXXHFFcFeJgAAwCmFZMGSpIqKCq1bt07r1q3Tl19%2BqZSUFF1zzTUqLy/X73//e23ZskW5ubnBXiYAAEATIVew1q5dq7Vr1%2BqTTz5RXFycJk6cqCeffFI9e/b0HdO5c2fNmzePggUAAEJSyBWs3NxcjRo1SgUFBbrsssvUpk3Tt4ldcMEF%2BvWvfx2E1QEAADQv5ArW%2B%2B%2B/r44dO6qqqspXrrZt26akpCSFh4dLklJSUpSSkhLMZQIAAJxWyH0X4eHDhzVmzBg9%2B%2ByzvrGpU6dqwoQJ2rdvXxBXBgAAEJiQK1iPPvqoevTood/85je%2BsQ0bNqhz587Ky8sL4soAAAACE3IF69NPP9X999%2Bv%2BPh431hcXJxmzZqlTZs2BXFlAAAAgQm5ghUREaHq6uom416vlzu6AwAAWwi5gnXZZZdp7ty5%2Bvbbb31jZWVlysvL0/Dhw4O4MgAAgMCE3HcR3nffffrNb36jK6%2B8UrGxsZKk6upqJSUlKScnJ8irAwAAaF7IFaxOnTppzZo1%2Buijj1RSUqKIiAj17t1bl1xyicLCwoK9PAAAgGaFXMGSpPDwcA0fPpyXBAEAgC2FXMHyeDxavHixtm7dqmPHjjV5Y/vbb7/dovNNnTpVcXFxeuyxxyRJO3bs0EMPPaQvv/xSvXv31iOPPKKLLrrId/z69eu1ePFieTweDRs2THPmzFFcXNzZBwMAAD8ZIfcm9//%2B7//We%2B%2B9p8suu0wTJ07UNddc4/fREq%2B//rree%2B893%2BdHjx7V1KlTlZaWptWrVys5OVnTpk3T0aNHJR2/Y3xubq6mT5%2BuwsJCVVdX874vAADQYiF3BWvTpk1avny50tLSzuo8VVVVmj9/vvr37%2B8b27Bhg6KiojRr1iyFhYUpNzdX77//vjZu3KiMjAytWLFCY8eO1cSJEyVJ8%2BfP16hRo1RWVqbu3buf1XoAAMBPR8hdwYqJiVGnTp3O%2Bjx/%2BMMfNGHCBPXu3ds35na7lZqa6nuzfFhYmFJSUlRcXOyb/2Gx69y5s7p06SK3233W6wEAAD8dIXcFa8KECVq%2BfLlmz57t%2B%2BHOLfXxxx/r008/1WuvvaaHH37YN%2B7xePwKl3T8uxZLSkokSRUVFUpISGgyX15e3qKvX1FRIY/H4zcWERHT5NxnKzy8jd%2BvTuHUXJJzszk1l0Q2O3JqLsnZ2SRn5Qq5glVVVaX169fr3XffVffu3RUZGek3/%2Bc///mMj6%2BtrdVDDz2kBx98UG3btvWb83q9Tc4XGRmpuro6SVJNTc0Z5wNVWFio/Px8v7Hs7GzNnDmzRecJVGxsdKucN9icmktybjan5pLIZkdOzSU5N5uTcoVcwZKk8ePH/%2BjH5ufn66KLLjrlLR6ioqKalKW6ujpfETvdfHR0y57wzMxMpaen%2B41FRMTo4MEjLTpPc8LD2yg2NlrV1V41NDRaeu5gcmouybnZnJpLIpsdOTWX5OxsklolV8eO7Sw9X6BCrmDl5eWd1eNff/11VVZWKjk5WZJ8hemNN97Q%2BPHjVVlZ6Xd8ZWWl76W7xMTEU87/8AdPByIhIaHJy4EezyHV17fOZmhoaGy1cweTU3NJzs3m1FwS2ezIqbkk52ZzUq6QfLGzoqJC%2Bfn5uvvuu/W///u/2rhxo3bv3h3QY1988UW99tprevXVV/Xqq68qPT1d6enpevXVV%2BVyufTZZ5/57q1ljNHWrVvlcrkkSS6XS0VFRb5z7du3T/v27fPNAwAABCLkCtaePXt09dVXa82aNXrjjTd09OhRbdiwQZMmTQrou/m6du2qHj16%2BD7atWundu3aqUePHhozZoyqq6s1b948lZaWat68efJ6vRo7dqwkKSsrS2vXrtXKlSu1a9cuzZo1SyNHjuQWDQAAoEVCrmA99thjGj16tN566y397Gc/kyQtXLhQ6enpevzxx8/q3O3bt9fSpUtVVFSkjIwMud1uLVu2TDExMZKk5ORkzZ49WwUFBcrKylKHDh3O%2BiVLAADw0xNy78HaunWr/vKXv/j9YOeIiAjdeeeduu6661p8vhM/IueEAQMGaM2aNac9PiMjQxkZGS3%2BOgAAACeE3BWsxsZGNTY2fYPbkSNHfvR9sQAAAM6lkCtYw4YN09KlS/1KVlVVlRYsWKAhQ4YEcWUAAACBCbmCdf/992v79u0aNmyYamtrdccdd2jUqFH67rvvdN999wV7eQAAAM0KufdgJSYm6tVXX9X69eu1c%2BdONTY2KisrSxMmTFD79u2DvTwAAIBmhVzBkqTo6GhNnjw52MsAAAD4UUKuYE2ZMuWM8839LEIAAIBgC7mC1bVrV7/P6%2BvrtWfPHn355Ze66aabgrQqAACAwIVcwTrdjT0LCgpUXl5%2BjlcDAADQciH3XYSnM2HCBP3tb38L9jIAAACaZZuC9dlnn3GjUQAAYAsh9xLhqd7kfvjwYf3jH//Q9ddfH4QVAQAAtEzIFawuXbr4/RxCSfrZz36mX//61/rVr34VpFUBAAAELuQK1sk/nBkAAMBuQq5gbdmyJeBjL7744lZcCQAAwI8TcgXrxhtv9L1EaIzxjZ88FhYWpp07d577BQIAADQj5ArWM888o7lz5%2Bree%2B/VoEGDFBkZqc8//1yzZ8/WNddco3HjxgV7iQAAAGcUcrdpyMvL04MPPqgrr7xSHTt2VLt27TRkyBDNnj1bL730krp27er7AAAACEUhV7AqKipOWZ7at2%2BvgwcPBmFFAAAALRNyBWvgwIFauHChDh8%2B7BurqqrSggULdMkllwRxZQAAAIEJufdg/f73v9eUKVN02WWXqWfPnjLG6JtvvlF8fLz%2B/Oc/B3t5AAAAzQq5gtWrVy9t2LBB69ev11dffSVJuuGGG3TVVVcpOjo6yKsDAABoXsgVLEnq0KGDJk%2BerO%2B%2B%2B07du3eXdPxu7gAAAHYQcu/BMsbo8ccf18UXX6zx48ervLxc9913n3Jzc3Xs2LFgLw8AAKBZIVewXnzxRa1du1YPPfSQIiMjJUmjR4/WW2%2B9pfz8/CCvDgAAoHkhV7AKCwv14IMPKiMjw3f39nHjxmnu3Ll67bXXgrw6AACA5oVcwfruu%2B/Ur1%2B/JuN9%2B/aVx%2BMJwooAAABaJuTe5N61a1d9/vnn6tatm9/4%2B%2B%2B/73vDO/6/tNyNwV5CwP72u6HBXgIAAOdEyBWsW2%2B9VY888og8Ho%2BMMfr4449VWFioF198Uffff3%2BwlwcAANCskCtYkyZNUn19vZ5%2B%2BmnV1NTowQcfVFxcnH73u98pKysr2MsDAABoVsgVrPXr12vMmDHKzMzUgQMHZIxRp06dgr0sAACAgIXcm9xnz57tezN7XFwc5QoAANhOyBWsnj176ssvvzzr8%2BzZs0e33nqrkpOTNXLkSC1fvtw3V1ZWpptvvlkDBw7UuHHj9OGHH/o99qOPPtL48ePlcrk0ZcoUlZWVnfV6AADAT0fIvUTYt29f3XPPPVq%2BfLl69uypqKgov/m8vLxmz9HY2KipU6eqf//%2BWrNmjfbs2aO77rpLiYmJGj9%2BvLKzs9WnTx%2BtWrVKb731lqZPn64NGzaoS5cu2rt3r7KzszVjxgwNHz5cBQUFuvPOO7Vu3TrffbkAAADOJOQK1tdff63U1FRJ%2BtH3vaqsrFS/fv308MMPq3379urZs6cuueQSFRUV6fzzz1dZWZlefvllxcTEqFevXvr444%2B1atUqzZgxQytXrtRFF12kW265RdLxQjd06FBt3rxZgwcPtiwnAABwrpAoWPPnz9f06dMVExOjF1988azPl5CQoMWLF0s6/rMNt27dqi1btuihhx6S2%2B3WL3/5S8XExPiOT01NVXFxsSTJ7XYrLS3NNxcdHa2kpCQVFxdTsAAAQEBComA9//zzuvXWW/1Kz9SpUzV37lwlJCSc1bnT09O1d%2B9ejRo1SldeeaUeffTRJufs1KmTysvLJR2/anam%2BUBUVFQ0ufoWERFz1llOFh4ecm%2BhO6OIiMDWeyKX3fIFwqnZnJpLIpsdOTWX5OxskrNyhUTBMsY0GduyZYtqa2vP%2BtxPPvmkKisr9fDDDysvL09er9f3Q6RPiIyMVF1dnSQ1Ox%2BIwsLCJj%2BYOjs7WzNnzvyRKZyhY8d2LTo%2BNja6lVYSfE7N5tRcEtnsyKm5JOdmc1KukChYral///6SpNraWt1zzz2aNGmSvF6v3zF1dXVq27atJCkqKqpJmaqrq1NsbGzAXzMzM1Pp6el%2BYxERMTp48MiPiXBadmv6geYPD2%2Bj2NhoVVd71dDQ2MqrOrecms2puSSy2ZFTc0nOziapVXK19D/3VnFkwaqsrFRxcbFGjx7tG%2Bvdu7eOHTum%2BPh47d69u8nxJ16%2BS0xMVGVlZZP5U/0A6tNJSEho8nKgx3NI9fXO2wwt0dL8DQ2Njv09c2o2p%2BaSyGZHTs0lOTebk3KFzCUQK2%2BB8N1332n69Onav3%2B/b2z79u2Ki4tTamqqvvjiC9XU1PjmioqK5HK5JEkul0tFRUW%2BOa/Xqx07dvjmAQAAmhMyV7Dmzp3rd8%2BrY8eOacGCBWrXzv/SXiD3werfv7%2BSkpL0wAMPKCcnR//85z%2B1YMEC3X777Ro0aJA6d%2B6snJwc3XnnnXrnnXe0bds233knTZqk5557TsuWLdOoUaNUUFCgbt268R2EAAAgYCFRsC6%2B%2BOIm33WXnJysgwcP6uDBgy0%2BX3h4uJYsWaI5c%2BYoMzNT0dHRuvHGGzVlyhSFhYVpyZIlys3NVUZGhnr06KGCggJ16dJFktStWzc99dRTevTRR1VQUKDk5GQVFBRwk1EAABCwkChYVtz76mSJiYlNvpPvhB49emjFihWnfeyIESM0YsQIy9cEAAB%2BGkLmPVgAAABOQcECAACwGAULAADAYiHxHiwACGVpuRuDvYQW%2BdvvhgZ7CcBPHlewAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGKOLVj79%2B/XzJkzNWjQIA0fPlx5eXmqra2VJJWVlenmm2/WwIEDNW7cOH344Yd%2Bj/3oo480fvx4uVwuTZkyRWVlZcGIAAAAbMqRBcsYo5kzZ8rr9eovf/mLFi1apHfeeUeLFy%2BWMUbZ2dk6//zztWrVKk2YMEHTp0/X3r17JUl79%2B5Vdna2MjIy9Ne//lVxcXG68847ZYwJcioAAGAXEcFeQGvYvXu3iouL9fe//13nn3%2B%2BJGnmzJn6wx/%2BoMsuu0xlZWV6%2BeWXFRMTo169eunjjz/WqlWrNGPGDK1cuVIXXXSRbrnlFklSXl6ehg4dqs2bN2vw4MHBjAUAAGzCkVew4uPjtXz5cl%2B5OuHw4cNyu9365S9/qZiYGN94amqqiouLJUlut1tpaWm%2BuejoaCUlJfnmAQAAmuPIK1ixsbEaPny47/PGxkatWLFCQ4YMkcfjUUJCgt/xnTp1Unl5uSR%2BUf33AAAUPklEQVQ1Ox%2BIiooKeTwev7GIiJgm5z1b4eH26scREYGt90Quu%2BULhFOzOTWXZM9MP/W95tRckrOzSc7K5ciCdbIFCxZox44d%2Butf/6o//elPioyM9JuPjIxUXV2dJMnr9Z5xPhCFhYXKz8/3G8vOztbMmTN/ZAJn6NixXYuOj42NbqWVBJ9Tszk1l92w145zai7JudmclMvxBWvBggV64YUXtGjRIvXp00dRUVGqqqryO6aurk5t27aVJEVFRTUpU3V1dYqNjQ34a2ZmZio9Pd1vLCIiRgcPHvmRKU7Nbk0/0Pzh4W0UGxut6mqvGhoaW3lV55ZTszk1l2S/fSax15yaS3J2Nkmtkqul/%2BGwiqML1pw5c/TSSy9pwYIFuvLKKyVJiYmJKi0t9TuusrLS9/JdYmKiKisrm8z369cv4K%2BbkJDQ5OVAj%2BeQ6uudtxlaoqX5GxoaHft75tRsTs1lN%2By145yaS3JuNiflst9/zQKUn5%2Bvl19%2BWQsXLtRVV13lG3e5XPriiy9UU1PjGysqKpLL5fLNFxUV%2Bea8Xq927NjhmwcAAGiOIwvWV199pSVLlui3v/2tUlNT5fF4fB%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%2BuSTT3xjZWVluvnmmzVw4ECNGzdOH374od9jPvroI40fP14ul0tTpkxRWVnZuV42AACwMUcXrNraWt11110qKSnxjRljlJ2drfPPP1%2BrVq3ShAkTNH36dO3du1eStHfvXmVnZysjI0N//etfFRcXpzvvvFPGmGDFAAAANuPYglVaWqrrrrtO3377rd/4pk2bVFZWptmzZ6tXr16aNm2aBg4cqFWrVkmSVq5cqYsuuki33HKLfvGLXygvL0///Oc/tXnz5mDEAAAANuTYgrV582YNHjxYhYWFfuNut1u//OUvFRMT4xtLTU1VcXGxbz4tLc03Fx0draSkJN88AABAcyKCvYDWcv31159y3OPxKCEhwW%2BsU6dOKi8vD2g%2BEBUVFfJ4PH5jERExTc57tsLD7dWPIyICW%2B%2BJXHbLFwinZnNqLsmemX7qe82puSRnZ5OclcuxBet0vF6vIiMj/cYiIyNVV1cX0HwgCgsLlZ%2Bf7zeWnZ2tmTNn/shVO0PHju1adHxsbHQrrST4nJrNqbnshr12nFNzSc7N5qRcP7mCFRUVpaqqKr%2Bxuro6tW3b1jd/cpmqq6tTbGxswF8jMzNT6enpfmMRETE6ePDIj1z1qdmt6QeaPzy8jWJjo1Vd7VVDQ2Mrr%2Brccmo2p%2BaS7LfPJPaaU3NJzs4mqVVytfQ/HFb5yRWsxMRElZaW%2Bo1VVlb6Xr5LTExUZWVlk/l%2B/foF/DUSEhKavBzo8RxSfb3zNkNLtDR/Q0OjY3/PnJrNqbnshr12nFNzSc7N5qRc9vuv2VlyuVz64osvVFNT4xsrKiqSy%2BXyzRcVFfnmvF6vduzY4ZsHAABozk%2BuYA0aNEidO3dWTk6OSkpKtGzZMm3btk3XXnutJGnSpEnaunWrli1bppKSEuXk5Khbt24aPHhwkFcOAADs4idXsMLDw7VkyRJ5PB5lZGRo3bp1KigoUJcuXSRJ3bp101NPPaVVq1bp2muvVVVVlQoKChQWFhbklQMAALv4SbwH6x//%2BIff5z169NCKFStOe/yIESM0YsSI1l4WAABwqJ/cFSwAAIDWRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIUrFOora3VAw88oLS0NA0bNkx//OMfg70kAABgIxHBXkAomj9/vrZv364XXnhBe/fu1X333acuXbpozJgxwV4aAACwAQrWSY4ePaqVK1fq2WefVVJSkpKSklRSUqK//OUvFCwAABAQXiI8ya5du1RfX6/k5GTfWGpqqtxutxobG4O4MgAAYBdcwTqJx%2BNRx44dFRkZ6Rs7//zzVVtbq6qqKsXFxTV7joqKCnk8Hr%2BxiIgYJSQkWLrW8HB79eOIiMDWeyKX3fIFwqnZnJpLsmemn/pec2ouydnZJGflomCdxOv1%2BpUrSb7P6%2BrqAjpHYWGh8vPz/camT5%2BuGTNmWLPI/1NRUaGb/rVEmZmZlpe3YKqoqNALLyx3XC7Judmcmkty7j6TnPu8OTWX1PJsn86zx1tbKioq9NRTTznqOXNOVbRIVFRUkyJ14vO2bdsGdI7MzEytXr3a7yMzM9PytXo8HuXn5ze5WmZ3Ts0lOTebU3NJZLMjp%2BaSnJvNibm4gnWSxMREHTx4UPX19YqIOP7b4/F41LZtW8XGxgZ0joSEBMc0cAAA0HJcwTpJv379FBERoeLiYt9YUVGR%2BvfvrzZt%2BO0CAADNozGcJDo6WhMnTtTDDz%2Bsbdu26a233tIf//hHTZkyJdhLAwAANhH%2B8MMPPxzsRYSaIUOGaMeOHXriiSf08ccf6/bbb9ekSZOCvaxTateunQYNGqR27doFeymWcmouybnZnJpLIpsdOTWX5NxsTssVZowxwV4EAACAk/ASIQAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQpWiKqrq9P48eP1ySefnPaYHTt2aPLkyXK5XJo0aZK2b9/uN79%2B/XqNHj1aLpdL2dnZOnDgQGsvu1mB5Hr33Xc1YcIEJScn6%2Bqrr9bbb7/tN5%2BWlqYLL7zQ7%2BPIkSOtvfRmBZLtjjvuaLL2d955xzf/pz/9ScOHD1dycrIeeOABeb3ec7H0ZjWX7cYbb2yS68ILL1ROTo4k6fvvv28yN3jw4HMZwc/%2B/fs1c%2BZMDRo0SMOHD1deXp5qa2tPeayd9llLctltn7Ukm932WaDZ7LbP9uzZo1tvvVXJyckaOXKkli9fftpj7bTPAmYQcmpqakx2drbp06eP2bRp0ymPOXLkiBk6dKh57LHHTGlpqZkzZ4659NJLzZEjR4wxxrjdbjNgwACzZs0as3PnTvPrX//aTJ069VzGaCKQXDt37jRJSUnmhRdeMN98841ZsWKFSUpKMjt37jTGGFNeXm769Oljvv32W1NRUeH7aGxsPJdRmggkmzHGXH755Wbt2rV%2Ba6%2BtrTXGGLNx40aTmppq/ud//se43W4zbtw488gjj5yrCKcVSLaDBw/6ZXrzzTdNUlKS2bZtmzHGmE8//dQMGjTI75jKyspzGcOnsbHRXHfddea2224zX375pdmyZYu5/PLLzWOPPdbkWDvts5bksts%2Ba0k2Y%2By1z1qSzU77rKGhwVxxxRXm7rvvNl9//bV59913TUpKilm3bl2TY%2B20z1qCghViSkpKzK9%2B9Stz9dVXn/EftJUrV5r09HTfX3iNjY3m8ssvN6tWrTLGGHPvvfea%2B%2B67z3f83r17zYUXXmi%2B/fbb1g9xCoHmWrBggbn11lv9xm655RazcOFCY4wxf//7383QoUNbfb0tEWi22tpa069fP7N79%2B5Tzl9//fXmySef9H2%2BZcsWM2DAAHP06NFWWXcgAs32Q/X19WbcuHFm0aJFvrFXXnnFZGZmtuZSA1ZaWmr69OljPB6Pb%2By1114zw4YNa3KsnfZZS3LZbZ%2B1JJvd9llLsv1QqO%2Bz/fv3m//8z/80hw4d8o1lZ2ebhx56qMmxdtpnLcFLhCFm8%2BbNGjx4sAoLC894nNvtVmpqqsLCwiRJYWFhSklJUXFxsW8%2BLS3Nd3znzp3VpUsXud3u1lv8GQSa65prrtE999zTZPzQoUOSpNLSUv385z9vlTX%2BWIFm2717t8LCwtS9e/cmcw0NDfr888/9nrOBAwfq2LFj2rVrl%2BVrDlSg2X5o9erV%2Bv777/Xb3/7WN1ZaWqqePXu2wgpbLj4%2BXsuXL9f555/vN3748OEmx9ppn7Ukl932WUuy2W2ftSTbD4X6PktISNDixYvVvn17GWNUVFSkLVu2aNCgQU2OtdM%2Ba4mIYC8A/q6//vqAjvN4POrdu7ffWKdOnVRSUiJJqqioUEJCQpP58vJyaxbaQoHm6tWrl9/nJSUl%2Bvjjj/Uf//EfkqSvvvpKXq9XN954o77%2B%2Bmv169dPDzzwQFD/MQg02%2B7du9W%2BfXvNmjVLmzdv1r/%2B679qxowZGjFihKqrq1VbW%2Bv3nEVEROi8884L2nMmBZ7tBGOMli9frilTpqhdu3a%2B8a%2B%2B%2Bkr19fW69tprtX//fqWlpSknJ6fJn9FzITY2VsOHD/d93tjYqBUrVmjIkCFNjrXTPmtJLrvts5Zks9s%2Ba0m2E%2Bywz34oPT1de/fu1ahRo3TllVc2mbfTPmsJrmDZlNfrVWRkpN9YZGSk6urqJEk1NTVnnLeDAwcOaMaMGUpJSdG///u/Szr%2Bl%2Bf333%2BvO%2B64Q0uWLFHbtm118803N/u/vVCwe/du1dTUaNiwYVq%2BfLlGjBihO%2B64Q59//rlqamokyfbP2SeffKLy8nJdd911fuO7d%2B/W4cOHlZOTo0WLFqmiokK33367GhoagrTS/2/BggXasWOH/uu//qvJnJ332Zly/ZAd99mZstl9nwXyvNltnz355JN65plntHPnTuXl5TWZt/M%2BOxOuYNlUVFRUkz9cdXV1atu27Rnno6Ojz9kaz0ZlZaV%2B85vfyBijJ598Um3aHP%2B/wHPPPadjx475/tf2%2BOOPa8SIEXrnnXd09dVXB3PJzbrzzjt14403qkOHDpKkvn376osvvtArr7zi%2B8vUzs%2BZJL3xxhu67LLLdN555/mNv/766woLC/P9%2BXzyySc1bNgwud1upaSkBGOpko7/Y/bCCy9o0aJF6tOnT5N5u%2B6z5nKdYMd91lw2O%2B%2BzQJ83u%2B2z/v37S5Jqa2t1zz33aNasWX6Fya77rDlcwbKpxMREVVZW%2Bo1VVlb6LqOebj4%2BPv6crfHH2r9/v2644QbV1dXpz3/%2Bs%2BLi4nxzkZGRfpfEo6Ki1K1bN%2B3fvz8YS22RNm3a%2BP7SP%2BGCCy7Q/v37dd555ykqKsrvOauvr1dVVZUtnrMTPvjgA99VkB%2BKjo72/WUpHb%2B8f9555wX1eZszZ46ef/55LViw4JQvW0j23GeB5JLsuc8CyWbXfRbo8ybZY59VVlbqrbfe8hvr3bu3jh071uRKqB33WSAoWDblcrn02WefyRgj6fhr8lu3bpXL5fLNFxUV%2BY7ft2%2Bf9u3b55sPVUePHtVtt92mNm3aaMWKFUpMTPTNGWM0evRorV692u/4PXv26IILLgjGclvk/vvv992v5oRdu3bpggsuUJs2bdS/f3%2B/56y4uFgRERHq27fvuV7qj3LgwAGVlZUpNTXVb/zw4cO6%2BOKLtWnTJt/Y/v37dfDgwaA9b/n5%2BXr55Ze1cOFCXXXVVac9zm77LNBcdtxngWaz4z4LNJtkn3323Xffafr06X7lbvv27YqLi/Mr85L99lnAgvK9iwjIyd8WX1FRYbxerzHGmEOHDpkhQ4aYOXPmmJKSEjNnzhwzdOhQ331Dtm7dapKSkswrr7ziu2/ItGnTgpLjZGfKtXDhQjNgwADjdrv97uVSXV1tjDFmzpw5ZuTIkWbTpk3myy%2B/NNnZ2Wb8%2BPGmvr4%2BKFlOdqZsb7zxhklKSjJr1qwx33zzjXnqqafMgAEDTFlZmTHGmPXr15uUlBTz5ptvGrfbba666iozZ86coOQ4lTNlM8aYTZs2mf79%2B5/yXknTpk0zv/rVr4zb7Tbbt283WVlZ5rbbbjsn6z5ZaWmp6devn1m0aJHfn7GKigpjjH33WUty2W2ftSSb3fZZS7IZY599Vl9fbzIyMswtt9xiSkpKzLvvvmsuvfRS86c//ckYY9991hIUrBB28j9offr08d0XxJjjN1%2BbOHGi6d%2B/v7n22mvNF1984ff4VatWmREjRpiBAwea7Oxsc%2BDAgXO29jM5U64rr7zS9OnTp8nHiXug1NTUmLy8PDN06FDjcrnMtGnTzN69e4OS41Sae85eeeUVc8UVV5iLLrrIXHPNNWbz5s1%2Bj1%2B6dKm55JJLTGpqqsnJyTE1NTXnbO3NaS7b66%2B/ftp7J1VVVZn777/fDB482CQnJ5t77rnHVFVVtfqaT2Xp0qWn/DPWp08fY4x991lLctltn7X0ObPTPmtpNrvsM2OO37A2OzvbpKSkmKFDh5qnn37aVwztus9aIsyY/7smBwAAAEvwHiwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACL/T9JitP5Vug6bwAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common2537098389377086444\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">709</td>\n",
       "        <td class=\"number\">54.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">323</td>\n",
       "        <td class=\"number\">24.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:46%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">277</td>\n",
       "        <td class=\"number\">21.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:39%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme2537098389377086444\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">323</td>\n",
       "        <td class=\"number\">24.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:46%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">277</td>\n",
       "        <td class=\"number\">21.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:39%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">709</td>\n",
       "        <td class=\"number\">54.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">323</td>\n",
       "        <td class=\"number\">24.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:46%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">277</td>\n",
       "        <td class=\"number\">21.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:39%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">709</td>\n",
       "        <td class=\"number\">54.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_survived\">survived<br/>\n",
       "            <small>Boolean</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr class=\"\">\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.2%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.38197</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minifreqtable5347592083870176753\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>0</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 61.8%\">\n",
       "            809\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:62%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 38.2%\">\n",
       "            500\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable5347592083870176753, #minifreqtable5347592083870176753\"\n",
       "        aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable5347592083870176753\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">809</td>\n",
       "        <td class=\"number\">61.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">500</td>\n",
       "        <td class=\"number\">38.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:62%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_name\">name<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"alert\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>1307</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>99.8%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>0.0%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>0</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable7426848542639285730\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>Kelly, Mr. James</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.2%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        2\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>Connolly, Miss. Kate</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.2%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        2\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>Vartanian, Mr. David</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.1%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        1\n",
       "    </td>\n",
       "</tr><tr class=\"other\">\n",
       "    <th>Other values (1304)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 99.6%\">\n",
