{"cells":[{"cell_type":"markdown","metadata":{"id":"Vkf1B-vMwpVB"},"source":["# Basic Plots, Groupping and Multi-Level Tables"]},{"cell_type":"markdown","metadata":{"id":"fpPtZgqIvuXz"},"source":["Inspiration and some of the parts came from: Python Data Science [GitHub repository](https://github.com/jakevdp/PythonDataScienceHandbook/tree/master), [MIT License](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/LICENSE-CODE) and [Introduction to Pandas](https://colab.research.google.com/notebooks/mlcc/intro_to_pandas.ipynb) by Google, [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)\n","\n","If running this from Google Colab, uncomment the cell below and run it. Otherwise, just skip it."]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11192,"status":"ok","timestamp":1692083921583,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"5saSBc40voZF","outputId":"f9b23c3f-c297-441d-9e00-708f8278bd5c"},"outputs":[],"source":["#!pip install seaborn\n","#!pip install watermark\n","#!pip install pivottablejs"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import seaborn as sns\n","\n","# For generating a pivot table widget\n","from pivottablejs import pivot_ui\n","from IPython.display import HTML\n","from IPython.display import IFrame\n","import json, io"]},{"cell_type":"markdown","metadata":{"id":"ZkUd2sa-yP5e"},"source":["## Learning Objectives:\n","\n","\n"," * Simple plotting from *DataFrame*\n","\n"," * split-apply-combine on tidy data\n","\n"," * Pivot tables\n","\n"," For this notebook, we will use the california housing dataframes."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":500,"status":"ok","timestamp":1692083888391,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"av6RYOraVG1V","outputId":"fc356d6e-ae0d-4444-8586-b7f85a149bc2"},"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>longitude</th>\n","      <th>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>total_bedrooms</th>\n","      <th>population</th>\n","      <th>households</th>\n","      <th>median_income</th>\n","      <th>median_house_value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-114.31</td>\n","      <td>34.19</td>\n","      <td>15.0</td>\n","      <td>5612.0</td>\n","      <td>1283.0</td>\n","      <td>1015.0</td>\n","      <td>472.0</td>\n","      <td>1.4936</td>\n","      <td>66900.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-114.47</td>\n","      <td>34.40</td>\n","      <td>19.0</td>\n","      <td>7650.0</td>\n","      <td>1901.0</td>\n","      <td>1129.0</td>\n","      <td>463.0</td>\n","      <td>1.8200</td>\n","      <td>80100.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>-114.56</td>\n","      <td>33.69</td>\n","      <td>17.0</td>\n","      <td>720.0</td>\n","      <td>174.0</td>\n","      <td>333.0</td>\n","      <td>117.0</td>\n","      <td>1.6509</td>\n","      <td>85700.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-114.57</td>\n","      <td>33.64</td>\n","      <td>14.0</td>\n","      <td>1501.0</td>\n","      <td>337.0</td>\n","      <td>515.0</td>\n","      <td>226.0</td>\n","      <td>3.1917</td>\n","      <td>73400.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-114.57</td>\n","      <td>33.57</td>\n","      <td>20.0</td>\n","      <td>1454.0</td>\n","      <td>326.0</td>\n","      <td>624.0</td>\n","      <td>262.0</td>\n","      <td>1.9250</td>\n","      <td>65500.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n","0    -114.31     34.19                15.0       5612.0          1283.0   \n","1    -114.47     34.40                19.0       7650.0          1901.0   \n","2    -114.56     33.69                17.0        720.0           174.0   \n","3    -114.57     33.64                14.0       1501.0           337.0   \n","4    -114.57     33.57                20.0       1454.0           326.0   \n","\n","   population  households  median_income  median_house_value  \n","0      1015.0       472.0         1.4936             66900.0  \n","1      1129.0       463.0         1.8200             80100.0  \n","2       333.0       117.0         1.6509             85700.0  \n","3       515.0       226.0         3.1917             73400.0  \n","4       624.0       262.0         1.9250             65500.0  "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n","california_housing_dataframe.head()"]},{"cell_type":"markdown","metadata":{"id":"Hg5U--9k39nj"},"source":["## Simple Plotting"]},{"cell_type":"markdown","metadata":{"id":"WrkBjfz5kEQu"},"source":["The example below is using `DataFrame.describe` to show interesting statistics about a `DataFrame`."]