       "            1304\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable7426848542639285730, #minifreqtable7426848542639285730\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable7426848542639285730\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">Kelly, Mr. James</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Connolly, Miss. Kate</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Vartanian, Mr. David</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Madsen, Mr. Fridtjof Arne</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Geiger, Miss. Amalie</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Stanley, Mr. Edward Roland</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Nakid, Mr. Sahid</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Sutton, Mr. Frederick</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Fortune, Mr. Mark</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Bourke, Mrs. John (Catherine)</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (1297)</td>\n",
       "        <td class=\"number\">1297</td>\n",
       "        <td class=\"number\">99.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_sex\">sex<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>2</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>0.2%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>0.0%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>0</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable4404782374317584295\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>male</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 64.4%\">\n",
       "            843\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>female</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:55%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 35.6%\">\n",
       "            466\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable4404782374317584295, #minifreqtable4404782374317584295\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable4404782374317584295\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">male</td>\n",
       "        <td class=\"number\">843</td>\n",
       "        <td class=\"number\">64.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">female</td>\n",
       "        <td class=\"number\">466</td>\n",
       "        <td class=\"number\">35.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:55%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_age\">age<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>99</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>7.6%</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>20.1%</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>263</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>29.881</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0.1667</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>80</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram9163199437125564854\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAAATFJREFUeJzt3cGNglAUQNFxYkkWYU%2Bup6cpwp6wAXMDJoSnnrMn%2BZvL930gnpZlWX6Ap36PXgBMdj56AUe53P43X3P/u%2B6wEiazg0AQCASBQPjaGeQVW%2BcWM8v7s4NAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEr7vvyGe9788OAkEgEAQCQSAQBAJBIBAEAkEgEDwoHMbDxVnsIBAEAkEgEAQCQSAQBAJh3DGvY04mOU37l9tXAmEbN5T1/MSCIBAIAoEwbkhnfw5C1rODQBAIBIFAEAgEQzqrfOtgLxB28wlRjXvVBCYxg0AQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUB4ALFNJ5gUfuDGAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives9163199437125564854,#minihistogram9163199437125564854\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives9163199437125564854\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles9163199437125564854\"\n",
       "                                                  aria-controls=\"quantiles9163199437125564854\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram9163199437125564854\" aria-controls=\"histogram9163199437125564854\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common9163199437125564854\" aria-controls=\"common9163199437125564854\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme9163199437125564854\" aria-controls=\"extreme9163199437125564854\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles9163199437125564854\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0.1667</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>5</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>21</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>28</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>39</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>57</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>80</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>79.833</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>18</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>14.413</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.48236</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>0.14695</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>29.881</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>11.262</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>0.40767</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>31256</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>207.75</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>10.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram9163199437125564854\">\n",
       "            <img 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om4ApWcHCwzpw5U2m9pKSET3QHAACOEHAFq0ePHkpLS9O3337rW8vPz1d6erq6d%2B9u42QAAABVE3A/RThhwgQ9/PDD6tu3ryIiIiRJZ86cUYcOHTRp0iSbpwMAALi2gCtYDRs21GuvvaaPPvpIubm5Cg4O1q233qq77rpLHo/H7vEAGNBv3od2j1Atn85MtnsEAA4TcAVLkoKCgtS9e3dOCQIAAEcKuILl9Xo1b9487d27VxcvXqz0xvZ3333XpskAAACqJuAK1nPPPacDBw7onnvu0a9//Wu7xwEAAKi2gCtYu3bt0vLly5WQkGD3KAAAANcl4D6mITw8XA0bNrR7DAAAgOsWcAVr4MCBWr58ucrLy%2B0eBQAA4LoE3CnC4uJibd%2B%2BXf/4xz/UvHlzhYSE%2BG3/61//Wq39lZWVafDgwXruuefUuXNnSVJaWppWr17td7vnnntOI0aMkCRt375d8%2BbNk9frVbdu3TRjxgw1aNDgBlIBAICaJOAKliQNGDDAyH5KS0s1btw45ebm%2Bq3n5eVp3Lhxuu%2B%2B%2B3xrdevWlSTt379fU6ZM0bRp09S2bVvNnDlTkyZN0tKlS43MBAAA3C/gClZ6erqR/Rw%2BfFjjxo276u8vzMvL06OPPur3C6UvW7Nmjfr166dBgwZJkmbNmqVevXopPz9fzZs3NzIbAABwt4B7D5YkFRQUKDMzU%2BPGjdP//u//6q233tKRI0eqtY9PPvlEnTt3VlZWlt/62bNndfr0abVs2fKqX5eTk%2BP3E4xNmjRR06ZNlZOTU%2B0cAACgZgq4I1jHjh3T/fffr7p16%2Br06dMaO3as3njjDU2aNEkrV65UTExMlfbz4IMPXnU9Ly9PHo9HS5Ys0fvvv6969erp4Ycf9p0uLCgoUFRUlN/XNGzYUKdOnapyhoKCAnm9Xr%2B14ODwSvu9EUFBtfz%2BdBM3Z4Nzue356ObXGdmcx425Aq5g/fnPf1bv3r2VlpamuLg4SdKcOXM0YcIEvfjii5XenF5dR44ckcfjUatWrTRixAjt2bNHzz33nOrWras%2BffrowoULld5YHxISorKysirfR1ZWljIzM/3WUlJSlJqaekOzX01ERJjxfQYKN2eD87j1%2BejWXBLZnMhNuQKuYO3du1dr1671%2B8XOwcHBevLJJ3X//fff8P4HDRqkXr16qV69epKktm3b6ptvvtG6devUp08fhYaGVipTZWVlCgur%2BoM%2BfPhwJSUl%2Ba0FB4erqOjcDc9/WVBQLUVEhOnMmRKVl1cY228gcHM2OJfbno9ufp2RzXluZq769esY3V9VBVzBqqioUEVF5b/cc%2BfOKSgo6Ib37/F4fOXqslatWmnXrl2SpMaNG6uwsNBve2Fh4VXfEP9zoqKiKp0O9Hp/0KVL5l8M5eUVN2W/gcDN2eA8bn0%2BujWXRDYnclOugDvZ2a1bNy1dutSvZBUXFysjI0NdunS54f3Pnz9fo0aN8ls7dOiQWrVqJUmKiYlRdna2b9vJkyd18uTJKr/3CwAAIOAK1sSJE3XgwAF169ZNpaWl%2Bo//%2BA/16tVLx48f14QJE254/7169dKePXv0yiuv6Ntvv9Xf/vY3bdmyRY888ogk6YEHHtDWrVu1YcMGHTp0SOPHj1fPnj35iAYAAFBlAXeKsHHjxtqyZYu2b9%2BuL7/8UhUVFXrggQc0cOBA34eB3ojf/va3mj9/vhYsWKD58%2BcrOjpas2fPVmxsrCQpNjZW06dP14IFC/T999%2Bra9eumjFjxg3fLwAAqDkCrmBJUlhYmIYNG2Zsf1999ZXf9d69e6t3794/e/vBgwdr8ODBxu4fAADULAFXsEaOHPlPt1f3dxECAAD80gKuYEVHR/tdv3Tpko4dO6avv/5aDz30kE1TAQAAVF3AFayf%2B12ECxcurNanqQMAANgl4H6K8OcMHDhQb775pt1jAAAAXJNjCtZnn31m5INGAQAAbraAO0V4tTe5nz17Vl999dXP/gJnAACAQBJwBatp06Z%2Bv4dQkn71q19pxIgRuvfee22aCgAAoOoCrmD9%2Bc9/tnsEAACAGxJwBWvPnj1Vvu2dd955EycBAAC4PgFXsH7/%2B9/7ThFaluVbv3LN4/Hoyy%2B//OUHBAAAuIaAK1hLlixRWlqannnmGSUmJiokJESff/65pk%2Bfrvvuu0/9%2B/e3e0QAAIB/KuA%2BpiE9PV1Tp05V3759Vb9%2BfdWpU0ddunTR9OnTtW7dOkVHR/suAAAAgSjgClZBQcFVy1PdunVVVFRkw0QAAADVE3AFq1OnTpozZ47Onj3rWysuLlZGRobuuusuGycDAAComoB7D9azzz6rkSNHqkePHmrZsqUsy9I333yjyMhI/fWvf7V7PAAAgGsKuILVunVrvfHGG9q%2Bfbvy8vIkSb/73e90zz33KCwszObpAAAAri3gCpYk3XLLLRo2bJiOHz%2Bu5s2bS/rx09wBAACcIODeg2VZll588UXdeeedGjBggE6dOqUJEyZoypQpunjxot3jAQAAXFPAFazVq1dr69atev755xUSEiJJ6t27t9555x1lZmbaPB0AAMC1BVzBysrK0tSpUzV48GDfp7f3799faWlp2rZtm83TAQAAXFvAFazjx4%2BrXbt2ldbbtm0rr9drw0QAAADVE3AFKzo6Wp9//nml9ffff9/3hncAAIBAFnA/Rfjoo49q2rRp8nq9sixLH3/8sbKysrR69WpNnDjR7vEAAACuKeAK1pAhQ3Tp0iUtXrxYFy5c0NSpU9WgQQONHTtWDzzwgN3jAQAAXFPAFazt27crOTlZw4cP13fffSfLstSwYUO7xwIAAKiygHsP1vTp031vZm/QoAHlCgAAOE7AFayWLVvq66%2B/tnsMAACA6xZwpwjbtm2rP/3pT1q%2BfLlatmyp0NBQv%2B3p6ek2TQYAAFA1AVewjh49qvj4eEnic68AAIAjBUTBmjVrlsaMGaPw8HCtXr3a7nEAAABuSEC8B2vFihUqKSnxWxs9erQKCgpsmggAAOD6BUTBsiyr0tqePXtUWlpqwzQAAAA3JiAKFgAAgJtQsAAAAAwLmILl8XjsHgEAAMCIgPgpQklKS0vz%2B8yrixcvKiMjQ3Xq1PG7HZ%2BDBQAAAl1AFKw777yz0mdexcbGqqioSEVFRTZNBQAAcH0ComDx2VcAAMBNAuY9WAAAAG5BwQIAADAsIE4RAkAgS5jylt0jVMubY7vaPQJQ43EECwAAwDAKFgAAgGGuL1hlZWUaMGCAdu/e7VvLz8/XqFGj1KlTJ/Xv3187d%2B70%2B5qPPvpIAwYMUExMjEaOHKn8/PxfemwAAOBgri5YpaWlevrpp5Wbm%2BtbsyxLKSkpatSokTZt2qSBAwdqzJgxOnHihCTpxIkTSklJ0eDBg7Vx40Y1aNBATz755FV/ITUAAMDVuLZgHT58WPfff7%2B%2B/fZbv/Vdu3YpPz9f06dPV%2BvWrfXEE0%2BoU6dO2rRpkyRpw4YNuuOOO/TII4/otttuU3p6uv7nf/5Hn3zyiR0xAACAA7m2YH3yySfq3LmzsrKy/NZzcnLUvn17hYeH%2B9bi4%2BO1b98%2B3/aEhATftrCwMHXo0MG3HQAA4Fpc%2BzENDz744FXXvV6voqKi/NYaNmyoU6dOVWk7AADAtbi2YP2ckpIShYSE%2BK2FhISorKysSturoqCgoNLvVgwODq9U3G5EUFAtvz/dxM3ZgF9CcPC1Xztufp2RzXncmKvGFazQ0FAVFxf7rZWVlal27dq%2B7VeWqbKyMkVERFT5PrKyspSZmem3lpKSotTU1Ouc%2BudFRIQZ32egcHM24GaqX79OlW/r5tcZ2ZzHTblqXMFq3LixDh8%2B7LdWWFjoO7rUuHFjFRYWVtrerl27Kt/H8OHDlZSU5LcWHByuoqJz1zl1ZUFBtRQREaYzZ0pUXl5hbL%2BBwM3ZgF9CVb7XuPl1RjbnuZm5qvMfDpNqXMGKiYnRsmXLdOHCBd9Rq%2BzsbMXHx/u2Z2dn%2B25fUlKigwcPasyYMVW%2Bj6ioqEqnA73eH3TpkvkXQ3l5xU3ZbyBwczbgZqrO68bNrzOyOY%2BbcrnnZGcVJSYmqkmTJpo0aZJyc3O1bNky7d%2B/X0OHDpUkDRkyRHv37tWyZcuUm5urSZMmqVmzZurcubPNkwMAAKeocQUrKChIixYtktfr1eDBg/X6669r4cKFatq0qSSpWbNmeumll7Rp0yYNHTpUxcXFWrhwoTwej82TAwAAp6gRpwi/%2Buorv%2BstWrTQmjVrfvb2d999t%2B6%2B%2B%2B6bPRYAAHCpGncECwAA4GajYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAwLtnsA3Jg%2BL35g9whV9ubYrnaPAADAL4IjWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhfA4WALhMv3kf2j1ClfH5eHArjmABAAAYRsECAAAwjFOE%2BMU46bQFAAA3giNYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGE1tmC9/fbbuv322/0uqampkqSDBw9q2LBhiomJ0ZAhQ3TgwAGbpwUAAE5SYwvW4cOH1atXL%2B3cudN3SUtL0/nz5zV69GglJCRo8%2BbNio2N1RNPPKHz58/bPTIAAHCIGluw8vLy1KZNG0VGRvouEREReuONNxQaGqrx48erdevWmjJliurUqaO33nrL7pEBAIBD1OiC1bJly0rrOTk5io%2BPl8fjkSR5PB7FxcVp3759v/CEAADAqWrk7yK0LEtHjx7Vzp07tXTpUpWXlys5OVmpqanyer269dZb/W7fsGFD5ebmVnn/BQUF8nq9fmvBweGKiooyMr8kBQXV2G4MwEWCg81/L7v8/dGN3yfdms2NuWpkwTpx4oRKSkoUEhKiefPm6fjx40pLS9OFCxd86z8VEhKisrKyKu8/KytLmZmZfmspKSm%2BN9EDAH5Uv36dm7bviIiwm7Zvu7k1m5ty1ciCFR0drd27d%2BuWW26Rx%2BNRu3btVFFRoWeeeUaJiYmVylRZWZlq165d5f0PHz5cSUlJfmvBweEqKjpnZH7px5bvpicigJrJ5PfFyy5/fzxzpkTl5RXG928nt2a7mbluZon/Z2pkwZKkevXq%2BV1v3bq1SktLFRkZqcLCQr9thYWF1Tq9FxUVVen2Xu8PunTJPS8GADDhZn5fLC%2BvcO33Xbdmc1Mu95zsrIYPPvhAnTt3VklJiW/tyy%2B/VL169RQfH6/PPvtMlmVJ%2BvH9Wnv37lVMTIxd4wIAAIepkQUrNjZWoaGhevbZZ3XkyBHt2LFDs2bN0mOPPabk5GSdOXNGM2fO1OHDhzVz5kyVlJSoX79%2Bdo8NAAAcokYWrLp16%2BqVV17Rd999pyFDhmjKlCkaPny4HnvsMdWtW1dLly5Vdna2Bg8erJycHC1btkzh4eF2jw0AAByixr4H67bbbtOKFSuuuu23v/2tXnvttV94IgAA4BY18ggWAADAzUTBAgAAMIyCBQAAYFiNfQ8WAMB%2B/eZ9aPcI1fLm2K52jwCH4AgWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMOC7R4AAACn6DfvQ7tHqJY3x3a1e4QaiyNYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgF6ypKS0s1efJkJSQkqFu3bnr11VftHgkAADgIH9NwFbNmzdKBAwe0atUqnThxQhMmTFDTpk2VnJxs92gAAMABKFhXOH/%2BvDZs2KCXX35ZHTp0UIcOHZSbm6u1a9dSsAAAjuKkz%2B36dKa7/o3lFOEVDh06pEuXLik2Nta3Fh8fr5ycHFVUVNg4GQAAcAqOYF3B6/Wqfv36CgkRIy9tAAALl0lEQVQJ8a01atRIpaWlKi4uVoMGDa65j4KCAnm9Xr%2B14OBwRUVFGZszKIhuDABwFzf920bBukJJSYlfuZLku15WVlalfWRlZSkzM9NvbcyYMXrqqafMDKkfS9yqVcv1xh%2BHGy1ugaCgoEBZWVkaPtxd2dyaSyKbE7k1l0Q2JyooKNBLL73kqlzuqYqGhIaGVipSl6/Xrl27SvsYPny4Nm/e7HcZPny40Tm9Xq8yMzMrHSlzA7dmc2suiWxO5NZcEtmcyI25OIJ1hcaNG6uoqEiXLl1ScPCPfz1er1e1a9dWRERElfYRFRXlmgYOAACqjyNYV2jXrp2Cg4O1b98%2B31p2drY6duyoWrX46wIAANdGY7hCWFiYBg0apBdeeEH79%2B/XO%2B%2B8o1dffVUjR460ezQAAOAQQS%2B88MILdg8RaLp06aKDBw9q9uzZ%2Bvjjj/WHP/xBQ4YMsXusSurUqaPExETVqVPH7lGMc2s2t%2BaSyOZEbs0lkc2J3JbLY1mWZfcQAAAAbsIpQgAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwHKi0t1eTJk5WQkKBu3brp1VdftXukG1JWVqYBAwZo9%2B7dvrX8/HyNGjVKnTp1Uv/%2B/bVz504bJ6y%2B06dPKzU1VYmJierevbvS09NVWloqyfnZjh07pkcffVSxsbHq2bOnli9f7tvm9GyXjR49WhMnTvRdP3jwoIYNG6aYmBgNGTJEBw4csHG66nv77bd1%2B%2B23%2B11SU1MlOT9bWVmZpk2bpjvvvFP/8i//ojlz5ujy52c7NdvmzZsrPV6333672rZtK8m5uS47efKknnjiCcXFxSkpKUkrV670bXN6tp%2BiYDnQrFmzdODAAa1atUrPP/%2B8MjMz9dZbb9k91nUpLS3V008/rdzcXN%2BaZVlKSUlRo0aNtGnTJg0cOFBjxozRiRMnbJy06izLUmpqqkpKSrR27VrNnTtX7733nubNm%2Bf4bBUVFRo9erTq16%2Bv1157TdOmTdPixYu1bds2x2e77O9//7t27Njhu37%2B/HmNHj1aCQkJ2rx5s2JjY/XEE0/o/PnzNk5ZPYcPH1avXr20c%2BdO3yUtLc0V2dLS0vTRRx/plVde0ezZs/Vf//VfysrKcnS2y/85uXz5xz/%2BoRYtWmjkyJGOznXZ2LFjFR4ers2bN2vy5MmaN2%2Be3n77bVdk82PBUc6dO2d17NjR2rVrl29t4cKF1ogRI2yc6vrk5uZa9957r/Xv//7vVps2bXyZPvroI6tTp07WuXPnfLd96KGHrAULFtg1arUcPnzYatOmjeX1en1r27Zts7p16%2Bb4bKdPn7b%2B%2BMc/Wj/88INvLSUlxXr%2B%2Becdn82yLKuoqMjq0aOHNWTIEGvChAmWZVnWhg0brKSkJKuiosKyLMuqqKiw%2BvTpY23atMnOUatl3Lhx1uzZsyutOz1bUVGR1b59e2v37t2%2BtaVLl1oTJ050fLafWrJkidW7d2%2BrtLTU8bmKi4utNm3aWF999ZVvbcyYMda0adMcn%2B1KHMFymEOHDunSpUuKjY31rcXHxysnJ0cVFRU2TlZ9n3zyiTp37qysrCy/9ZycHLVv317h4eG%2Btfj4eO3bt%2B%2BXHvG6REZGavny5WrUqJHf%2BtmzZx2fLSoqSvPmzVPdunVlWZays7O1Z88eJSYmOj6bJP3lL3/RwIEDdeutt/rWcnJyFB8fL4/HI0nyeDyKi4tzVK68vDy1bNmy0rrTs2VnZ6tu3bpKTEz0rY0ePVrp6emOz3ZZcXGxXn75ZY0bN04hISGOz1W7dm2FhYVp8%2BbNunjxoo4cOaK9e/eqXbt2js92JQqWw3i9XtWvX18hISG%2BtUaNGqm0tFTFxcU2TlZ9Dz74oCZPnqywsDC/da/Xq6ioKL%2B1hg0b6tSpU7/keNctIiJC3bt3912vqKjQmjVr1KVLF8dn%2B6mkpCQ9%2BOCDio2NVd%2B%2BfR2f7eOPP9ann36qJ5980m/d6bksy9LRo0e1c%2BdO9e3bV71799aLL76osrIyx2fLz89XdHS0tmzZouTkZP3rv/6rFi5cqIqKCsdnu2zdunWKiopScnKyJOc/H0NDQzV16lRlZWUpJiZG/fr1U48ePTRs2DDHZ7tSsN0DoHpKSkr8ypUk3/WysjI7RjLu5zI6NV9GRoYOHjyojRs3auXKla7JtmDBAhUWFuqFF15Qenq6ox%2B30tJSPf/885o6dapq167tt83JuSTpxIkTvgzz5s3T8ePHlZaWpgsXLjg%2B2/nz53Xs2DGtX79e6enp8nq9mjp1qsLCwhyfTfqxHG/YsEGPPfaYb80NufLy8tSrVy89/PDDys3N1YwZM3TXXXe5IttPUbAcJjQ0tNKT7fL1K/9hcKrQ0NBKR%2BPKysocmS8jI0OrVq3S3Llz1aZNG1dl69ixo6Qfy8mf/vQnDRkyRCUlJX63cUq2zMxM3XHHHX5HHi/7udecE3JJUnR0tHbv3q1bbrlFHo9H7dq1U0VFhZ555hklJiY6OltwcLDOnj2r2bNnKzo6WtKPhXLdunVq0aKFo7NJ0ueff67Tp0/rnnvu8a05/fn48ccfa%2BPGjdqxY4dq166tjh076vTp01q8eLGaN2/u6GxX4hShwzRu3FhFRUW6dOmSb83r9ap27dqKiIiwcTJzGjdurMLCQr%2B1wsLCSoeOA92MGTO0YsUKZWRkqG/fvpKcn62wsFDvvPOO39qtt96qixcvKjIy0rHZ/v73v%2Budd95RbGysYmNjtW3bNm3btk2xsbGOf8wkqV69er73tUhS69atVVpa6ujHTPrx/Y6hoaG%2BciVJv/nNb3Ty5ElXPG4ffPCBEhISdMstt/jWnJ7rwIEDatGihV9pat%2B%2BvU6cOOH4bFeiYDlMu3btFBwc7Pemv%2BzsbHXs2FG1arnj4YyJidEXX3yhCxcu%2BNays7MVExNj41TVk5mZqfXr12vOnDl%2B//t0erbjx49rzJgxOn36tG/twIEDatCggeLj4x2bbfXq1dq2bZu2bNmiLVu2KCkpSUlJSdqyZYtiYmL02Wef%2BT5bybIs7d271xG5pB//ke7cubPf0cUvv/xS9erVU3x8vKOzxcTEqLS0VEePHvWtHTlyRNHR0Y5/3CRp//79iouL81tzeq6oqCgdO3bM70jVkSNH1KxZM8dnu5I7/kWuQcLCwjRo0CC98MIL2r9/v9555x29%2BuqrGjlypN2jGZOYmKgmTZpo0qRJys3N1bJly7R//34NHTrU7tGqJC8vT4sWLdLjjz%2Bu%2BPh4eb1e38Xp2Tp27KgOHTpo8uTJOnz4sHbs2KGMjAz94Q9/cHS26OhotWjRwnepU6eO6tSpoxYtWig5OVlnzpzRzJkzdfjwYc2cOVMlJSXq16%2Bf3WNXSWxsrEJDQ/Xss8/qyJEj2rFjh2bNmqXHHnvM8dlatWqlnj17atKkSTp06JA%2B%2BOADLVu2TA888IDjs0lSbm6u30%2B0SnJ8rqSkJP3qV7/Ss88%2Bq6NHj%2Bq///u/tWTJEv3%2B9793fLZKbPlwCNyQ8%2BfPW%2BPHj7c6depkdevWzVqxYoXdI92wn34OlmVZ1jfffGP97ne/s%2B644w7rnnvusT788EMbp6uepUuXWm3atLnqxbKcnc2yLOvUqVNWSkqKFRcXZ3Xt2tVavHix73NrnJ7tsgkTJvg%2BB8uyLCsnJ8caNGiQ1bFjR2vo0KHWF198YeN01ff1119bo0aNsjp16mR17drVeumll3yPmdOznTlzxnrmmWesTp06WXfddZersnXs2NF6//33K607PVdubq41atQoKy4uzurdu7e1YsUK1zxmP%2BWxrP87FgcAAAAjOEUIAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIb9P9ntYDt5%2B%2BXZAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common9163199437125564854\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">24.0</td>\n",
       "        <td class=\"number\">47</td>\n",
       "        <td class=\"number\">3.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">22.0</td>\n",
       "        <td class=\"number\">43</td>\n",
       "        <td class=\"number\">3.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">21.0</td>\n",
       "        <td class=\"number\">41</td>\n",
       "        <td class=\"number\">3.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">30.0</td>\n",
       "        <td class=\"number\">40</td>\n",
       "        <td class=\"number\">3.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">18.0</td>\n",
       "        <td class=\"number\">39</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">25.0</td>\n",
       "        <td class=\"number\">34</td>\n",
       "        <td class=\"number\">2.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">28.0</td>\n",
       "        <td class=\"number\">32</td>\n",
       "        <td class=\"number\">2.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">36.0</td>\n",
       "        <td class=\"number\">31</td>\n",
       "        <td class=\"number\">2.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">27.0</td>\n",
       "        <td class=\"number\">30</td>\n",
       "        <td class=\"number\">2.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">29.0</td>\n",
       "        <td class=\"number\">30</td>\n",
       "        <td class=\"number\">2.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (88)</td>\n",
       "        <td class=\"number\">679</td>\n",
       "        <td class=\"number\">51.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"missing\">\n",
       "        <td class=\"fillremaining\">(Missing)</td>\n",
       "        <td class=\"number\">263</td>\n",
       "        <td class=\"number\">20.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:39%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme9163199437125564854\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.1667</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.3333</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.4167</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.6667</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.75</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">70.5</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">71.0</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">74.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">76.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">80.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_sibsp\">sibsp<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>7</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.5%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.49885</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>8</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>68.1%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-4130452008163274622\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAAARxJREFUeJzt3MEJAjEUQEEVS7IIe/JsTxZhT/Eu8nADuovO3AP/8hICIfsxxtgBLx3WHgC27Lj2AM9Ol9viNffr%2BQOTgBMEkkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBMLmfnef4Ud4PsUJAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEH7iufsMT%2BR5x98GMmNpVDNBzYS71LdC/4VNaD/GGGsPAVvlDgJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBALhAdogGpWY44YEAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-4130452008163274622,#minihistogram-4130452008163274622\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-4130452008163274622\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-4130452008163274622\"\n",
       "                                                  aria-controls=\"quantiles-4130452008163274622\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-4130452008163274622\" aria-controls=\"histogram-4130452008163274622\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-4130452008163274622\" aria-controls=\"common-4130452008163274622\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-4130452008163274622\" aria-controls=\"extreme-4130452008163274622\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-4130452008163274622\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>8</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>8</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>1.0417</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>2.0881</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>20.043</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>0.49885</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>0.67911</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>3.8442</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>653</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>1.0851</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>10.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-4130452008163274622\">\n",
       "            <img 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K8vCyQAAANrH6wLrk08%2B0RNPPKHIyEjPsYiICM2ePVt///vfLZwMAACgfbwusBwOh%2Brq6tocb2ho4B3dAQCAT/C6wBo1apSWLFmi//7v//YcO3r0qLKyspSYmGjhZAAAAO3jdT9FOGfOHN1///0aN26cwsPDJUl1dXUaNGiQ5s6da/F0AAAAl%2BZ1gdW9e3dt27ZNH374oSoqKuRwONS/f3/deuut8vPzs3o8AACAS/K6wJIkf39/JSYm8pQgAADwSV4XWC6XSytXrtTevXt17ty5Ni9sf%2BeddyyaDAAAoH28LrD%2B7d/%2BTeXl5br99tt19dVXWz0OAABAh3ldYP3973/XunXrlJCQYPUoAAAAl8Xr3qYhJCRE3bt3t3oMAACAy%2BZ1gTVx4kStW7dO58%2Bft3oUAACAy%2BJ1TxHW1tZq586d%2Bs///E/16dNHAQEBrc6//PLLFk0GAADQPl4XWJI0YcIEq0cAAAC4bF4XWFlZWVaPAAAA0Cle9xosSaqurlZubq5mzZql//mf/9Hu3btVWVlp9VgAAADt4nWBdeTIEf3qV7/Stm3b9Oabb6q%2Bvl67du3S5MmTVVZWZvV4AAAAl%2BR1gfX0009r7Nixevvtt3XVVVdJklasWKGkpCQtW7bM4ukAAAAuzesCa%2B/evbr//vtb/cPODodDjzzyiPbv32/hZAAAAO3jdYHV0tKilpaWNse//vpr%2Bfv7WzARAABAx3hdYI0cOVJr1qxpFVm1tbXKycnR8OHDLZwMAACgfbwusJ544gmVl5dr5MiRamxs1MMPP6wxY8boyy%2B/1Jw5c6weDwAA4JK87n2wevbsqaKiIu3cuVOff/65WlpalJqaqokTJyosLMzq8QAAAC7J6wJLkoKDgzVlyhSrxwAAALgsXhdY06dP/97z/FuEAADA23ldYEVHR7f6uLm5WUeOHNEXX3yh%2B%2B67z6KpAAAA2s/rAuu7/i3CvLw8HT9%2B/AeeBgAAoOO87qcIv8vEiRP1xhtvWD0GAADAJflMYH366ae80SgAAPAJXvcU4cVe5H7mzBn985//1N13323BRAAAAB3jdYHVu3fvVv8OoSRdddVVuvfee/XrX//aoqkAAADaz%2BsC6%2Bmnn7Z6BAAAgE7xusD6%2BOOP233boUOHXsFJAAAALo/XBda0adM8TxG63W7P8W8f8/Pz0%2Beff/7DDwgAAHAJXhdYq1ev1pIlS/SHP/xBw4YNU0BAgD777DMtWrRId9xxh2677TarRwQAAPheXvc2DVlZWVqwYIHGjRunbt26KTQ0VMOHD9eiRYu0adMmRUdHe34BAAB4I68LrOrq6ovGU1hYmE6dOmXBRAAAAB3jdYF18803a8WKFTpz5oznWG1trXJycnTrrbdaOBkAAED7eN1rsObPn6/p06dr1KhRiomJkdvt1n/9138pMjJSL7/8stXjAQAAXJLXBVa/fv20a9cu7dy5U4cOHZIk3XPPPbr99tsVHBxs8XQAAACX5nWBJUldu3bVlClT9OWXX6pPnz6Svnk3dwAAAF/gda/BcrvdWrZsmYYOHaoJEybo%2BPHjmjNnjjIzM3Xu3DmrxwMAALgkrwusDRs2aPv27XryyScVEBAgSRo7dqzefvtt5ebmdvj%2B0tLS9MQTT3g%2B3r9/v6ZMmSKn06nJkyervLy81e137typsWPHyul0Kj09XSdPnuzcQgAA4EfH6wKroKBACxYsUEpKiufd22%2B77TYtWbJEO3bs6NB9vf7669qzZ4/n4/r6eqWlpSkhIUGFhYWKjY3VjBkzVF9fL0nat2%2BfMjMzNXPmTBUUFKiurk5z5841txwAAPhR8LrA%2BvLLLzVw4MA2xwcMGCCXy9Xu%2B6mtrVV2drYGDx7sObZr1y4FBgZq9uzZ6tevnzIzMxUaGqrdu3dLkjZu3Kjx48dr0qRJGjBggLKzs7Vnzx4dPXq084sBAIAfDa8LrOjoaH322Wdtjr/77rueF7y3xzPPPKOJEyeqf//%2BnmNlZWWKj4/3XBnz8/NTXFycSktLPecTEhI8t%2B/Vq5d69%2B6tsrKyy10HAAD8CHndTxE%2B%2BOCDeuqpp%2BRyueR2u1VcXKyCggJt2LCh1Wupvk9xcbE%2B%2BeQT7dixQwsXLvQcd7lcrYJLkrp3766KigpJ37yLfFRUVJvzx48f79AO1dXVba62ORwhbe67s/z9va6Pv5fD0b55L%2Bzla/u1h113s%2BteErv5IrvuJdl3Nzvu5XWBNXnyZDU3N%2Bv555/X2bNntWDBAkVEROixxx5TamrqJT%2B/sbFRTz75pBYsWKCgoKBW5xoaGjwvnL8gICBATU1NkqSzZ89%2B7/n2KigoaPOC/PT0dGVkZHTofuymW7fQDt0%2BPNy%2B73tm193supfEbr7IrntJ9t3NTnt5XWDt3LlTycnJmjp1qk6ePCm3263u3bu3%2B/Nzc3N10003KTExsc25wMDANrHU1NTkCbHvOt/RNzidOnWqkpKSWh1zOEJ06tTXHbqfS/G10m/v/v7%2BXRQeHqy6ugadP99yhaf6Ydl1N7vuJbGbL7LrXpJ9d7uSe3X0m3tTvC6wFi1apH//939X165dFRER0eHPf/3111VTU6PY2FhJ8gTTm2%2B%2BqQkTJqimpqbV7WtqajxP3fXs2fOi5yMjIzs0Q1RUVJunA12ur9TcbJ8/DJejo/ufP99i298zu%2B5m170kdvNFdt1Lsu9udtrL6y6BxMTE6Isvvrjsz9%2BwYYN27NihoqIiFRUVKSkpSUlJSSoqKpLT6dSnn34qt9st6Zs3Nd27d6%2BcTqckyel0qqSkxHNfVVVVqqqq8pwHAABoD6%2B7gjVgwAA9/vjjWrdunWJiYhQYGNjqfFZW1vd%2BfnR0dKuPQ0O/uTTYt29fde/eXcuXL9fSpUt11113afPmzWpoaND48eMlSampqZo2bZpuvvlmDR48WEuXLtXo0aM79NOLAAAAXhdYhw8fVnx8vCR16H2v2iMsLExr1qzRk08%2Bqb/85S%2B64YYblJ%2Bfr5CQEElSbGysFi1apOeee06nT5/WiBEjtHjxYqMzAAAA%2B/NzX3i%2BzELZ2dmaOXOmJ3TsyOX6yvh9Ohxd9Itl7xm/3yvljcdGtOt2DkcXdesWqlOnvrbNc/EX2HU3u%2B4lsZsvsutekn13u5J7RUZebfT%2B2ssrXoP14osvqqGhodWxtLQ0VVdXWzQRAADA5fOKwLrYRbSPP/5YjY2NFkwDAADQOV4RWAAAAHZCYAEAABjmNYF14R9gBgAA8HVe8zYNS5YsafWeV%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%2Bo5tvvlm33Xab3n///Vaf%2B%2BGHH2rChAlyOp2aPn26jh49asUKAADAR9kysNxutzIyMtTQ0KBXXnlFzz77rP72t79p5cqVcrvdSk9PV48ePbR161ZNnDhRM2fO1LFjxyRJx44dU3p6ulJSUvTqq68qIiJCjzzyiNxut8VbAQAAX%2BGweoArobKyUqWlpfrggw/Uo0cPSVJGRoaeeeYZjRo1SkePHtXmzZsVEhKifv36qbi4WFu3btWjjz6qLVu26KabbtIDDzwgScrKytKIESP0j3/8Q7fccouVawEAAB9hyytYkZGRWrdunSeuLjhz5ozKysp04403KiQkxHM8Pj5epaWlkqSysjIlJCR4zgUHB2vQoEGe8wAAAJdiy8AKDw9XYmKi5%2BOWlhZt3LhRw4cPl8vlUlRUVKvbd%2B/eXcePH5ekS54HAAC4FFs%2BRfhtOTk52r9/v1599VW99NJLCggIaHU%2BICBATU1NkqSGhobvPd8e1dXVcrlcrY45HCFtwq2z/P1t2cdew%2BEw//t74TGz22Nn170kdvNFdt1Lsu9udtzL9oGVk5Oj9evX69lnn9X111%2BvwMBA1dbWtrpNU1OTgoKCJEmBgYFtYqqpqUnh4eHt/poFBQXKzc1tdSw9PV0ZGRmXuQWs0K1b6BW77/Dw4Ct231ay614Su/kiu%2B4l2Xc3O%2B1l68BavHixNm3apJycHI0bN06S1LNnTx08eLDV7WpqajxXl3r27Kmampo25wcOHNjurzt16lQlJSW1OuZwhOjUqa8vZ43vZKfS90amHy/pm8csPDxYdXUNOn%2B%2Bxfj9W8Wue0ns5ovsupdk392u5F5X8pvl72PbwMrNzdXmzZu1YsUKJScne447nU7l5%2Bfr7NmznqtWJSUlio%2BP95wvKSnx3L6hoUH79%2B/XzJkz2/21o6Ki2jwd6HJ9peZm%2B/xh%2BDG4ko/X%2BfMttvzvwa57Sezmi%2By6l2Tf3ey0ly0vgRw6dEirVq3Sb3/7W8XHx8vlcnl%2BDRs2TL169dLcuXNVUVGh/Px87du3T3feeackafLkydq7d6/y8/NVUVGhuXPn6pprruEtGgAAQLvZMrDeeecdnT9/Xs8//7xGjhzZ6pe/v79WrVoll8ullJQUvfbaa8rLy1Pv3r0lSddcc43%2B9Kc/aevWrbrzzjtVW1urvLw8%2Bfn5WbwVAADwFbZ8ijAtLU1paWnfeb5v377auHHjd57/2c9%2Bpp/97GdXYjQAAPAjYMsrWAAAAFYisAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAxzWD0A4K3Gr/zA6hE65I3HRlg9AgDgf3EFCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDB%2BihAALiEhc7fVI3QIP1EKWI/AAmzCl95WggAAYHc8RQgAAGAYgXURjY2NmjdvnhISEjRy5Ei98MILVo8EAAB8CE8RXkR2drbKy8u1fv16HTt2THPmzFHv3r2VnJxs9WgAAMAHEFjfUl9fry1btmjt2rUaNGiQBg0apIqKCr3yyisEFgDAp/jSazM/WWqvv2MJrG85cOCAmpubFRsb6zkWHx%2Bv1atXq6WlRV268KwqAO/mS3%2Bp8gMPsCsC61tcLpe6deumgIAAz7EePXqosbFRtbW1ioiIuOR9VFdXy%2BVytTrmcIQoKirK6Kz%2B/sQefJPD4Tv/7fLn7Mq6Ev8tXHjM7PjY2Xk3yV57EVjf0tDQ0CquJHk%2Bbmpqatd9FBQUKDc3t9WxmTNn6tFHHzUz5P%2Bqrq7Wff%2BvQlOnTjUeb1aqrq5WQUGB7faS7LubXfeS7PvnTLLv41ZdXa3169fZbi%2Bp47v5ytNu1dXV%2BtOf/mSrx8w%2BqWhIYGBgm5C68HFQUFC77mPq1KkqLCxs9Wvq1KnGZ3W5XMrNzW1ztczX2XUvyb672XUvid18kV33kuy7mx334grWt/Ts2VOnTp1Sc3OzHI5vfntcLpeCgoIUHh7ervuIioqyTYEDAICO4wrWtwwcOFAOh0OlpaWeYyUlJRo8eDAvcAcAAO1CMXxLcHCwJk2apIULF2rfvn16%2B%2B239cILL2j69OlWjwYAAHyE/8KFCxdaPYS3GT58uPbv36/ly5eruLhYDz30kCZPnmz1WBcVGhqqYcOGKTQ01OpRjLLrXpKAnXufAAAIxUlEQVR9d7PrXhK7%2BSK77iXZdze77eXndrvdVg8BAABgJzxFCAAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiBBQAAYBiB5aMaGxs1b948JSQkaOTIkXrhhResHsmopqYmTZgwQR999JHVoxhx4sQJZWRkaNiwYUpMTFRWVpYaGxutHsuII0eO6MEHH1RsbKxGjx6tdevWWT2ScWlpaXriiSesHsOYt956SzfccEOrXxkZGVaPZURTU5OeeuopDR06VD/96U%2B1YsUK%2Bfr7aRcWFrZ5vG644QYNGDDA6tGMqKqq0owZMxQXF6ekpCS99NJLVo9khMPqAXB5srOzVV5ervXr1%2BvYsWOaM2eOevfureTkZKtH67TGxkbNmjVLFRUVVo9ihNvtVkZGhsLDw/XKK6/o9OnTmjdvnrp06aI5c%2BZYPV6ntLS0KC0tTYMHD9a2bdt05MgR/f73v1fPnj31q1/9yurxjHj99de1Z88e3XHHHVaPYszBgwc1ZswYLV682HMsMDDQwonMWbJkiT766CP9%2Bc9/1tdff63f/e536t27t%2B666y6rR7tst912mxITEz0fNzc367777tPo0aOtG8qgxx57TL1791ZhYaEOHjyoxx9/XNHR0frFL35h9WidQmD5oPr6em3ZskVr167VoEGDNGjQIFVUVOiVV17x%2BcA6ePCgZs2a5fPfcf5flZWVKi0t1QcffKAePXpIkjIyMvTMM8/4fGDV1NRo4MCBWrhwocLCwhQTE6Nbb71VJSUltgis2tpaZWdna/DgwVaPYtShQ4d0/fXXKzIy0upRjKqtrdXWrVv14osvasiQIZKkBx54QGVlZT4dWEFBQQoKCvJ8vGbNGrndbj3%2B%2BOMWTmXG6dOnVVpaqsWLFysmJkYxMTFKTExUcXGxzwcWTxH6oAMHDqi5uVmxsbGeY/Hx8SorK1NLS4uFk3XeP/7xD91yyy0qKCiwehRjIiMjtW7dOk9cXXDmzBmLJjInKipKK1euVFhYmNxut0pKSvTxxx9r2LBhVo9mxDPPPKOJEyeqf//%2BVo9i1KFDhxQTE2P1GMaVlJQoLCys1X9/aWlpysrKsnAqs2pra7V27VrNmjVLAQEBVo/TaUFBQQoODlZhYaHOnTunyspK7d27VwMHDrR6tE4jsHyQy%2BVSt27dWv3h6tGjhxobG1VbW2vhZJ139913a968eQoODrZ6FGPCw8NbXd5vaWnRxo0bNXz4cAunMi8pKUl33323YmNjNW7cOKvH6bTi4mJ98skneuSRR6wexSi3263Dhw/r/fff17hx4zR27FgtW7ZMTU1NVo/WaUePHlV0dLSKioqUnJysn//858rLy/P5bzz/r02bNikqKsrnn624IDAwUAsWLFBBQYGcTqfGjx%2BvUaNGacqUKVaP1mkElg9qaGho853LhY/t8D9Ju8vJydH%2B/fv1u9/9zupRjHruuee0evVqff755z5/xaCxsVFPPvmkFixY0OqpGTs4duyY5/8hK1eu1Jw5c7Rjxw5lZ2dbPVqn1dfX68iRI9q8ebOysrI0Z84cbdiwwTYvmna73dqyZYvuvfdeq0cx6tChQxozZowKCgqUlZWl3bt367XXXrN6rE7jNVg%2BKDAwsE1IXfjYbn8Z2E1OTo7Wr1%2BvZ599Vtdff73V4xh14XVKjY2NevzxxzV79myffQojNzdXN910U6srj3YRHR2tjz76SF27dpWfn58GDhyolpYW/eEPf9DcuXPl7%2B9v9YiXzeFw6MyZM1q%2BfLmio6MlfROUmzZt0gMPPGDxdJ332Wef6cSJE7r99tutHsWY4uJivfrqq9qzZ4%2BCgoI0ePBgnThxQs8//7x%2B/etfWz1epxBYPqhnz546deqUmpub5XB88xC6XC4FBQUpPDzc4unwXRYvXqxNmzYpJyfHFk%2BhSd%2B8yL20tFRjx471HOvfv7/OnTunM2fOKCIiwsLpLt/rr7%2Bumpoaz%2BscL3wD8%2Babb%2BrTTz%2B1cjQjfvKTn7T6uF%2B/fmpsbNTp06d99jGTvnm9Y2BgoCeuJOnaa69VVVWVhVOZ89577ykhIUFdu3a1ehRjysvL1bdv31YXB2688UatXr3awqnM4ClCHzRw4EA5HA6VlpZ6jpWUlGjw4MHq0oWH1Bvl5uZq8%2BbNWrFiha2%2B%2B/zyyy81c%2BZMnThxwnOsvLxcERERPv0X9YYNG7Rjxw4VFRWpqKhISUlJSkpKUlFRkdWjddp7772nW265RQ0NDZ5jn3/%2BuX7yk5/49GMmSU6nU42NjTp8%2BLDnWGVlZavg8mX79u1TXFyc1WMYFRUVpSNHjrR6VqayslLXXHONhVOZwd/GPig4OFiTJk3SwoULtW/fPr399tt64YUXNH36dKtHw0UcOnRIq1at0m9/%2B1vFx8fL5XJ5fvm6wYMHa9CgQZo3b54OHjyoPXv2KCcnRw899JDVo3VKdHS0%2Bvbt6/kVGhqq0NBQ9e3b1%2BrROi02NlaBgYGaP3%2B%2BKisrtWfPHmVnZ%2Btf//VfrR6t06677jqNHj1ac%2BfO1YEDB/Tee%2B8pPz9fqampVo9mREVFhe1%2BojUpKUlXXXWV5s%2Bfr8OHD%2Buvf/2rVq9erWnTplk9Wqf5ue30hkM/Ig0NDVq4cKH%2B4z/%2BQ2FhYXrwwQf1m9/8xuqxjLrhhhv08ssv65ZbbrF6lE7Jz8/X8uXLL3run//85w88jXknTpzQ4sWLVVxcrODgYN17772aMWOG/Pz8rB7NmAvv4v70009bPIkZFRUV%2BuMf/6jS0lKFhobqrrvuUnp6ui0es6%2B%2B%2BkqLFy/WW2%2B9peDgYN1999222W3IkCHKy8uz3WsDDx48qKVLl2rfvn2KiIjQPffco/vuu8/nHzMCCwAAwDCeIgQAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADDs/wPjb6EJB1P56QAAAABJRU5ErkJggg%3D%3D\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-4130452008163274622\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">891</td>\n",
       "        <td class=\"number\">68.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">319</td>\n",
       "        <td class=\"number\">24.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:36%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">42</td>\n",
       "        <td class=\"number\">3.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">22</td>\n",
       "        <td class=\"number\">1.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">20</td>\n",
       "        <td class=\"number\">1.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">8</td>\n",
       "        <td class=\"number\">9</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-4130452008163274622\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">891</td>\n",
       "        <td class=\"number\">68.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">319</td>\n",
       "        <td class=\"number\">24.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:36%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">42</td>\n",
       "        <td class=\"number\">3.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">20</td>\n",
       "        <td class=\"number\">1.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">22</td>\n",
       "        <td class=\"number\">1.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">42</td>\n",
       "        <td class=\"number\">3.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">20</td>\n",
       "        <td class=\"number\">1.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:48%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">22</td>\n",
       "        <td class=\"number\">1.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:52%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">8</td>\n",
       "        <td class=\"number\">9</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_parch\">parch<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>8</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>0.6%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>0.38503</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>9</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>76.5%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-6291251444545739100\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAAARZJREFUeJzt3cEJAjEQQFEVS7IIe/JsTxZhT/Eu8tHAuou%2Bdw/M5TPJKfsxxtgBLx3WHgC27Lj2AM9Ol9vHZ%2B7X8wKTgA0CSSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQNvcN9AxfR7MUGwSCQCD8xBVrhmsZ77BBIAgEgkAg/O0bZMbMu%2BUbvI2Wsx9jjLWHgK1yxYIgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoHwAHz7FJXKElCqAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-6291251444545739100,#minihistogram-6291251444545739100\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-6291251444545739100\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-6291251444545739100\"\n",
       "                                                  aria-controls=\"quantiles-6291251444545739100\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-6291251444545739100\" aria-controls=\"histogram-6291251444545739100\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-6291251444545739100\" aria-controls=\"common-6291251444545739100\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-6291251444545739100\" aria-controls=\"extreme-6291251444545739100\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-6291251444545739100\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>2</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>9</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>9</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>0.86556</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>2.2481</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>21.541</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>0.38503</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>0.58945</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>3.6691</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>504</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>0.74919</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>10.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-6291251444545739100\">\n",
       "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAAIABJREFUeJzt3X90VPWd//HXJGN%2BYmyAJF8SWFBsV0AcIAGhEBZSXEHZggEawYJCVxCDOWy1IMSN/NwoAfRsE8SAFgQXUn5ULSKutF3USquGJpGl1PBj2bgEM1kTA2RICJnvH9Y5O0Y0yod7MzPPxzmcntw7mfu%2B/eQcn3NncuPwer1eAQAAwJgwuwcAAAAINgQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYU67BwgVbvdZ488ZFuZQ586x%2BuST82pt9Rp/fnw91sB%2BrIH9WAP7sQaXl5BwrS3H5QpWAAsLc8jhcCgszGH3KCGLNbAfa2A/1sB%2BrEHHQ2ABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYFvCB1dzcrPHjx%2BuPf/yjb1tVVZXuu%2B8%2BDRgwQHfccYfefvttv%2B955513NH78eLlcLs2YMUNVVVV%2B%2Bzdt2qT09HQNHDhQixcvlsfjseRcAABAcAjowGpqatJPf/pTVVZW%2BrZ5vV5lZ2era9eu2rVrlyZMmKB58%2Bbp9OnTkqTTp08rOztbmZmZ2rlzpzp37qwHH3xQXu9nf1rg9ddfV2FhoZYtW6bNmzervLxcBQUFtpwfAAAITAEbWMeOHdOPfvQj/fd//7ff9j/84Q%2BqqqrSsmXL1Lt3b82ZM0cDBgzQrl27JEk7duzQzTffrFmzZum73/2u8vPz9T//8z969913JUkvvPCC7r33Xo0ePVq33HKLli5dql27dnEVCwAAtFvABta7776rW2%2B9VSUlJX7by8vL1bdvX8XExPi2paamqqyszLc/LS3Nty86Olr9%2BvVTWVmZLl26pA8%2B%2BMBv/4ABA3Tx4kUdPXr0Kp8RAAAIFk67B/i2pk2b9qXb3W63EhMT/bZ16dJFZ86c%2Bdr9DQ0Nampq8tvvdDr1ne98x/f9AAAAXydgA%2BtyPB6PIiIi/LZFRESoubn5a/dfuHDB9/Xlvr89ampq5Ha7/bY5nTFtwu5KhYeHKS13n9HnvJreeCTd7hGMCw8P8/tfWI81sB9rYD/WoOMJusCKjIxUfX2937bm5mZFRUX59n8xlpqbmxUXF6fIyEjf11/cHx0d3e4ZSkpKVFhY6LctOztbOTk57X6OYBQfH2v3CFdNXFz7fz5wdbAG9mMN7McadBxBF1hJSUk6duyY37ba2lrf1aOkpCTV1ta22d%2BnTx995zvfUWRkpGpra9W7d29JUktLi%2Brr65WQkNDuGbKyspSRkeG3zemMUV3d%2BW9zSpcVaK9UTJ9/RxAeHqa4uGg1NHh06VKr3eOEJNbAfqyB/ViDy7PrxX3QBZbL5VJxcbEuXLjgu2pVWlqq1NRU3/7S0lLf4z0ej44cOaJ58%2BYpLCxM/fv3V2lpqW699VZJUllZmZxOp2666aZ2z5CYmNjm7UC3%2B6xaWkL7hz6Yz//SpdagPr9AwBrYjzWwH2vQcQTWJZB2GDJkiLp166ZFixapsrJSxcXFqqio0OTJkyVJkyZN0qFDh1RcXKzKykotWrRI3bt39wXVtGnT9Nxzz2n//v2qqKjQkiVL9KMf/egbvUUIAABCW9AFVnh4uNatWye3263MzEy98sorKioqUnJysiSpe/fu%2BvnPf65du3Zp8uTJqq%2BvV1FRkRwOhyTpzjvv1Jw5c5SXl6dZs2bplltu0c9%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%2B33vnj17NGbMGLlcLmVnZ%2BuTTz6xeHoAABDIgjaw5s%2Bfr5iYGO3evVuLFy/W008/rTfeeEONjY2aPXu20tLStHv3bg0cOFBz5sxRY2OjJKmiokK5ubmaN2%2BeSkpK1NDQoEWLFtl8NgAAIJAEZWB9%2BumnKisr09y5c9WrVy%2BNGTNG6enpOnjwoPbu3avIyEgtWLBAvXv3Vm5urmJjY7Vv3z5J0tatWzVu3DhNnDhRN910k1atWqUDBw6oqqrK5rMCAACBIigDKyoqStHR0dq9e7cuXryoEydO6NChQ%2BrTp4/Ky8uVmpoqh8MhSXI4HBo0aJDKysokSeXl5UpLS/M9V7du3ZScnKzy8nJbzgUAAASeoAysyMhI5eXlqaSkRC6XS%2BPGjdPIkSM1ZcoUud1uJSYm%2Bj2%2BS5cuOnPmjCSppqbmK/cDAAB8HafdA1wtx48f1%2BjRozVz5kxVVlZq%2BfLlGjZsmDwejyIiIvweGxERoebmZknShQsXvnJ/e9TU1Mjtdvttczpj2oTblQoPD6w%2BdjoDa972%2BHwNAm0tgglrYD/WwH6sQccTlIF18OBB7dy5UwcOHFBUVJT69%2B%2Bvjz/%2BWM8884x69OjRJpaam5sVFRUl6bOrX1%2B2Pzo6ut3HLykpUWFhod%2B27Oxs5eTkfMszCg7x8bF2j3DVxMW1/%2BcDVwdrYD/WwH6sQccRlIF1%2BPBh9ezZ0xdNktS3b1%2BtX79eaWlpqq2t9Xt8bW2t7%2BpSUlLSl%2B5PSEho9/GzsrKUkZHht83pjFFd3flveipfKdBeqZg%2B/44gPDxMcXHRamjw6NKlVrvHCUmsgf1YA/uxBpdn14v7oAysxMREnTp1Ss3Nzb63%2B06cOKHu3bvL5XJpw4YN8nq9cjgc8nq9OnTokB544AFJksvlUmlpqTIzMyV9dj%2Bt6upquVyub3T8L74d6HafVUtLaP/QB/P5X7rUGtTnFwhYA/uxBvZjDTqOwLoE0k4ZGRm65ppr9Nhjj%2BnkyZP67W9/q/Xr12v69OkaO3asGhoatHLlSh07dkwrV66Ux%2BPRuHHjJElTp07Vyy%2B/rB07dujo0aNasGCBRo0apR49eth8VgAAIFAEZWBde%2B212rRpk9xutyZPnqz8/HzNnTtXWVlZ6tSpk5599lnfVary8nIVFxcrJiZGkjRw4EAtW7ZMRUVFmjp1qq677jrl5%2BfbfEYAACCQOLxer9fuIUKB233W%2BHM6nWG6bfVbxp/3anlt/nC7RzDO6QxTfHys6urOc1neJqyB/VgD%2B7EGl5eQcK0txw3KK1gAAAB2IrAAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMszywpkyZou3bt%2Bvs2bNWHxoAAMASlgfW0KFDtX79eo0YMUI//elP9fbbb8vr9Vo9BgAAwFVjeWA9/PDD%2Bt3vfqd169YpPDxcDz30kEaNGqWnnnpKJ0%2BetHocAAAA45x2HNThcGj48OEaPny4PB6PtmzZonXr1qm4uFiDBg3Svffeq7//%2B7%2B3YzQAAIArZktgSVJNTY1eeeUVvfLKK/rwww81aNAg3XXXXTpz5owee%2Bwxvffee8rNzbVrPAAAgG/N8rcIX375Zc2aNUujR4/W5s2b9Xd/93fat2%2Bf/u3f/k1TpkzRQw89pAULFmjnzp1XdJzm5mYtXbpUgwcP1ve//32tXbvW91mvI0eOaMqUKXK5XJo0aZIOHz7s97179uzRmDFj5HK5lJ2drU8%2B%2BeSKZgEAAKHF8sDKzc1VbGysioqKdODAAT388MPq1auX32NuuOEG/fjHP76i46xYsULvvPOOnnvuOa1Zs0a//OUvVVJSosbGRs2ePVtpaWnavXu3Bg4cqDlz5qixsVGSVFFRodzcXM2bN08lJSVqaGjQokWLrmgWAAAQWix/i/DNN99UfHy86uvrFRb2Wd9VVFSoX79%2BCg8PlyQNGjRIgwYN%2BtbHqK%2Bv165du/SLX/xCt9xyiyRp1qxZKi8vl9PpVGRkpBYsWCCHw6Hc3Fy9%2Beab2rdvnzIzM7V161aNGzdOEydOlCStWrVKo0ePVlVVlXr06HGFZw8AAEKB5Vewzp07p7Fjx2rDhg2%2BbbNnz9aECRNUXV1t5BilpaXq1KmThgwZ4neM/Px8lZeXKzU1VQ6HQ9JnH7gfNGiQysrKJEnl5eVKS0vzfV%2B3bt2UnJys8vJyI7MBAIDgZ3lg/cu//It69uypmTNn%2Brbt3btX3bp1U35%2BvpFjVFVVKSUlRS%2B99JLGjh2rH/zgByoqKlJra6vcbrcSExP9Ht%2BlSxedOXNG0mcfvv%2Bq/QAAAF/H8rcI33//ff3yl79UQkKCb1vnzp21YMEC3XPPPUaO0djYqFOnTmn79u3Kz8%2BX2%2B1WXl6eoqOj5fF4FBER4ff4iIgINTc3S5IuXLjwlfvbo6amRm6322%2Bb0xnTJtyuVHh4YP2lI6czsOZtj8/XINDWIpiwBvZjDezHGnQ8lgeW0%2BlUQ0NDm%2B0ej8fYHd2dTqfOnTunNWvWKCUlRZJ0%2BvRpbdu2TT179mwTS83NzYqKipIkRUZGfun%2B6Ojodh%2B/pKREhYWFftuys7OVk5PzbU4naMTHx9o9wlUTF9f%2Bnw9cHayB/VgD%2B7EGHYflgTVy5EitWLFCa9eu1d/8zd9I%2Buwtvfz8fKWnpxs5RkJCgiIjI31xJUnXX3%2B9qqurNWTIENXW1vo9vra21nd1KSkp6Uv3/98rbl8nKytLGRkZftuczhjV1Z3/pqfylQLtlYrp8%2B8IwsPDFBcXrYYGjy5darV7nJDEGtiPNbAfa3B5dr24tzywFi5cqJkzZ%2Br2229XXFycJKmhoUH9%2BvUzdjsEl8ulpqYmnTx5Utdff70k6cSJE0pJSZHL5dKGDRvk9XrlcDjk9Xp16NAhPfDAA77vLS0tVWZmpiSpurpa1dXVcrlc7T5%2BYmJim7cD3e6zamkJ7R/6YD7/S5dag/r8AgFrYD/WwH6sQcdheWB16dJFv/rVr/TOO%2B%2BosrJSTqdTN954o4YNG%2Bb7zb4rdcMNN2jUqFFatGiRlixZIrfbreLiYs2dO1djx47VmjVrtHLlSt19993avn27PB6Pxo0bJ0maOnWqpk%2BfrgEDBqh///5auXKlRo0axS0aAABAuzm8pj741MGcPXtWy5cv1xtvvKHo6GhNmzZN2dnZcjgcqqio0OOPP67jx4/rb//2b7V06VL17dvX9727d%2B/Wv/7rv%2BrTTz/V8OHDtXz5csXHx1/RPG732Ss9pTaczjDdtvot4897tbw2f7jdIxjndIYpPj5WdXXnedVoE9bAfqyB/ViDy0tIuNaW41oeWG63W08//bQOHTqkixcvtvlg%2B29%2B8xsrx7EMgUVg4epgDezHGtiPNbg8uwLL8rcI//mf/1mHDx/WnXfeqWuvteekAQAAribLA%2BsPf/iDNm7c6He3dAAAgGBi%2Be/5x8TEqEuXLlYfFgAAwDKWB9aECRO0ceNGXbp0yepDAwAAWMLytwjr6%2Bu1Z88e/cd//Id69OjR5s/SvPDCC1aPBAAAYJTlgSVJ48ePt%2BOwAAAAlrA8sPLz860%2BJAAAgKVs%2BWN2NTU1Kiws1MMPP6z//d//1b59%2B3TixAk7RgEAADDO8sA6deqU/uEf/kG/%2BtWv9Prrr6uxsVF79%2B7VpEmTVF5ebvU4AAAAxlkeWE888YTGjBmj/fv365prrpEkrV27VhkZGVq9erXV4wAAABhneWAdOnRIM2fO9PvDzk6nUw8%2B%2BKCOHDli9TgAAADGWR5Yra2tam1t%2B3eSzp8/r/DwcKvHAQAAMM7ywBoxYoSeffZZv8iqr69XQUGBhg4davU4AAAAxlkeWI8%2B%2BqgOHz6sESNGqKmpSXPnztXo0aP10UcfaeHChVaPAwAAYJzl98FKSkrSSy%2B9pD179ujPf/6zWltbNXXqVE2YMEGdOnWyehwAAADjbLmTe3R0tKZMmWLHoQEAAK46ywNrxowZX7mfv0UIAAACneWBlZKS4vd1S0uLTp06pQ8//FD33nuv1eMAAAAY12H%2BFmFRUZHOnDlj8TQAAADm2fK3CL/MhAkT9Nprr9k9BgAAwBXrMIH1pz/9iRuNAgCAoNAhPuR%2B7tw5/eUvf9G0adOsHgcAAMA4ywMrOTnZ7%2B8QStI111yjH//4x/rhD39o9TgAAADGWR5YTzzxhNWHBAAAsJTlgfXee%2B%2B1%2B7GDBw%2B%2BipMAAABcHZYH1vTp031vEXq9Xt/2L25zOBz685//bPV4AAAAV8zywFq/fr1WrFihn/3sZxoyZIgiIiL0wQcfaNmyZbrrrrt0xx13WD0SAACAUZbfpiE/P195eXm6/fbbFR8fr9jYWA0dOlTLli3Ttm3blJKS4vsHAAAQiCwPrJqami%2BNp06dOqmurs7qcQAAAIyzPLAGDBigtWvX6ty5c75t9fX1Kigo0LBhw6weBwAAwDjLP4P12GOPacaMGRo5cqR69eolr9er//qv/1JCQoJeeOEFq8cBAAAwzvLA6t27t/bu3as9e/bo%2BPHjkqR77rlHd955p6Kjo60eBwAAwDjLA0uSrrvuOk2ZMkUfffSRevToIemzu7kDAAAEA8s/g%2BX1erV69WoNHjxY48eP15kzZ7Rw4ULl5ubq4sWLVo8DAABgnOWBtWXLFr388st6/PHHFRERIUkaM2aM9u/fr8LCQqvHAQAAMM7ywCopKVFeXp4yMzN9d2%2B/4447tGLFCv3617%2B2ehwAAADjLA%2Bsjz76SH369Gmz/aabbpLb7bZ6HAAAAOMsD6yUlBR98MEHbba/%2Beabvg%2B8AwAABDLLf4vwJz/5iZYuXSq32y2v16uDBw%2BqpKREW7Zs0aOPPmr1OAAAAMZZHliTJk1SS0uLnnnmGV24cEF5eXnq3Lmz5s%2Bfr6lTp1o9DgAAgHGWB9aePXs0duxYZWVl6ZNPPpHX61WXLl2sHgMAAOCqsfwzWMuWLfN9mL1z587EFQAACDqWB1avXr304YcfWn1YAAAAy1j%2BFuFNN92kRx55RBs3blSvXr0UGRnptz8/P9/qkQAAAIyyPLBOnjyp1NRUSeK%2BVwAAIChZElirVq3SvHnzFBMToy1btlhxSAAAANtY8hmsX/ziF/J4PH7bZs%2BerZqaGisODwAAYClLAsvr9bbZ9t5776mpqcmKwwMAAFjK8t8iBAAACHZBH1izZ8/2%2BxM8R44c0ZQpU%2BRyuTRp0iQdPnzY7/F79uzRmDFj5HK5lJ2drU8%2B%2BcTqkQEAQICzLLAcDodVh/J59dVXdeDAAd/XjY2Nmj17ttLS0rR7924NHDhQc%2BbMUWNjoySpoqJCubm5mjdvnkpKStTQ0KBFixZZPjcAAAhslt2mYcWKFX73vLp48aIKCgoUGxvr9zhT98Gqr6/XqlWr1L9/f9%2B2vXv3KjIyUgsWLJDD4VBubq7efPNN7du3T5mZmdq6davGjRuniRMnSvrstx9Hjx6tqqoq9ejRw8hcAAAg%2BFkSWIMHD25zz6uBAweqrq5OdXV1V%2BWYTz75pCZMmOD3m4rl5eVKTU31XU1zOBwaNGiQysrKlJmZqfLyct1///2%2Bx3fr1k3JyckqLy8nsAAAQLtZElhW3/vq4MGDev/99/XrX/9aS5Ys8W13u9268cYb/R7bpUsXVVZWSpJqamqUmJjYZv%2BZM2e%2B0fFramraBKXTGdPmua9UeHhgfYTO6Qysedvj8zUItLUIJqyB/VgD%2B7EGHY/ld3K/2pqamvT4448rLy9PUVFRfvs8Ho8iIiL8tkVERKi5uVmSdOHCha/c314lJSUqLCz025adna2cnJxv9DzBJj4%2B9usfFKDi4qLtHiHksQb2Yw3sxxp0HEEXWIWFhbr55puVnp7eZl9kZGSbWGpubvaF2OX2R0d/sx/YrKwsZWRk%2BG1zOmNUV3f%2BGz3P1wm0Vyqmz78jCA8PU1xctBoaPLp0qdXucUISa2A/1sB%2BrMHl2fXiPugC69VXX1Vtba0GDhwoSb5gev311zV%2B/HjV1tb6Pb62ttb31l1SUtKX7k9ISPhGMyQmJrZ5O9DtPquWltD%2BoQ/m8790qTWozy8QsAb2Yw3sxxp0HEEXWFu2bFFLS4vv69WrV0uSHnnkEb333nvasGGDvF6vHA6HvF6vDh06pAceeECS5HK5VFpaqszMTElSdXW1qqur5XK5rD8RAAAQsIIusFJSUvy%2B/vw2ED179lSXLl20Zs0arVy5Unfffbe2b98uj8ejcePGSZKmTp2q6dOna8CAAerfv79WrlypUaNG8RuEAADgGwmsD/FcoU6dOunZZ5/1XaUqLy9XcXGxYmJiJH1264hly5apqKhIU6dO1XXXXWfsvlwAACB0OLxf9peYYZzbfdb4czqdYbpt9VvGn/dqeW3%2BcLtHMM7pDFN8fKzq6s7zuQebsAb2Yw3sxxpcXkLCtbYcN6SuYAEAAFiBwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADAsaAPr448/Vk5OjoYMGaL09HTl5%2BerqalJklRVVaX77rtPAwYM0B133KG3337b73vfeecdjR8/Xi6XSzNmzFBVVZUdpwAAAAJUUAaW1%2BtVTk6OPB6PXnzxRT311FP63e9%2Bp6efflper1fZ2dnq2rWrdu3apQkTJmjevHk6ffq0JOn06dPKzs5WZmamdu7cqc6dO%2BvBBx%2BU1%2Bu1%2BawAAECgcNo9wNVw4sQJlZWV6fe//726du0qScrJydGTTz6pkSNHqqqqStu3b1dMTIx69%2B6tgwcPateuXXrooYe0Y8cO3XzzzZo1a5YkKT8/X8OHD9e7776rW2%2B91c7TAgAAASIor2AlJCRo48aNvrj63Llz51ReXq6%2BffsqJibGtz01NVVlZWWSpPLycqWlpfn2RUdHq1%2B/fr79AAAAXycor2DFxcUpPT3d93Vra6u2bt2qoUOHyu12KzEx0e/xXbp00ZkzZyTpa/e3R01Njdxut982pzOmzfNeqfDwwOpjpzOw5m2Pz9cg0NYimLAG9mMN7McadDxBGVhfVFBQoCNHjmjnzp3atGmTIiIi/PZHRESoublZkuTxeL5yf3uUlJSosLDQb1t2drZycnK%2B5RkEh/j4WLtHuGri4qLtHiHksQb2Yw3sxxp0HEEfWAUFBdq8ebOeeuopfe9731NkZKTq6%2Bv9HtPc3KyoqChJUmRkZJuYam5uVlxcXLuPmZWVpYyMDL9tTmeM6urOf8uz%2BHKB9krF9Pl3BOHhYYqLi1ZDg0eXLrXaPU5IYg3sxxrYjzW4PLte3Ad1YC1fvlzbtm1TQUGBbr/9dklSUlKSjh075ve42tpa39t3SUlJqq2tbbO/T58%2B7T5uYmJim7cD3e6zamkJ7R/6YD7/S5dag/r8AgFrYD/WwH6sQccRWJdAvoHCwkJt375da9eu1Z133unb7nK59J//%2BZ%2B6cOGCb1tpaalcLpdvf2lpqW%2Bfx%2BPRkSNHfPsBAAC%2BTlAG1vHjx7Vu3Trdf//9Sk1Nldvt9v0bMmSIunXrpkWLFqmyslLFxcWqqKjQ5MmTJUmTJk3SoUOHVFxcrMrKSi1atEjdu3fnFg0AAKDdgjKwfvOb3%2BjSpUt65plnNGLECL9/4eHhWrdundxutzIzM/XKK6%2BoqKhIycnJkqTu3bvr5z//uXbt2qXJkyervr5eRUVFcjgcNp8VAAAIFA4vtyi3hNt91vhzOp1hum31W8af92p5bf5wu0cwzukMU3x8rOrqzvO5B5uwBvZjDezHGlxeQsK1thw3KK9gAQAA2InAAgAAMCyob9OAjmXc07%2B3e4RvJBjf0gQAWIMrWAD94aqYAAAJEklEQVQAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIY57R4A6KjGPf17u0f4Rl6bP9zuEQAAf8UVLAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMIrC/R1NSkxYsXKy0tTSNGjNDzzz9v90gAACCAOO0eoCNatWqVDh8%2BrM2bN%2Bv06dNauHChkpOTNXbsWLtHAwAAAYDA%2BoLGxkbt2LFDGzZsUL9%2B/dSvXz9VVlbqxRdfJLAAAEC7EFhfcPToUbW0tGjgwIG%2BbampqVq/fr1aW1sVFsa7quiYxj39e7tHQAfx2vzhdo8AhDwC6wvcbrfi4%2BMVERHh29a1a1c1NTWpvr5enTt3/trnqKmpkdvt9tvmdMYoMTHR6Kzh4cQegLYCKbbfeCTd7hGCwuf/PfjifxduW/2WHeN8K8H2s0BgfYHH4/GLK0m%2Br5ubm9v1HCUlJSosLPTbNm/ePD300ENmhvyrmpoa3fv/KpWVlWU83tA%2BNTU1KikpYQ1sxBrYjzWwX01NjTZv3thmDd5fyUdb7MIlkC%2BIjIxsE1Kffx0VFdWu58jKytLu3bv9/mVlZRmf1e12q7CwsM3VMliHNbAfa2A/1sB%2BrEHHwxWsL0hKSlJdXZ1aWlrkdH72f4/b7VZUVJTi4uLa9RyJiYm8igMAIIRxBesL%2BvTpI6fTqbKyMt%2B20tJS9e/fnw%2B4AwCAdqEYviA6OloTJ07UkiVLVFFRof379%2Bv555/XjBkz7B4NAAAEiPAlS5YssXuIjmbo0KE6cuSI1qxZo4MHD%2BqBBx7QpEmT7B7rS8XGxmrIkCGKjY21e5SQxRrYjzWwH2tgP9agY3F4vV6v3UMAAAAEE94iBAAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAClBNTU1avHix0tLSNGLECD3//PN2jxRSPv74Y%2BXk5GjIkCFKT09Xfn6%2Bmpqa7B4rZM2ePVuPPvqo3WOEnObmZi1dulSDBw/W97//fa1du1bcu9pa1dXVmjNnjgYNGqSMjAxt2rTJ7pHwV067B8C3s2rVKh0%2BfFibN2/W6dOntXDhQiUnJ2vs2LF2jxb0vF6vcnJyFBcXpxdffFGffvqpFi9erLCwMC1cuNDu8ULOq6%2B%2BqgMHDuiuu%2B6ye5SQs2LFCv3xj3/Uc889p/Pnz%2Buf/umflJycrLvvvtvu0ULG/PnzlZycrN27d%2BvYsWN65JFHlJKSottuu83u0UIeV7ACUGNjo3bs2KHc3Fz169dPt912m/7xH/9RL774ot2jhYQTJ06orKxM%2Bfn5%2Bu53v6u0tDTl5ORoz549do8Wcurr67Vq1Sr179/f7lFCTn19vXbt2qXly5frlltu0bBhwzRr1iyVl5fbPVrI%2BPTTT1VWVqa5c%2BeqV69eGjNmjNLT03Xw4EG7R4MIrIB09OhRtbS0aODAgb5tqampKi8vV2trq42ThYaEhARt3LhRXbt29dt%2B7tw5myYKXU8%2B%2BaQmTJigG2%2B80e5RQk5paak6deqkIUOG%2BLbNnj1b%2Bfn5Nk4VWqKiohQdHa3du3fr4sWLOnHihA4dOqQ%2BffrYPRpEYAUkt9ut%2BPh4RURE%2BLZ17dpVTU1Nqq%2Bvt3Gy0BAXF6f09HTf162trdq6dauGDh1q41Sh5%2BDBg3r//ff14IMP2j1KSKqqqlJKSopeeukljR07Vj/4wQ9UVFTEizwLRUZGKi8vTyUlJXK5XBo3bpxGjhypKVOm2D0axGewApLH4/GLK0m%2Br5ubm%2B0YKaQVFBToyJEj2rlzp92jhIympiY9/vjjysvLU1RUlN3jhKTGxkadOnVK27dvV35%2Bvtxut/Ly8hQdHa1Zs2bZPV7IOH78uEaPHq2ZM2eqsrJSy5cv17Bhw/TDH/7Q7tFCHoEVgCIjI9uE1Odf8x8baxUUFGjz5s166qmn9L3vfc/ucUJGYWGhbr75Zr8ribCW0%2BnUuXPntGbNGqWkpEiSTp8%2BrW3bthFYFjl48KB27typAwcOKCoqSv3799fHH3%2BsZ555hsDqAAisAJSUlKS6ujq1tLTI6fxsCd1ut6KiohQXF2fzdKFj%2BfLl2rZtmwoKCnT77bfbPU5IefXVV1VbW%2Bv7HOLnLzBef/11/elPf7JztJCRkJCgyMhIX1xJ0vXXX6/q6mobpwothw8fVs%2BePf1eWPft21fr16%2B3cSp8jsAKQH369JHT6VRZWZnS0tIkffaB0/79%2ByssjI/VWaGwsFDbt2/X2rVruTWGDbZs2aKWlhbf16tXr5YkPfLII3aNFHJcLpeampp08uRJXX/99ZI%2B%2Bw3b/xtcuLoSExN16tQpNTc3%2Bz4mcuLECXXv3t3mySDxIfeAFB0drYkTJ2rJkiWqqKjQ/v379fzzz2vGjBl2jxYSjh8/rnXr1un%2B%2B%2B9Xamqq3G637x%2BskZKSop49e/r%2BxcbGKjY2Vj179rR7tJBxww03aNSoUVq0aJGOHj2qt956S8XFxZo6dardo4WMjIwMXXPNNXrsscd08uRJ/fa3v9X69es1ffp0u0eDJIeX2%2B4GJI/HoyVLlujf//3f1alTJ/3kJz/RfffdZ/dYIaG4uFhr1qz50n1/%2BctfLJ4Gknx3cX/iiSdsniS0nD17VsuXL9cbb7yh6OhoTZs2TdnZ2XI4HHaPFjKOHTumlStXqqKiQp07d9Y999yje%2B%2B9lzXoAAgsAAAAw3iLEAAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwLD/DyjEg91/V4bIAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-6291251444545739100\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">1002</td>\n",
       "        <td class=\"number\">76.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">170</td>\n",
       "        <td class=\"number\">13.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">113</td>\n",
       "        <td class=\"number\">8.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">8</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-6291251444545739100\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0</td>\n",
       "        <td class=\"number\">1002</td>\n",
       "        <td class=\"number\">76.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1</td>\n",
       "        <td class=\"number\">170</td>\n",
       "        <td class=\"number\">13.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">2</td>\n",
       "        <td class=\"number\">113</td>\n",
       "        <td class=\"number\">8.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">8</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">8</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:75%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:75%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_ticket\">ticket<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"alert\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>939</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>71.7%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>0.0%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>0</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable8950128611859980159\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>CA. 2343</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.8%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        11\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>CA 2144</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.6%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        8\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>1601</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.6%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        8\n",
       "    </td>\n",
       "</tr><tr class=\"other\">\n",
       "    <th>Other values (936)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 97.9%\">\n",
       "            1282\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable8950128611859980159, #minifreqtable8950128611859980159\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable8950128611859980159\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">CA. 2343</td>\n",
       "        <td class=\"number\">11</td>\n",
       "        <td class=\"number\">0.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">CA 2144</td>\n",
       "        <td class=\"number\">8</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">1601</td>\n",
       "        <td class=\"number\">8</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">347077</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">347082</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3101295</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">S.O.C. 14879</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">PC 17608</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">113781</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">382652</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (929)</td>\n",
       "        <td class=\"number\">1235</td>\n",
       "        <td class=\"number\">94.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_fare\">fare<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>282</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>21.5%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>0.1%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>1</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>33.295</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>512.33</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>1.3%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram-2021821369331812041\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAAARNJREFUeJzt3bEJAkEQQFEVS7IIezK2J4uwpzUX%2BejCeYe%2Bly9M8hkm2v0YY%2ByAlw5rDwBbdlx7gGeny%2B3jN/freYFJwAaBJBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoGwuX/SZ/hbnaXYIBAEAkEgEAQC4SeO9BkOe97xt4F8w0yEM4S7nP0YY6w9BGyVGwSCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCATCA5JpFJU50JYtAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-2021821369331812041,#minihistogram-2021821369331812041\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives-2021821369331812041\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-2021821369331812041\"\n",
       "                                                  aria-controls=\"quantiles-2021821369331812041\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram-2021821369331812041\" aria-controls=\"histogram-2021821369331812041\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common-2021821369331812041\" aria-controls=\"common-2021821369331812041\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme-2021821369331812041\" aria-controls=\"extreme-2021821369331812041\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-2021821369331812041\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>0</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>7.225</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>7.8958</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>14.454</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>31.275</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>133.65</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>512.33</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>512.33</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>23.379</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>51.759</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>1.5545</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>27.028</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>33.295</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>29.799</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>4.3677</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>43550</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>2679</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>10.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-2021821369331812041\">\n",
       "            <img 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AAxtlesP7rv/5LF198sW655Rbf2vbt29W1a1fl5%2BfbPQ4AAIBxthesjz/%2BWPfee68SExN9awkJCZo1a5Y%2B%2BOADu8cBAAAwzvaC5XQ6VVtb22Ld6/VyR3cAABAWbC9YV111lRYvXqyvv/7at1ZeXq78/HxlZmbaPQ4AAIBxtv8U4ezZs3XLLbdo5MiRiouLkyTV1taqf//%2BmjNnjt3jAAAAGGd7wercubNeeOEFvf/%2B%2ByorK5PT6VTv3r115ZVXyuFw2D0OAACAcUH5VTkRERHKzMzkJUEAABCWbC9YHo9HK1eu1N69e3X69OkWb2x/88037R4JAADAKNsL1p/%2B9Cft379fY8aM0fnnnx%2Bw4zQ0NCg/P1/btm3Teeedp4kTJ%2BoPf/iDHA6HSktLNX/%2BfB08eFC9e/fW/fffrwEDBvi%2Bdtu2bVq5cqU8Ho%2BGDh2qRYsWKSEhIWCzAgCA8GJ7wfrggw%2B0du1aZWRkBPQ4ixcv1ocffqg///nPOnnypP7whz%2BoW7duuu666zR9%2BnRde%2B21euCBB/T8889rxowZev311xUbG6t9%2B/YpLy9P999/v/r27aslS5Zozpw5Wr16dUDnBQAA4cP2ghUbG6vOnTsH9Bg1NTUqKirS008/rcsuu0ySdOutt8rtdsvpdCoqKkqzZs2Sw%2BFQXl6e3nnnHb322mvKzs7Ws88%2Bq1GjRmn8%2BPGSpKVLl2r48OEqLy9Xjx49Ajo3AAAID7bfB2vcuHFau3atmpqaAnaM4uJiderUSYMGDfKtTZ8%2BXfn5%2BXK73UpPT/f9xKLD4VBaWppKSkokSW632%2B/qWteuXdWtWze53e6AzQsAAMKL7VewampqtG3bNr399tvq0aOHIiMj/fafeeaZcz5GeXm5unfvrhdffFFPPPGETp8%2BrezsbN12223yeDzq3bu33%2BM7d%2B6ssrIySVJlZaWSkpJa7B89erTVx6%2BsrJTH4/FbczpjWzzvuYqIsL0fnxOns%2B3P%2B32moZZtW0am5pFpYJCree0506DcpmHs2LEBff66ujp99dVX2rhxo/Lz8%2BXxeDRv3jzFxMTI6/W2KHWRkZFqaGiQJJ06deqf7rdGYWGhCgoK/NZycnKUm5t7lmcUHuLjOwZ7hFaLi4sJ9ghhh0zNI9PAIFfz2mOmthes/Pz8gB/D6XTqxIkTeuihh9S9e3dJ0pEjR/T888/r4osvblGWGhoaFB0dLUmKior60f2YmNb/4Zg8ebKysrJ%2BMFOsqqtPns3p/KRQ%2B47A9PkHQkREB8XFxai21qumpuZgjxMWyNQ8Mg0McjWvLWQarG/ug3IFq7KyUn/5y190%2BPBhzZ07V3v27FGfPn10ySWXGHn%2BxMRERUVF%2BcqVJP3rv/6rKioqNGjQIFVVVfk9vqqqyvfyXXJy8o/uJyYmtvr4SUlJLV4O9HiOq7Gxff%2BFDaXzb2pqDql5QwGZmkemgUGu5rXHTG2/BPLVV1/p2muv1QsvvKAdO3aorq5O27dv14QJE4y9kdzlcqm%2Bvl6HDx/2rX355Zfq3r27XC6XPvnkE98NTi3L0t69e%2BVyuXxfW1xc7Pu6iooKVVRU%2BPYBAADOxPaC9cADD2jEiBF64403dN5550mSHn74YWVlZWn58uVGjnHJJZdo2LBhmjNnjg4cOKB3331Xa9as0ZQpU3TNNdeotrZWS5Ys0aFDh7RkyRJ5vV6NGjVKkjRlyhS99NJL2rRpkw4cOKBZs2Zp2LBh3KIBAAC0mu0Fa%2B/evbrlllv8frGz0%2BnU7bffrtLSUmPHWb58uf7lX/5FU6ZM0ezZs3XDDTfopptuUqdOnbR69WoVFxcrOztbbrdba9asUWxsrCQpNTVVCxcu1KpVqzRlyhRdcMEFtrxvDAAAhA/b34PV3Nys5uaWr8OePHlSERERxo5z/vnna%2BnSpT%2B6d9lll%2BmFF174ya/Nzs5Wdna2sVkAAED7YvsVrKFDh2r16tV%2BJaumpkbLli3T4MGD7R4HAADAONsL1r333qv9%2B/dr6NChqq%2Bv12233abhw4frm2%2B%2B0ezZs%2B0eBwAAwDjbXyJMTk7Wiy%2B%2BqG3btunzzz9Xc3OzpkyZonHjxqlTp052jwMAAGBcUO6DFRMTo0mTJgXj0AAAAAFne8GaOnXqP9038bsIAQAAgsn2gvWPd1eXpMbGRn311Vc6ePCgpk2bZvc4AAAAxrWZ30W4atUqHT161OZpAAAAzGszvy143LhxevXVV4M9BgAAwDlrMwXrk08%2BMXqjUQAAgGBpE29yP3HihP7nf/5H119/vd3jAAAAGGd7werWrZvf7yGUpPPOO0833nijrrvuOrvHAQAAMM72gvXAAw/YfUgAAABb2V6w9uzZ0%2BrHXn755QGcBAAAIDBsL1g33XST7yVCy7J86z9cczgc%2Bvzzz%2B0eDwAA4JzZXrCeeOIJLV68WPfcc48GDRqkyMhIffrpp1q4cKF%2B/etfa/To0XaPBAAAYJTtt2nIz8/XvHnzNHLkSMXHx6tjx44aPHiwFi5cqOeff17du3f3fQAAAIQi2wtWZWXlj5anTp06qbq62u5xAAAAjLO9YKWkpOjhhx/WiRMnfGs1NTVatmyZrrzySrvHAQAAMM7292Ddd999mjp1qq666ir17NlTlmXpr3/9qxITE/XMM8/YPQ4AAIBxthesXr16afv27dq2bZu%2B%2BOILSdINN9ygMWPGKCYmxu5xAAAAjLO9YEnSBRdcoEmTJumbb75Rjx49JH13N3cAAIBwYPt7sCzL0vLly3X55Zdr7NixOnr0qGbPnq28vDydPn3a7nEAAACMs71gbdiwQS%2B99JLmz5%2BvyMhISdKIESP0xhtvqKCgwO5xAAAAjLO9YBUWFmrevHnKzs723b199OjRWrx4sbZu3Wr3OAAAAMbZXrC%2B%2BeYb9evXr8V637595fF47B4HAADAONsLVvfu3fXpp5%2B2WH/nnXd8b3gHAAAIZbb/FOFvf/tb3X///fJ4PLIsS7t371ZhYaE2bNige%2B%2B91%2B5xAAAAjLO9YE2YMEGNjY16/PHHderUKc2bN08JCQm68847NWXKFLvHAQAAMM72grVt2zZdc801mjx5so4dOybLstS5c2e7xwAAAAgY29%2BDtXDhQt%2Bb2RMSEihXAAAg7NhesHr27KmDBw/afVgAAADb2P4SYd%2B%2BfXX33Xdr7dq16tmzp6Kiovz28/Pz7R4JAADAKNsL1uHDh5Weni5J3PcKAACEJVsK1tKlSzVz5kzFxsZqw4YNdhwSAAAgaGx5D9bTTz8tr9frtzZ9%2BnRVVlbacXgAAABb2VKwLMtqsbZnzx7V19fbcXgAAABb2f5ThAAAAOGOggUAAGCYbQXL4XDYdSgAAICgsu02DYsXL/a759Xp06e1bNkydezY0e9x3AcLAACEOlsK1uWXX97inlepqamqrq5WdXW1HSMAAADYxpaCxb2vAABAe8Kb3AEAAAyjYAEAABhGwQIAADAs7AvW9OnTde%2B99/o%2BLy0t1aRJk%2BRyuTRhwgTt37/f7/Hbtm3TiBEj5HK5lJOTo2PHjtk9MgAACHFhXbBeeeUV7dy50/d5XV2dpk%2BfroyMDG3ZskWpqamaMWOG6urqJEn79u1TXl6eZs6cqcLCQtXW1mrOnDnBGh8AAISosC1YNTU1Wrp0qQYOHOhb2759u6KiojRr1iz16tVLeXl56tixo1577TVJ0rPPPqtRo0Zp/Pjx6tu3r5YuXaqdO3eqvLw8WKcBAABCUNgWrAcffFDjxo1T7969fWtut1vp6em%2Bu8o7HA6lpaWppKTEt5%2BRkeF7fNeuXdWtWze53W57hwcAACHNtju522n37t36%2BOOPtXXrVi1YsMC37vF4/AqXJHXu3FllZWWSpMrKSiUlJbXYP3r06M86fmVlZYsbqzqdsS2e%2B1xFRIRWP3Y62/6832caatm2ZWRqHpkGBrma154zDbuCVV9fr/nz52vevHmKjo722/N6vYqMjPRbi4yMVENDgyTp1KlT/3S/tQoLC1VQUOC3lpOTo9zc3J/1POEmPr7jmR/URsTFxQR7hLBDpuaRaWCQq3ntMdOwK1gFBQUaMGCAMjMzW%2BxFRUW1KEsNDQ2%2BIvZT%2BzExP%2B8PxuTJk5WVleW35nTGqrr65M96njMJte8ITJ9/IEREdFBcXIxqa71qamoO9jhhgUzNI9PAIFfz2kKmwfrmPuwK1iuvvKKqqiqlpqZKkq8w7dixQ2PHjlVVVZXf46uqqnwv3SUnJ//ofmJi4s%2BaISkpqcXLgR7PcTU2tu%2B/sKF0/k1NzSE1byggU/PINDDI1bz2mGnYFawNGzaosbHR9/ny5cslSXfffbf27NmjJ598UpZlyeFwyLIs7d27V7/73e8kSS6XS8XFxcrOzpYkVVRUqKKiQi6Xy/4TAQAAISvsClb37t39Pu/Y8btLgxdffLE6d%2B6shx56SEuWLNF//Md/aOPGjfJ6vRo1apQkacqUKbrpppuUkpKigQMHasmSJRo2bJh69Ohh%2B3kAAIDQFVpv4jlHnTp10urVq31Xqdxut9asWaPY2FhJUmpqqhYuXKhVq1ZpypQpuuCCC5Sfnx/kqQEAQKhxWJZlBXuI9sDjOW78OZ3ODrp6%2BbvGnzdQXr1zSLBHOCOns4Pi4zuquvpku3u/QKCQqXlkGhjkal5byDQx8fygHLddXcECAACwAwULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGhW3B%2Bvbbb5Wbm6tBgwYpMzNT%2Bfn5qq%2BvlySVl5fr5ptvVkpKikaPHq1du3b5fe3777%2BvsWPHyuVyaerUqSovLw/GKQAAgBAVlgXLsizl5ubK6/Xqueee04oVK/TWW29p5cqVsixLOTk56tKli4qKijRu3DjNnDlTR44ckSQdOXJEOTk5ys7O1ubNm5WQkKDbb79dlmUF%2BawAAECocAZ7gED48ssvVVJSovfee09dunSRJOXm5urBBx/UVVddpfLycm3cuFGxsbHq1auXdu/eraKiIt1xxx3atGmTBgwYoFtvvVWSlJ%2BfryFDhuijjz7SFVdcEczTAgAAISIsr2AlJiZq7dq1vnL1vRMnTsjtduvSSy9VbGysbz09PV0lJSWSJLfbrYyMDN9eTEyM%2Bvfv79sHAAA4k7C8ghUXF6fMzEzf583NzXr22Wc1ePBgeTweJSUl%2BT2%2Bc%2BfOOnr0qCSdcb81Kisr5fF4/NacztgWz3uuIiJCqx87nW1/3u8zDbVs2zIyNY9MA4NczWvPmYZlwfqhZcuWqbS0VJs3b9a6desUGRnptx8ZGamGhgZJktfr/af7rVFYWKiCggK/tZycHOXm5p7lGYSH%2BPiOwR6h1eLiYoI9QtghU/PINDDI1bz2mGnYF6xly5Zp/fr1WrFihfr06aOoqCjV1NT4PaahoUHR0dGSpKioqBZlqqGhQXFxca0%2B5uTJk5WVleW35nTGqrr65FmexY8Lte8ITJ9/IEREdFBcXIxqa71qamoO9jhhgUzNI9PAIFfz2kKmwfrmPqwL1qJFi/T8889r2bJlGjlypCQpOTlZhw4d8ntcVVWV7%2BW75ORkVVVVtdjv169fq4%2BblJTU4uVAj%2Be4Ghvb91/YUDr/pqbmkJo3FJCpeWQaGORqXnvMNLQugfwMBQUF2rhxox5%2B%2BGGNGTPGt%2B5yufTZZ5/p1KlTvrXi4mK5XC7ffnFxsW/P6/WqtLTUtw8AAHAmYVmwvvjiCz322GP6z//8T6Wnp8vj8fg%2BBg0apK5du2rOnDkqKyvTmjVrtG/fPk2cOFGSNGHCBO3du1dr1qxRWVmZ5syZo4suuohbNAAAgFYLy4L15ptvqqmpSY8//riGDh3q9xEREaHHHntMHo9H2dnZevnll7Vq1Sp169ZNknTRRRfp0UcfVVFRkSZOnKiamhqtWrVKDocjyGcFAABChcPiFuW28HiOG39Op7ODrl7%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%2BvuXPnKiMjQ0OHDtVTTz0V7JEAAEAI4QrWj1i6dKn279%2Bv9evX68iRI5o9e7a6deuma665JtijAQgCrrgB%2BLkoWD9QV1enTZs26cknn1T//v3Vv39/lZWV6bnnnqNgAQCAVuElwh84cOCAGhsblZqa6ltLT0%2BX2%2B1Wc3NzECcDAAChgitYP%2BDxeBQfH6/IyEjfWpcuXVRfX6%2BamholJCSc8TkqKyvl8Xj81pzOWCUlJRmdNSKCfoz/E2ovYyFwnE7%2BbTgb3/%2BbGk7/tl69/N1gj9Bqr9%2BdGewRjKJg/YDX6/UrV5J8nzc0NLTqOQoLC1VQUOC3NnPmTN1xxx1mhvy7yspKTft/ZZo8ebLx8tZeVVZWqrCwkEwNIlPzyDQwKisrtX792rDK9eMlwX1rS3v%2Bsxo%2BNd2QqKioFkXq%2B8%2Bjo6Nb9RyTJ0/Wli1b/D4mT55sfFaPx6OCgoIWV8tw9sjUPDI1j0wDg1zNa8%2BZcgXrB5KTk1VdXa3GxkY5nd/F4/F4FB0drbi4uFY9R1JSUrtr6gAA4P9wBesH%2BvXrJ6fTqZKSEt9acXGxBg4cqA4diAsAAJwZjeEHYmJiNH78eC1YsED79u3TG2%2B8oaeeekpTp04N9mgAACBERCxYsGBBsIdoawYPHqzS0lI99NBD2r17t373u99pwoQJwR7rR3Xs2FGDBg1Sx44dgz1K2CBT88jUPDINDHI1r71m6rAsywr2EAAAAOGElwgBAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwQlR9fb3mzp2rjIwMDR06VE899VSwRwoZDQ0NGjt2rD788EPfWnl5uW6%2B%2BWalpKRo9OjR2rVrl9/XvP/%2B%2Bxo7dqxcLpemTp2q8vJyu8duk7799lvl5uZq0KBByszMVH5%2Bvurr6yWR6dn66quv9Nvf/lapqakaNmyY1q5d69sj03M3ffp03Xvvvb7PS0tLNWnSJLlcLk2YMEH79%2B/3e/y2bds0YsQIuVwu5eTk6NixY3aP3Ga9/vrr%2BsUvfuH3kZubK4lcJQpWyFq6dKn279%2Bv9evXa/78%2BSooKNBrr70W7LHavPr6et11110qKyvzrVmWpZycHHXp0kVFRUUaN26cZs6cqSNHjkiSjhw5opycHGVnZ2vz5s1KSEjQ7bffrvb%2BSxAsy1Jubq68Xq%2Bee%2B45rVixQm%2B99ZZWrlxJpmepublZ06dPV3x8vF544QXdf//9evzxx7V161YyNeCVV17Rzp07fZ/X1dVp%2BvTpysjI0JYtW5SamqoZM2aorq5OkrRv3z7l5eVp5syZKiwsVG1trebMmROs8ducQ4cOafjw4dq1a5fvY/HixeT6PQsh5%2BTJk9bAgQOtDz74wLe2atUq68YbbwziVG1fWVmZdd1111nXXnut1adPH19%2B77//vpWSkmKdPHnS99hp06ZZjzzyiGVZlrVy5Uq/bOvq6qzU1FS//NujQ4cOWX369LE8Ho9vbevWrdbQoUPJ9Cx9%2B%2B231u9//3vr%2BPHjvrWcnBxr/vz5ZHqOqqurrauuusqaMGGCNXv2bMuyLGvTpk1WVlaW1dzcbFmWZTU3N1tXX321VVRUZFmWZd1zzz2%2Bx1qWZR05csT6xS9%2BYX399df2n0Ab9Mc//tF66KGHWqyT63e4ghWCDhw4oMbGRqWmpvrW0tPT5Xa71dzcHMTJ2raPPvpIV1xxhQoLC/3W3W63Lr30UsXGxvrW0tPTVVJS4tvPyMjw7cXExKh///6%2B/fYqMTFRa9euVZcuXfzWT5w4QaZnKSkpSStXrlSnTp1kWZaKi4u1Z88eDRo0iEzP0YMPPqhx48apd%2B/evjW326309HQ5HA5JksPhUFpa2k9m2rVrV3Xr1k1ut9ve4duoL774Qj179myxTq7foWCFII/Ho/j4eEVGRvrWunTpovr6etXU1ARxsrbt%2Buuv19y5cxUTE%2BO37vF4lJSU5LfWuXNnHT16tFX77VVcXJwyMzN9nzc3N%2BvZZ5/V4MGDydSArKwsXX/99UpNTdXIkSPJ9Bzs3r1bH3/8sW6//Xa/9TNlVllZSaY/wbIsHT58WLt27dLIkSM1YsQILV%2B%2BXA0NDeT6d85gD4Cfz%2Bv1%2BpUrSb7PGxoagjFSSPupPL/P8kz7%2BM6yZctUWlqqzZs3a926dWR6jh555BFVVVVpwYIFys/P58/pWaqvr9f8%2BfM1b948RUdH%2B%2B2dKbNTp06R6U84cuSIL7%2BVK1fqm2%2B%2B0eLFi3Xq1Cly/TsKVgiKiopq8Qfx%2B89/%2BA8IziwqKqrFlb%2BGhgZflj%2BVd1xcnG0ztnXLli3T%2BvXrtWLFCvXp04dMDRg4cKCk7wrC3XffrQkTJsjr9fo9hkzPrKCgQAMGDPC72vq9n8rsTJn%2B8Cp4e9S9e3d9%2BOGHuuCCC%2BRwONSvXz81Nzfrnnvu0aBBg8hVFKyQlJycrOrqajU2Nsrp/O4/ocfjUXR0dLv/x/RsJCcn69ChQ35rVVVVvkvYycnJqqqqarHfr18/22ZsyxYtWqTnn39ey5Yt08iRIyWR6dmqqqpSSUmJRowY4Vvr3bu3Tp8%2BrcTERH355ZctHk%2Bm/9wrr7yiqqoq33tWv/8f%2B44dOzR27NgfzexMmSYmJtowedt34YUX%2Bn3eq1cv1dfXKzExkVzFe7BCUr9%2B/eR0Ov3evFpcXKyBAweqQwf%2Bk/5cLpdLn332mU6dOuVbKy4ulsvl8u0XFxf79rxer0pLS3377VlBQYE2btyohx9%2BWGPGjPGtk%2BnZ%2BeabbzRz5kx9%2B%2B23vrX9%2B/crISFB6enpZHoWNmzYoK1bt%2BrFF1/Uiy%2B%2BqKysLGVlZenFF1%2BUy%2BXSJ5984ruVhWVZ2rt3709mWlFRoYqKinafqSS9%2B%2B67uuKKK/yuqn7%2B%2Bee68MILlZ6eTq4St2kIVX/605%2BsMWPGWG6323r99dettLQ0a8eOHcEeK2T8420aGhsbrdGjR1t33nmndfDgQWv16tVWSkqK9be//c2yLMsqLy%2B3Bg4caK1evdo6ePCg9fvf/9669tprfT%2BC3F4dOnTI6tevn7VixQqrsrLS74NMz05jY6OVnZ1t3XrrrVZZWZn19ttvW//%2B7/9urVu3jkwNmT17tu8WAcePH7cGDx5sLVq0yCorK7MWLVpkDRkyxHcrjL1791r9%2B/e3/vKXv1iff/65deONN1ozZswI5vhtxvHjx63MzEzrrrvusr744gvr7bfftoYOHWqtWbOGXP%2BOghWi6urqrFmzZlkpKSnW0KFDraeffjrYI4WUfyxYlmVZf/3rX60bbrjBGjBggDVmzBjrvffe83v822%2B/bf3qV7%2ByLrvsMmvatGlhd7%2BWs7F69WqrT58%2BP/phWWR6to4ePWrl5ORYaWlp1pAhQ6zHH3/cV5LI9Nz9Y8GyLMtyu93W%2BPHjrYEDB1oTJ060PvvsM7/HFxUVWb/85S%2BtlJQUKycnxzp27JjdI7dZBw8etG6%2B%2BWYrJSXFGjJkiPXoo4/6/qySq2U5LIvb/AIAAJjEG3YAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwLD/D6qaJVfwxbSPAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-2021821369331812041\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">8.05</td>\n",
       "        <td class=\"number\">60</td>\n",
       "        <td class=\"number\">4.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">13.0</td>\n",
       "        <td class=\"number\">59</td>\n",
       "        <td class=\"number\">4.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.75</td>\n",
       "        <td class=\"number\">55</td>\n",
       "        <td class=\"number\">4.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">26.0</td>\n",
       "        <td class=\"number\">50</td>\n",
       "        <td class=\"number\">3.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.8958</td>\n",
       "        <td class=\"number\">49</td>\n",
       "        <td class=\"number\">3.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">10.5</td>\n",
       "        <td class=\"number\">35</td>\n",
       "        <td class=\"number\">2.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.775</td>\n",
       "        <td class=\"number\">26</td>\n",
       "        <td class=\"number\">2.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.2292</td>\n",
       "        <td class=\"number\">24</td>\n",
       "        <td class=\"number\">1.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.925</td>\n",
       "        <td class=\"number\">23</td>\n",
       "        <td class=\"number\">1.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">26.55</td>\n",
       "        <td class=\"number\">22</td>\n",
       "        <td class=\"number\">1.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (271)</td>\n",
       "        <td class=\"number\">905</td>\n",
       "        <td class=\"number\">69.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-2021821369331812041\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">0.0</td>\n",
       "        <td class=\"number\">17</td>\n",
       "        <td class=\"number\">1.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3.1708</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4.0125</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">6.2375</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">227.525</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:71%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">247.5208</td>\n",
       "        <td class=\"number\">3</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:43%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">262.375</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">263.0</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:85%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">512.3292</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:57%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_cabin\">cabin<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"alert\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>187</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>14.3%</td>\n",
       "        </tr>\n",
       "        <tr class=\"alert\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>77.5%</td>\n",
       "        </tr>\n",
       "        <tr class=\"alert\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>1014</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable-5875232077832678797\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>C23 C25 C27</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.5%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        6\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>G6</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.4%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        5\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>B57 B59 B63 B66</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.4%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        5\n",
       "    </td>\n",
       "</tr><tr class=\"other\">\n",
       "    <th>Other values (183)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:28%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 21.3%\">\n",
       "            279\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"missing\">\n",
       "    <th>(Missing)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 77.5%\">\n",
       "            1014\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-5875232077832678797, #minifreqtable-5875232077832678797\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-5875232077832678797\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">C23 C25 C27</td>\n",
       "        <td class=\"number\">6</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">G6</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">B57 B59 B63 B66</td>\n",
       "        <td class=\"number\">5</td>\n",
       "        <td class=\"number\">0.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">C78</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">C22 C26</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">B96 B98</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">F33</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">F4</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">F2</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">D</td>\n",
       "        <td class=\"number\">4</td>\n",
       "        <td class=\"number\">0.3%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (176)</td>\n",
       "        <td class=\"number\">251</td>\n",
       "        <td class=\"number\">19.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"missing\">\n",
       "        <td class=\"fillremaining\">(Missing)</td>\n",
       "        <td class=\"number\">1014</td>\n",
       "        <td class=\"number\">77.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_embarked\">embarked<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>4</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>0.3%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>0.2%</td>\n",
       "        </tr>\n",
       "        <tr class=\"ignore\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>2</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable5821145551053426132\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>S</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 69.8%\">\n",
       "            914\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>C</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:30%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 20.6%\">\n",
       "            270\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>Q</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:14%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 9.4%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        123\n",
       "    </td>\n",
       "</tr><tr class=\"missing\">\n",
       "    <th>(Missing)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.2%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        2\n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable5821145551053426132, #minifreqtable5821145551053426132\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable5821145551053426132\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">S</td>\n",
       "        <td class=\"number\">914</td>\n",
       "        <td class=\"number\">69.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">C</td>\n",
       "        <td class=\"number\">270</td>\n",
       "        <td class=\"number\">20.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:30%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Q</td>\n",
       "        <td class=\"number\">123</td>\n",
       "        <td class=\"number\">9.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"missing\">\n",
       "        <td class=\"fillremaining\">(Missing)</td>\n",
       "        <td class=\"number\">2</td>\n",
       "        <td class=\"number\">0.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_boat\">boat<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>29</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>2.2%</td>\n",
       "        </tr>\n",
       "        <tr class=\"alert\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>62.9%</td>\n",
       "        </tr>\n",
       "        <tr class=\"alert\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>823</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable3328761826358527547\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>13</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:5%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 3.0%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        39\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>C</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:5%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 2.9%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        38\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>15</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:5%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 2.8%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        37\n",
       "    </td>\n",
       "</tr><tr class=\"other\">\n",
       "    <th>Other values (25)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:45%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 28.4%\">\n",
       "            372\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"missing\">\n",
       "    <th>(Missing)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 62.9%\">\n",
       "            823\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable3328761826358527547, #minifreqtable3328761826358527547\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable3328761826358527547\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">13</td>\n",
       "        <td class=\"number\">39</td>\n",
       "        <td class=\"number\">3.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">C</td>\n",
       "        <td class=\"number\">38</td>\n",
       "        <td class=\"number\">2.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">15</td>\n",
       "        <td class=\"number\">37</td>\n",
       "        <td class=\"number\">2.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14</td>\n",
       "        <td class=\"number\">33</td>\n",
       "        <td class=\"number\">2.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4</td>\n",
       "        <td class=\"number\">31</td>\n",
       "        <td class=\"number\">2.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">10</td>\n",
       "        <td class=\"number\">29</td>\n",
       "        <td class=\"number\">2.2%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">5</td>\n",
       "        <td class=\"number\">27</td>\n",
       "        <td class=\"number\">2.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">3</td>\n",
       "        <td class=\"number\">26</td>\n",
       "        <td class=\"number\">2.0%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9</td>\n",
       "        <td class=\"number\">25</td>\n",
       "        <td class=\"number\">1.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">11</td>\n",
       "        <td class=\"number\">25</td>\n",
       "        <td class=\"number\">1.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (18)</td>\n",
       "        <td class=\"number\">176</td>\n",
       "        <td class=\"number\">13.4%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"missing\">\n",
       "        <td class=\"fillremaining\">(Missing)</td>\n",
       "        <td class=\"number\">823</td>\n",
       "        <td class=\"number\">62.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_body\">body<br/>\n",
       "            <small>Numeric</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-6\">\n",
       "    <div class=\"row\">\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "                <tr>\n",
       "                    <th>Distinct count</th>\n",
       "                    <td>122</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Unique (%)</th>\n",
       "                    <td>9.3%</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Missing (%)</th>\n",
       "                    <td>90.8%</td>\n",
       "                </tr>\n",
       "                <tr class=\"alert\">\n",
       "                    <th>Missing (n)</th>\n",
       "                    <td>1188</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Infinite (n)</th>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "\n",
       "        </div>\n",
       "        <div class=\"col-sm-6\">\n",
       "            <table class=\"stats \">\n",
       "\n",
       "                <tr>\n",
       "                    <th>Mean</th>\n",
       "                    <td>160.81</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Minimum</th>\n",
       "                    <td>1</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <th>Maximum</th>\n",
       "                    <td>328</td>\n",
       "                </tr>\n",
       "                <tr class=\"ignore\">\n",
       "                    <th>Zeros (%)</th>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-3 collapse in\" id=\"minihistogram2551727992664658787\">\n",
       "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAAAS5JREFUeJzt2tFJBDEUQNGsWJJF2JPf9mQR21P8FuTiLsxONp7zPxAClxcec5lzzgH86uXsA8DKXs8%2BAD%2B9fXzd/M318/2AkzCGCQJJIBAEAkEgEAQCYYstls0PRzFBIAgEwhZPrHvc8yy71U7PuEfc1xjr3ZkJAkEgEJZ7Yj1qlP9n7vjvTBAIAoEgEAgCgSAQCMttsXZiW/T8TBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAI/ubdgL%2BGj2OCQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIhMucc559CFiVCQJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBALhGyzqGjWSaoRBAAAAAElFTkSuQmCC\">\n",
       "\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives2551727992664658787,#minihistogram2551727992664658787\"\n",
       "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"row collapse col-md-12\" id=\"descriptives2551727992664658787\">\n",
       "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
       "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles2551727992664658787\"\n",
       "                                                  aria-controls=\"quantiles2551727992664658787\" role=\"tab\"\n",
       "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#histogram2551727992664658787\" aria-controls=\"histogram2551727992664658787\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#common2551727992664658787\" aria-controls=\"common2551727992664658787\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
       "        <li role=\"presentation\"><a href=\"#extreme2551727992664658787\" aria-controls=\"extreme2551727992664658787\"\n",
       "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
       "\n",
       "    </ul>\n",
       "\n",
       "    <div class=\"tab-content\">\n",
       "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles2551727992664658787\">\n",
       "            <div class=\"col-md-4 col-md-offset-1\">\n",
       "                <p class=\"h4\">Quantile statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Minimum</th>\n",
       "                        <td>1</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>5-th percentile</th>\n",
       "                        <td>16</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q1</th>\n",
       "                        <td>72</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Median</th>\n",
       "                        <td>155</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Q3</th>\n",
       "                        <td>256</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>95-th percentile</th>\n",
       "                        <td>307</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Maximum</th>\n",
       "                        <td>328</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Range</th>\n",
       "                        <td>327</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Interquartile range</th>\n",
       "                        <td>184</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "            <div class=\"col-md-4 col-md-offset-2\">\n",
       "                <p class=\"h4\">Descriptive statistics</p>\n",
       "                <table class=\"stats indent\">\n",
       "                    <tr>\n",
       "                        <th>Standard deviation</th>\n",
       "                        <td>97.697</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Coef of variation</th>\n",
       "                        <td>0.60753</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Kurtosis</th>\n",
       "                        <td>-1.2541</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Mean</th>\n",
       "                        <td>160.81</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>MAD</th>\n",
       "                        <td>84.169</td>\n",
       "                    </tr>\n",
       "                    <tr class=\"\">\n",
       "                        <th>Skewness</th>\n",
       "                        <td>0.091739</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Sum</th>\n",
       "                        <td>19458</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Variance</th>\n",
       "                        <td>9544.7</td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th>Memory size</th>\n",
       "                        <td>10.4 KiB</td>\n",
       "                    </tr>\n",
       "                </table>\n",
       "            </div>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram2551727992664658787\">\n",
       "            <img 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77779L//%2B7/64osvlJGRYaN0AAAQIlwXsD755BPFxMSosLAwEK6%2B%2BOILtW/fXhEREZKkjh07qmPHjuc9d0REhGbNmqXHH39cgwcPVosWLTRz5kw1a9bM6nMAAAA1m%2BsC1tGjRzV06FDddNNNevjhhyVJY8aMUaNGjfSHP/xBTZs2Pa/5tm7dGnS7RYsWWrhwobV6AQBA6HHdeyJ///vfq0WLFho9enRgbPny5WratCmH8gAAwCXBdQFr3bp1euSRR9S4cePAWIMGDfTwww9r9erVDlYGAABwkusClsfj0eHDhyuNFxUVVfpsQQAAACe4LmDdcMMNmjJlir799tvA2O7du5WRkaHExEQHKwMAADjJdSe5T5w4UaNHj1afPn0UHR0tSTp8%2BLDat2%2BvRx991OHqAAAAXBiwGjZsqKVLl%2Brzzz9XXl6ePB6PWrdurW7dup3XBz4DAABcLK4LWNLJ61UlJiZySBAAAFySXBewfD6fZsyYoQ0bNuiHH36odGL73/72N4cqAwAAOMl1AevJJ5/U5s2b1b9/f11%2B%2BeVOlwMAAFCJ6wLW6tWrNW/ePHXu3NnpUgAAAKrkuss0REVFqWHDhk6XAQAAcEauC1gDBw7UvHnzVFZW5nQpAAAAVXLdIcLCwkItW7ZMK1eu1M9//nPVqlUr6P433njDocoAAABOcl3AkqTk5GSnSwAAADgj1wWsjIwMp0sAAAA4K9edgyVJBQUFysrK0oQJE/Tvf/9bf/nLX7Rjxw6nywIAAJDkwoC1a9cuDRgwQEuXLtWHH36o48ePa/ny5RoyZIhyc3OdLg8AAMB9AWvq1Knq1auXVqxYocsuu0ySNH36dCUlJWnatGkOVwcAAODCgLVhwwaNHj066IOdPR6P7rvvPm3ZssXBygAAAE5yXcDy%2B/3y%2B/2Vxo8dO6aIiAgHKgIAAAjmuoDVo0cPzZ07NyhkFRYWKjMzU127dnWwMgAAgJNcF7AeeeQRbd68WT169FBxcbHuvfde9ezZU3v27NHEiROdLg8AAMB918Fq0qSJ3n33XS1btkxfffWV/H6/hg4dqoEDB6pevXpOlwcAAOC%2BgCVJkZGRuu2225wuAwAAoEquC1gjR4486/18FiEAAHCa6wJW8%2BbNg26XlpZq165d2rZtm%2B68806HqgIAACjnuoB1ps8inDlzpvbv32/lMfbt26fJkydr7dq1ql%2B/vkaOHKlRo0ZZmRsAANR8rnsX4ZkMHDhQf/7zn63Mdf/99ysqKkrvvPOOHnvsMc2YMUMfffSRlbkBAEDNV2MC1saNG61caPT777/Xpk2bdO%2B996ply5bq1auXEhMTtWrVKgtVAgCAUOC6Q4RVneR%2B9OhRbd26VcOGDfvJ89epU0eRkZF65513NGHCBO3evVsbNmzQ/fff/5PnBgAAocF1AatZs2ZBn0MoSZdddpmGDx%2BuW2655SfPX7t2bU2aNEnp6el64403VFZWpsGDB3NZCAAAcM5cF7CmTp160R9j%2B/bt6tmzp0aPHq28vDylp6erW7du5xzgCgoK5PP5gsY8nih5vV4r9UVEhAd9B4DTeTzB2wi2FXCDU%2Bu2pnBdwFq7du05/%2Bx111133vOvWrVKb7/9tj7%2B%2BGPVqVNHsbGxOnDggGbPnn3OASs7O1tZWVlBY6mpqUpLSzvves4mOjrS6nwAaoaYmLpBt9lWwA0qrlu3c13AGjFiROAQoTEmMF5xLCwsTF999dV5z79582a1aNFCderUCYy1a9dOc%2BbMOec5UlJSlJSUFDTm8UTp0KFj511PVSIiwhUdHanDh4uszAegZjm1rTl9W1FW5ne4KuDsbL1GVuRUcHNdwJozZ46mTJmihx56SAkJCapVq5b%2B%2Bc9/6plnntGtt96qfv36/aT5vV6vdu3apZKSEtWqVUuStGPHDl1xxRXnNUfFw4E%2B3xGVltrdwLHBBFCVituasjK/9e0PYFtNW6OuO%2BCZkZGhSZMmqU%2BfPoqJiVHdunXVtWtXPfPMM1q0aJGaN28e%2BLoQSUlJuuyyy/TEE09o586d%2Bvvf/645c%2BZoxIgRlp8JAACoqVy3B6ugoKDK8FSvXj0dOnToJ89/%2BeWX67XXXtOzzz6rX/3qV2rQoIHuvfdepaSk/OS5AQBAaHBdwIqPj9f06dP13HPPqV69epKkwsJCZWZmqlu3blYeo3Xr1po/f76VuQAAQOhxXcB64oknNHLkSN1www1q2bKljDH65ptv1LhxY73xxhtOlwcAAOC%2BgHXVVVdp%2BfLlWrZsmbZv3y5JuuOOO9S/f39FRvJWZAAA4DzXBSxJ%2BtnPfqbbbrtNe/bs0c9//nNJJ6/mDgAAcClw3bsIjTGaNm2arrvuOiUnJ2v//v2aOHGiHn/8cf3www9OlwcAAOC%2BgLVgwQK99957euqppwLXqerVq5dWrFhR6erpAAAATnBdwMrOztakSZM0ePDgwNXb%2B/XrpylTpuiDDz5wuDoAAAAXBqw9e/aobdu2lcavvvrqSh%2BwDAAA4ATXBazmzZvrn//8Z6XxTz75JHDCOwAAgJNc9y7Cu%2B%2B%2BW08//bR8Pp%2BMMVq1apWys7O1YMECPfLII06XBwAA4L6ANWTIEJWWlmr27Nk6ceKEJk2apAYNGuj%2B%2B%2B/X0KFDnS4PAADAfQFr2bJl6tu3r1JSUvTdd9/JGKOGDRs6XRYAAECA687BeuaZZwInszdo0IBwBQAALjmuC1gtW7bUtm3bnC4DAADgjFx3iPDqq6/Wgw8%2BqHnz5qlly5aqXbt20P0ZGRkOVQYAAHCS6wLWzp071alTJ0niulcAAOCS5IqA9fzzz2vs2LGKiorSggULnC4HAADgrFxxDtb8%2BfNVVFQUNDZmzBgVFBQ4VBEAAMCZuSJgGWMqja1du1bFxcUOVAMAAHB2rghYAAAAbkLAAgAAsMw1ASssLMzpEgAAAM6JK95FKElTpkwJuubVDz/8oMzMTNWtWzfo57gOFgAAcJorAtZ1111X6ZpXHTp00KFDh3To0CGHqgIAAKiaKwIW174CAABu4ppzsAAAANyCgAUAAGAZAasKJSUlevrpp3Xdddfp%2Buuv1/Tp06u82CkAAEBVXHEOVnWbMmWKcnJy9Morr%2BjYsWN64IEH1KxZM/361792ujQAAOAC7MGqoLCwUEuWLFF6erquvfZadevWTXfddZdyc3OdLg0AALgEe7AqWL9%2BverVq6eEhITA2JgxYxysCAAAuA17sCrYvXu3mjdvrnfffVd9%2B/bVTTfdpJkzZ8rv9ztdGgAAcAn2YFVw/Phx7dq1S4sXL1ZGRoZ8Pp8mTZqkyMhI3XXXXec0R0FBQaULo3o8UfJ6vVZqjIgID/oOAKfzeIK3EWwr4Aan1m1NQcCqwOPx6OjRo3rhhRfUvHlzSdLevXu1aNGicw5Y2dnZysrKChpLTU1VWlqa1VqjoyOtzgegZoiJCf4IMbYVcIOK69btCFgVNG7cWLVr1w6EK0m68sortW/fvnOeIyUlRUlJSUFjHk%2BUDh06ZqXGiIhwRUdH6vDhIivzAahZTm1rTt9WlJVxmgMubbZeIytyKrgRsCqIi4tTcXGxdu7cqSuvvFKStGPHjqDA9WO8Xm%2Blw4E%2B3xGVltrdwLHBBFCVituasjK/9e0PYFtNW6M164CnBa1atdKNN96oRx99VF9//bU%2B/fRTvfzyyxo6dKjTpQEAAJdgD1YVpk2bpvT0dA0dOlSRkZG64447NGLECKfLAgAALkHAqsLll1%2Bu559/3ukyAACAS3GIEAAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAs8zhdAADArv834x9OlwCEPPZgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAtaPGDNmjB555BGnywAAAC5CwDqLP/3pT/r444%2BdLgMAALgMAesMCgsL9fzzzys2NtbpUgAAgMt4nC7gUvXcc89p4MCBKigocLoUAADgMuzBqsKqVau0bt063XfffU6XAgAAXIg9WBUUFxfrqaee0qRJk1SnTp0LmqOgoEA%2Bny9ozOOJktfrtVGiIiLCg74DAOB2Hk/Nek0jYFWQlZWla665RomJiRc8R3Z2trKysoLGUlNTlZaW9lPLCxIdHWl1PgAAnBITU9fpEqwKM8YYp4u4lCQlJengwYOKiIiQJJWUlEiSatWqpY0bN57THNWxBys6OlKHDxcp6Tne5QgAcL%2BPHrzwHRtn41RwYw9WBQsWLFBpaWng9rRp0yRJDz744DnP4fV6K4Upn%2B%2BISkv9dor8P2VlducDAMAptl8jnUbAqqB58%2BZBt%2BvWPZl8W7Ro4UQ5AADAhWrWGWUAAACXAPZg/YipU6c6XQIAAHAZ9mABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIBVhQMHDigtLU0JCQlKTExURkaGiouLnS4LAAC4hMfpAi41xhilpaUpOjpab775pr7//ns99thjCg8P18SJE50uDwAAuAB7sCrYsWOHNm3apIyMDP3iF79Q586dlZaWpmXLljldGgAAcAkCVgWNGzfWvHnz1KhRo6Dxo0ePOlQRAABwGwJWBdHR0UpMTAzc9vv9Wrhwobp27epgVQAAwE04B%2BtHZGZmasuWLXr77bfP%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%2BeW3evFmvv/669u7dq4kTJ6pZs2bq27ev06UBAAAXIGBVcPz4cb311lv6wx/%2BoPbt26t9%2B/bKy8vTm2%2B%2BScACAADnhEOEFXz99dcqLS1Vhw4dAmOdOnVSbm6u/H6/g5UBAAC3YA9WBT6fTzExMapVq1ZgrFGjRiouLlZhYaEaNGjwo3MUFBTI5/MFjXk8UfJ6vVZqjIgID/oOAIDbeTw16zWNgFVBUVFRULiSFLhdUlJyTnNkZ2crKysraGzs2LEaN26clRoLCgr0%2BuvzlJKSonXPhvZhy4KCAmVnZyslJcVagHUrelGOXpxEH8rRi3L0onrUrLhoQe3atSsFqVO369Spc05zpKSk6J133gn6SklJsVajz%2BdTVlZWpb1koYhelKMX5ejFSfShHL0oRy%2BqB3uwKmjSpIkOHTqk0tJSeTwn2%2BPz%2BVSnTh1FR0ef0xxer5d/FQAAEMLYg1VB27Zt5fF4tGnTpsDY%2BvXrFRsbq/Bw2gUAAH4ciaGCyMhIDRo0SJMnT9YXX3yhFStW6NVXX9XIkSOdLg0AALhExOTJkyc7XcSlpmvXrtqyZYteeOEFrVq1Sv/5n/%2BpIUOGOF1WkLp16yohIUF169Z1uhTH0Yty9KIcvTiJPpSjF%2BXoxcUXZowxThcBAABQk3CIEAAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAGaORZPAAAJG0lEQVQAAFhGwAIAALCMgAUAAGAZActliouL9dhjj6lz587q0aOHXn31VadLqjYfffSRfvnLXwZ9paWlSZK2bNmi2267TXFxcRoyZIg2b97scLUXR0lJiZKTk5WTkxMY2717t0aNGqX4%2BHj169dPn332WdDvfP7550pOTlZcXJxGjhyp3bt3V3fZF0VVvZgyZUqlNbJw4cLA/cuWLVOvXr0UFxen1NRUfffdd06UbsWBAweUlpamhIQEJSYmKiMjQ8XFxZJCb02crRehtCYkadeuXbr77rvVoUMH3XjjjZo3b17gvlBbF44zcJVnnnnGDBgwwGzevNn89a9/NR06dDB//vOfnS6rWsyaNcvcc889pqCgIPD1/fffm2PHjpnu3bubqVOnmvz8fJOenm6uv/56c%2BzYMadLturEiRMmNTXVtGnTxqxevdoYY4zf7zcDBgwwEyZMMPn5%2BWbOnDkmLi7O/Otf/zLGGPOvf/3LxMfHm1deecVs27bN/O53vzPJycnG7/c7%2BVR%2Bsqp6YYwxo0aNMnPnzg1aI8ePHzfGGJObm2uuvfZas3TpUvPVV1%2BZ4cOHmzFjxjj1FH4Sv99vbr/9dvOb3/zGbNu2zaxdu9b07t3bTJ06NeTWxNl6YUzorAljjCkrKzM333yzmTBhgtm5c6dZuXKl6dixo3n//fdDbl1cCghYLnLs2DETGxsb9IIyc%2BZMM3z4cAerqj4TJkwwL7zwQqXxt956yyQlJQU2BH6/3/Tu3dssWbKkuku8aPLy8swtt9xiBgwYEBQqPv/8cxMfHx8UJu%2B8807z0ksvGWOMmTFjRtD6OH78uOnQoUPQGnKbM/XCGGMSExPNp59%2BWuXvPfTQQ2bixImB23v37jW//OUvzbfffnvRa7YtPz/ftGnTxvh8vsDYBx98YHr06BFya%2BJsvTAmdNaEMcYcOHDA/O53vzNHjhwJjKWmppqnnnoq5NbFpYBDhC7y9ddfq7S0VB06dAiMderUSbm5ufL7/Q5WVj22b9%2Buli1bVhrPzc1Vp06dFBYWJkkKCwtTx44dtWnTpmqu8OJZs2aNunTpouzs7KDx3NxctWvXTlFRUYGxTp06BZ57bm6uOnfuHLgvMjJS7du3d3VvztSLo0eP6sCBA1WuEalyL5o2bapmzZopNzf3YpZ7UTRu3Fjz5s1To0aNgsaPHj0acmvibL0IpTUhSV6vVzNmzFC9evVkjNH69eu1du1aJSQkhNy6uBR4nC4A587n8ykmJka1atUKjDVq1EjFxcUqLCxUgwYNHKzu4jLGaOfOnfrss880d%2B5clZWVqW/fvkpLS5PP51Pr1q2Dfr5hw4bKy8tzqFr7hg0bVuW4z%2BeT1%2BsNGmvYsKH2799/Tve70Zl6sX37doWFhWnOnDn65JNPVL9%2BfY0ePVq33nqrJKmgoKDG9CI6OlqJiYmB236/XwsXLlTXrl1Dbk2crRehtCYqSkpK0t69e9WzZ0/16dNHv//970NqXVwKCFguUlRUFBSuJAVul5SUOFFStdm7d2/g%2Bc%2BYMUN79uzRlClTdOLEiTP2pab3RDrzmjj13EOpNzt27FBYWJhatWql4cOHa%2B3atXryySdVr1499e7dWydOnKixvcjMzNSWLVv09ttv67XXXgvpNXF6L7788suQXRMvvfSSDh48qMmTJysjI4NthQMIWC5Su3btSov91O06deo4UVK1ad68uXJycvSzn/1MYWFhatu2rfx%2Bvx566CElJCRU2Zea3hPp5JooLCwMGjv9uZ9pzURHR1dbjdVl0KBB6tmzp%2BrXry9Juvrqq/XNN99o0aJF6t279xl7ERkZ6US51mRmZur111/Xiy%2B%2BqDZt2oT0mqjYi1/84hchuSYkKTY2VtLJd54/%2BOCDGjJkiIqKioJ%2BJlTWhVM4B8tFmjRpokOHDqm0tDQw5vP5VKdOnZD4I6hfv37gPCtJuuqqq1RcXKzGjRvr4MGDQT978ODBSru7a6ImTZqc9bmf6f7GjRtXW43VJSwsLPBCekqrVq104MABSTWzF%2Bnp6Zo/f74yMzPVp08fSaG7JqrqRaitiYMHD2rFihVBY61bt9YPP/zwo9vJmtaLSwEBy0Xatm0rj8cTdNLh%2BvXrFRsbq/Dwmv2/8tNPP1WXLl2C/gX21VdfqX79%2BurUqZM2btwoY4ykk%2BdrbdiwQXFxcU6VW23i4uL05Zdf6sSJE4Gx9evXB557XFyc1q9fH7ivqKhIW7ZsqZG9%2Ba//%2Bi%2BNGjUqaOzrr79Wq1atJFXuxb59%2B7Rv3z7X9iIrK0uLFy/W9OnT1b9//8B4KK6JM/Ui1NbEnj17NHbs2ECAlKTNmzerQYMG6tSpU8itC8c5%2ByZGnK8nn3zS9O/f3%2BTm5pqPPvrIdOzY0Xz44YdOl3XRHTlyxCQmJprx48eb7du3m5UrV5oePXqYl19%2B2Rw5csR07drVpKenm7y8PJOenm66d%2B9e466DdcrplyYoLS01/fr1M/fff7/Ztm2bmTt3romPjw9c22b37t0mNjbWzJ07N3BtmwEDBtSYa9uc3ovc3FzTrl07M2/ePLNr1y7z5ptvmmuuucZs2LDBGGPMhg0bTPv27c0f//jHwDWP7rnnHifLv2D5%2Bfmmbdu25sUXXwy6vlNBQUHIrYmz9SKU1oQxJ7cHgwcPNnfddZfJy8szK1euNNdff7157bXXQm5dXAoIWC5z/Phx8/DDD5v4%2BHjTo0cPM3/%2BfKdLqjbbtm0zo0aNMvHx8aZ79%2B7mv//7vwN//Lm5uWbQoEEmNjbW/OpXvzJffvmlw9VePBWv/fTNN9%2BYO%2B64w1xzzTWmf//%2B5h//%2BEfQz69cudLcfPPN5tprrzV33nmna6/xU5WKvfjoo4/MgAEDTGxsrOnbt2%2Blf3wsWbLE/Md//IeJj483qamp5rvvvqvukq2YO3euadOmTZVfxoTWmvixXoTKmjhl//79JjU11XTs2NF0797dzJ49O7CdDKV1cSkIM%2Bb/jqsAAADAipp94g4AAIADCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsOz/A3IA9lZG3CgZAAAAAElFTkSuQmCC\"/>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common2551727992664658787\">\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">142.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">68.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">47.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">98.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">188.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">120.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">156.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">37.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">58.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">70.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (111)</td>\n",
       "        <td class=\"number\">111</td>\n",
       "        <td class=\"number\">8.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"missing\">\n",
       "        <td class=\"fillremaining\">(Missing)</td>\n",
       "        <td class=\"number\">1188</td>\n",
       "        <td class=\"number\">90.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme2551727992664658787\">\n",
       "            <p class=\"h4\">Minimum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">1.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">4.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">7.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">9.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">14.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "            <p class=\"h4\">Maximum 5 values</p>\n",
       "            \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">312.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">314.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">322.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">327.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">328.0</td>\n",
       "        <td class=\"number\">1</td>\n",
       "        <td class=\"number\">0.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "        </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div><div class=\"row variablerow\">\n",
       "    <div class=\"col-md-3 namecol\">\n",
       "        <p class=\"h4 pp-anchor\" id=\"pp_var_home.dest\">home.dest<br/>\n",
       "            <small>Categorical</small>\n",
       "        </p>\n",
       "    </div><div class=\"col-md-3\">\n",
       "    <table class=\"stats \">\n",
       "        <tr class=\"alert\">\n",
       "            <th>Distinct count</th>\n",
       "            <td>370</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <th>Unique (%)</th>\n",
       "            <td>28.3%</td>\n",
       "        </tr>\n",
       "        <tr class=\"alert\">\n",
       "            <th>Missing (%)</th>\n",
       "            <td>43.1%</td>\n",
       "        </tr>\n",
       "        <tr class=\"alert\">\n",
       "            <th>Missing (n)</th>\n",
       "            <td>564</td>\n",
       "        </tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-6 collapse in\" id=\"minifreqtable-7479712759773974881\">\n",
       "    <table class=\"mini freq\">\n",
       "        <tr class=\"\">\n",
       "    <th>New York, NY</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:10%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 4.9%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        64\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>London</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:3%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 1.1%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        14\n",
       "    </td>\n",
       "</tr><tr class=\"\">\n",
       "    <th>Montreal, PQ</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:2%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 0.8%\">\n",
       "            &nbsp;\n",
       "        </div>\n",
       "        10\n",
       "    </td>\n",
       "</tr><tr class=\"other\">\n",
       "    <th>Other values (366)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 50.2%\">\n",
       "            657\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr><tr class=\"missing\">\n",
       "    <th>(Missing)</th>\n",
       "    <td>\n",
       "        <div class=\"bar\" style=\"width:85%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
       "             data-delay=500 title=\"Percentage: 43.1%\">\n",
       "            564\n",
       "        </div>\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "    </table>\n",
       "</div>\n",
       "<div class=\"col-md-12 text-right\">\n",
       "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-7479712759773974881, #minifreqtable-7479712759773974881\"\n",
       "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
       "        Toggle details\n",
       "    </a>\n",
       "</div>\n",
       "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-7479712759773974881\">\n",
       "    \n",
       "<table class=\"freq table table-hover\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        <td class=\"fillremaining\">Value</td>\n",
       "        <td class=\"number\">Count</td>\n",
       "        <td class=\"number\">Frequency (%)</td>\n",
       "        <td style=\"min-width:200px\">&nbsp;</td>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tr class=\"\">\n",
       "        <td class=\"fillremaining\">New York, NY</td>\n",
       "        <td class=\"number\">64</td>\n",
       "        <td class=\"number\">4.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">London</td>\n",
       "        <td class=\"number\">14</td>\n",
       "        <td class=\"number\">1.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Montreal, PQ</td>\n",
       "        <td class=\"number\">10</td>\n",
       "        <td class=\"number\">0.8%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Paris, France</td>\n",
       "        <td class=\"number\">9</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Cornwall / Akron, OH</td>\n",
       "        <td class=\"number\">9</td>\n",
       "        <td class=\"number\">0.7%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Philadelphia, PA</td>\n",
       "        <td class=\"number\">8</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Wiltshire, England Niagara Falls, NY</td>\n",
       "        <td class=\"number\">8</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Winnipeg, MB</td>\n",
       "        <td class=\"number\">8</td>\n",
       "        <td class=\"number\">0.6%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Belfast</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"\">\n",
       "        <td class=\"fillremaining\">Brooklyn, NY</td>\n",
       "        <td class=\"number\">7</td>\n",
       "        <td class=\"number\">0.5%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"other\">\n",
       "        <td class=\"fillremaining\">Other values (359)</td>\n",
       "        <td class=\"number\">601</td>\n",
       "        <td class=\"number\">45.9%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr><tr class=\"missing\">\n",
       "        <td class=\"fillremaining\">(Missing)</td>\n",
       "        <td class=\"number\">564</td>\n",
       "        <td class=\"number\">43.1%</td>\n",
       "        <td>\n",
       "            <div class=\"bar\" style=\"width:93%\">&nbsp;</div>\n",
       "        </td>\n",
       "</tr>\n",
       "</table>\n",
       "</div>\n",
       "</div>\n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Correlations</h1>\n",
       "    </div>\n",
       "    \n",
       "    <div class=\"row headerrow highlight\">\n",
       "        <h1>Sample</h1>\n",
       "    </div>\n",
       "    <div class=\"row variablerow\">\n",
       "    <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
       "        <table border=\"1\" class=\"dataframe sample\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "    </div>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "<pandas_profiling.ProfileReport at 0x10c5a7358>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas_profiling\n",
    "pandas_profiling.ProfileReport(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 14)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.381971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.837836</td>\n",
       "      <td>0.486055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pclass     survived\n",
       "count  1309.000000  1309.000000\n",
       "mean      2.294882     0.381971\n",
       "std       0.837836     0.486055\n",
       "min       1.000000     0.000000\n",
       "25%       2.000000     0.000000\n",
       "50%       3.000000     0.000000\n",
       "75%       3.000000     1.000000\n",
       "max       3.000000     1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().iloc[:, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass          0\n",
       "survived        0\n",
       "name            0\n",
       "sex             0\n",
       "age           263\n",
       "sibsp           0\n",
       "parch           0\n",
       "ticket          0\n",
       "fare            1\n",
       "cabin        1014\n",
       "embarked        2\n",
       "boat          823\n",
       "body         1188\n",
       "home.dest     564\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     1\n",
       "2     2\n",
       "3     1\n",
       "4     2\n",
       "5     1\n",
       "6     1\n",
       "7     2\n",
       "8     1\n",
       "9     2\n",
       "10    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum(axis=1).loc[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = df.isnull().any(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    True\n",
       "1    True\n",
       "2    True\n",
       "3    True\n",
       "4    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask.head()  # rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      NaN\n",
       "1      NaN\n",
       "2      NaN\n",
       "3    135.0\n",
       "4      NaN\n",
       "Name: body, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[mask].body.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      843\n",
       "female    466\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S      914\n",
       "C      270\n",
       "Q      123\n",
       "NaN      2\n",
       "Name: embarked, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.embarked.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Allen, Miss. Elisabeth Walton\n",
       "1    Allison, Master. Hudson Trevor\n",
       "2      Allison, Miss. Helen Loraine\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = df.name\n",
    "name.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\n",
    "    columns=[\n",
    "        \"name\",\n",
    "        \"ticket\",\n",
    "        \"home.dest\",\n",
    "        \"boat\",\n",
    "        \"body\",\n",
    "        \"cabin\",\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pclass', 'survived', 'age', 'sibsp', 'parch', 'fare', 'sex_female',\n",
       "       'sex_male', 'embarked_C', 'embarked_Q', 'embarked_S'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=\"sex_male\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pclass', 'survived', 'age', 'sibsp', 'parch', 'fare', 'sex_female',\n",
       "       'embarked_C', 'embarked_Q', 'embarked_S'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.survived\n",
    "X = df.drop(columns=\"survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import janitor as jn\n",
    "X, y = jn.get_features_targets(\n",
    "    df, target_columns=\"survived\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(\n",
    "    X, y, test_size=0.3, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pclass', 'age', 'sibsp', 'parch', 'fare', 'sex_female', 'embarked_C',\n",
       "       'embarked_Q', 'embarked_S'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.experimental import (\n",
    "    enable_iterative_imputer,\n",
    ")\n",
    "from sklearn import impute\n",
    "num_cols = [\n",
    "    \"pclass\",\n",
    "    \"age\",\n",
    "    \"sibsp\",\n",
    "    \"parch\",\n",
    "    \"fare\",\n",
    "    \"sex_female\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/pandas/core/indexing.py:494: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/pandas/core/indexing.py:494: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "imputer = impute.IterativeImputer()\n",
    "imputed = imputer.fit_transform(\n",
    "    X_train[num_cols]\n",
    ")\n",
    "X_train.loc[:, num_cols] = imputed\n",
    "imputed = imputer.transform(X_test[num_cols])\n",
    "X_test.loc[:, num_cols] = imputed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "meds = X_train.median()\n",
    "X_train = X_train.fillna(meds)\n",
    "X_test = X_test.fillna(meds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "cols = ['pclass', 'age', 'sibsp', 'parch', 'fare', 'sex_female', 'embarked_C',\n",
    "   'embarked_Q', 'embarked_S']\n",
    "sca = preprocessing.StandardScaler()\n",
    "X_train = sca.fit_transform(X_train)\n",
    "X_train = pd.DataFrame(X_train, columns=cols)\n",
    "X_test = sca.transform(X_test)\n",
    "X_test = pd.DataFrame(X_test, columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "def tweak_titanic(df):\n",
    "    df = df.drop(\n",
    "        columns=[\n",
    "            \"name\",\n",
    "            \"ticket\",\n",
    "            \"home.dest\",\n",
    "            \"boat\",\n",
    "            \"body\",\n",
    "            \"cabin\",\n",
    "        ]\n",
    "    ).pipe(pd.get_dummies, drop_first=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_train_test_X_y(\n",
    "    df, y_col, size=0.3, std_cols=None\n",
    "):\n",
    "    y = df[y_col]\n",
    "    X = df.drop(columns=y_col)\n",
    "    X_train, X_test, y_train, y_test = model_selection.train_test_split(\n",
    "        X, y, test_size=size, random_state=42\n",
    "    )\n",
    "    cols = X.columns\n",
    "    num_cols = [\n",
    "        \"pclass\",\n",
    "        \"age\",\n",
    "        \"sibsp\",\n",
    "        \"parch\",\n",
    "        \"fare\",\n",
    "    ]\n",
    "    fi = impute.IterativeImputer()\n",
    "    fitted = fi.fit_transform(X_train[num_cols])\n",
    "    X_train = X_train.assign(**{c:fitted[:,i] for i, c in enumerate(num_cols)})\n",
    "    test_fit = fi.transform(X_test[num_cols])\n",
    "    X_test = X_test.assign(**{c:test_fit[:,i] for i, c in enumerate(num_cols)})\n",
    "    if std_cols:\n",
    "        std = preprocessing.StandardScaler()\n",
    "        fitted = std.fit_transform(X_train[std_cols])\n",
    "        X_train = X_train.assign(**{c:fitted[:,i] for i, c in enumerate(std_cols)})\n",
    "        test_fit = std.transform(X_test[std_cols])\n",
    "        X_test = X_test.assign(**{c:test_fit[:,i] for i, c in enumerate(std_cols)})\n",
    "\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ti_df = tweak_titanic(orig_df)\n",
    "std_cols = \"pclass,age,sibsp,fare\".split(\",\")\n",
    "X_train, X_test, y_train, y_test = get_train_test_X_y(\n",
    "    ti_df, \"survived\", std_cols=std_cols\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48346055979643765"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "bm = DummyClassifier()\n",
    "bm.fit(X_train, y_train)\n",
    "bm.score(X_test, y_test)  # accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4896551724137931"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "metrics.precision_score(\n",
    "    y_test, bm.predict(X_test)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([X_train, X_test])\n",
    "y = pd.concat([y_train, y_test])\n",
    "from sklearn import model_selection\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.linear_model import (\n",
    "    LogisticRegression,\n",
    ")\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import (\n",
    "    KNeighborsClassifier,\n",
    ")\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import (\n",
    "    RandomForestClassifier,\n",
    ")\n",
    "import xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DummyClassifier         AUC: 0.516 STD: 0.04\n",
      "LogisticRegression      AUC: 0.843 STD: 0.03\n",
      "DecisionTreeClassifier  AUC: 0.761 STD: 0.03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier    AUC: 0.830 STD: 0.05\n",
      "GaussianNB              AUC: 0.817 STD: 0.04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC                     AUC: 0.838 STD: 0.05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier  AUC: 0.828 STD: 0.03\n",
      "XGBClassifier           AUC: 0.863 STD: 0.04\n"
     ]
    }
   ],
   "source": [
    "for model in [\n",
    "    DummyClassifier,\n",
    "    LogisticRegression,\n",
    "    DecisionTreeClassifier,\n",
    "    KNeighborsClassifier,\n",
    "    GaussianNB,\n",
    "    SVC,\n",
    "    RandomForestClassifier,\n",
    "    xgboost.XGBClassifier,\n",
    "]:\n",
    "    cls = model()\n",
    "    kfold = model_selection.KFold(\n",
    "        n_splits=10, random_state=42\n",
    "    )\n",
    "    s = model_selection.cross_val_score(\n",
    "        cls, X, y, scoring=\"roc_auc\", cv=kfold\n",
    "    )\n",
    "    print(\n",
    "        f\"{model.__name__:22}  AUC: \"\n",
    "        f\"{s.mean():.3f} STD: {s.std():.2f}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StackingClassifier  AUC: 0.803  STD: 0.05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from mlxtend.classifier import (\n",
    "    StackingClassifier,\n",
    ")\n",
    "clfs = [\n",
    "    x()\n",
    "    for x in [\n",
    "        LogisticRegression,\n",
    "        DecisionTreeClassifier,\n",
    "        KNeighborsClassifier,\n",
    "        GaussianNB,\n",
    "        SVC,\n",
    "        RandomForestClassifier,\n",
    "    ]\n",
    "]\n",
    "stack = StackingClassifier(\n",
    "    classifiers=clfs,\n",
    "    meta_classifier=LogisticRegression(),\n",
    ")\n",
    "kfold = model_selection.KFold(\n",
    "    n_splits=10, random_state=42\n",
    ")\n",
    "s = model_selection.cross_val_score(\n",
    "    stack, X, y, scoring=\"roc_auc\", cv=kfold\n",
    ")\n",
    "print(\n",
    "    f\"{stack.__class__.__name__}  \"\n",
    "    f\"AUC: {s.mean():.3f}  STD: {s.std():.2f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = ensemble.RandomForestClassifier(\n",
    "    n_estimators=100, random_state=42\n",
    ")\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7811704834605598"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7862068965517242"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.precision_score(\n",
    "    y_test, rf.predict(X_test)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age            0.285\n",
      "fare           0.262\n",
      "sex_male       0.241\n",
      "pclass         0.089\n",
      "sibsp          0.050\n"
     ]
    }
   ],
   "source": [
    "for col, val in sorted(\n",
    "    zip(\n",
    "        X_train.columns,\n",
    "        rf.feature_importances_,\n",
    "    ),\n",
    "    key=lambda x: x[1],\n",
    "    reverse=True,\n",
    ")[:5]:\n",
    "    print(f\"{col:10}{val:10.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_features': 'auto', 'min_samples_leaf': 0.1, 'n_estimators': 15, 'random_state': 42}\n"
     ]
    }
   ],
   "source": [
    "rf4 = ensemble.RandomForestClassifier()\n",
    "params = {\n",
    "    \"max_features\": [0.4, \"auto\"],\n",
    "    \"n_estimators\": [15, 200],\n",
    "    \"min_samples_leaf\": [1, 0.1],\n",
    "    \"random_state\": [42],\n",
    "}\n",
    "cv = model_selection.GridSearchCV(\n",
    "    rf4, params, n_jobs=-1\n",
    ").fit(X_train, y_train)\n",
    "print(cv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7073791348600509"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf5 = ensemble.RandomForestClassifier(\n",
    "    **{\n",
    "        \"max_features\": \"auto\",\n",
    "        \"min_samples_leaf\": 0.1,\n",
    "        \"n_estimators\": 200,\n",
    "        \"random_state\": 42,\n",
    "    }\n",
    ")\n",
    "rf5.fit(X_train, y_train)\n",
    "rf5.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[217,   7],\n",
       "       [108,  61]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = rf5.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/metrics/classification.py:182: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  score = y_true == y_pred\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mapping = {0: \"died\", 1: \"survived\"}\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "cm_viz = ConfusionMatrix(\n",
    "    rf5,\n",
    "    classes=[\"died\", \"survived\"],\n",
    "    label_encoder=mapping,\n",
    ")\n",
    "cm_viz.score(X_test, y_test)\n",
    "cm_viz.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6648483727810651"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf5.predict(X_test)\n",
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "roc_viz = ROCAUC(rf5)\n",
    "roc_viz.score(X_test, y_test)\n",
    "roc_viz.poof()\n",
    "#fig.savefig(\"images/mlpr_0305.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "cv = StratifiedKFold(12)\n",
    "sizes = np.linspace(0.3, 1.0, 10)\n",
    "lc_viz = LearningCurve(\n",
    "    rf5,\n",
    "    cv=cv,\n",
    "    train_sizes=sizes,\n",
    "    scoring=\"f1_weighted\",\n",
    "    n_jobs=4,\n",
    "    ax=ax,\n",
    ")\n",
    "lc_viz.fit(X, y)\n",
    "lc_viz.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6648483727810651"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "pic = pickle.dumps(rf5)\n",
    "rf6 = pickle.loads(pic)\n",
    "y_pred = rf6.predict(X_test)\n",
    "roc_auc_score(y_test, y_pred)"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light"
  },
  "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}