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1692083890073,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"YDpHvq6v5m9Z","outputId":"cf8530af-3ecd-4a30-8d71-a12e28723778"},"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>longitude</th>\n","      <th>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>total_bedrooms</th>\n","      <th>population</th>\n","      <th>households</th>\n","      <th>median_income</th>\n","      <th>median_house_value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>-119.562108</td>\n","      <td>35.625225</td>\n","      <td>28.589353</td>\n","      <td>2643.664412</td>\n","      <td>539.410824</td>\n","      <td>1429.573941</td>\n","      <td>501.221941</td>\n","      <td>3.883578</td>\n","      <td>207300.912353</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>2.005166</td>\n","      <td>2.137340</td>\n","      <td>12.586937</td>\n","      <td>2179.947071</td>\n","      <td>421.499452</td>\n","      <td>1147.852959</td>\n","      <td>384.520841</td>\n","      <td>1.908157</td>\n","      <td>115983.764387</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>-124.350000</td>\n","      <td>32.540000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","      <td>0.499900</td>\n","      <td>14999.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>-121.790000</td>\n","      <td>33.930000</td>\n","      <td>18.000000</td>\n","      <td>1462.000000</td>\n","      <td>297.000000</td>\n","      <td>790.000000</td>\n","      <td>282.000000</td>\n","      <td>2.566375</td>\n","      <td>119400.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>-118.490000</td>\n","      <td>34.250000</td>\n","      <td>29.000000</td>\n","      <td>2127.000000</td>\n","      <td>434.000000</td>\n","      <td>1167.000000</td>\n","      <td>409.000000</td>\n","      <td>3.544600</td>\n","      <td>180400.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>-118.000000</td>\n","      <td>37.720000</td>\n","      <td>37.000000</td>\n","      <td>3151.250000</td>\n","      <td>648.250000</td>\n","      <td>1721.000000</td>\n","      <td>605.250000</td>\n","      <td>4.767000</td>\n","      <td>265000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>-114.310000</td>\n","      <td>41.950000</td>\n","      <td>52.000000</td>\n","      <td>37937.000000</td>\n","      <td>6445.000000</td>\n","      <td>35682.000000</td>\n","      <td>6082.000000</td>\n","      <td>15.000100</td>\n","      <td>500001.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          longitude      latitude  housing_median_age   total_rooms  \\\n","count  17000.000000  17000.000000        17000.000000  17000.000000   \n","mean    -119.562108     35.625225           28.589353   2643.664412   \n","std        2.005166      2.137340           12.586937   2179.947071   \n","min     -124.350000     32.540000            1.000000      2.000000   \n","25%     -121.790000     33.930000           18.000000   1462.000000   \n","50%     -118.490000     34.250000           29.000000   2127.000000   \n","75%     -118.000000     37.720000           37.000000   3151.250000   \n","max     -114.310000     41.950000           52.000000  37937.000000   \n","\n","       total_bedrooms    population    households  median_income  \\\n","count    17000.000000  17000.000000  17000.000000   17000.000000   \n","mean       539.410824   1429.573941    501.221941       3.883578   \n","std        421.499452   1147.852959    384.520841       1.908157   \n","min          1.000000      3.000000      1.000000       0.499900   \n","25%        297.000000    790.000000    282.000000       2.566375   \n","50%        434.000000   1167.000000    409.000000       3.544600   \n","75%        648.250000   1721.000000    605.250000       4.767000   \n","max       6445.000000  35682.000000   6082.000000      15.000100   \n","\n","       median_house_value  \n","count        17000.000000  \n","mean        207300.912353  \n","std         115983.764387  \n","min          14999.000000  \n","25%         119400.000000  \n","50%         180400.000000  \n","75%         265000.000000  \n","max         500001.000000  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["california_housing_dataframe.describe()"]},{"cell_type":"markdown","metadata":{"id":"w9-Es5Y6laGd"},"source":["Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":469},"executionInfo":{"elapsed":484,"status":"ok","timestamp":1692083891092,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"nqndFVXVlbPN","outputId":"02f94795-a190-479f-f470-ad805ac28ddd"},"outputs":[{"data":{"text/plain":["array([[<Axes: title={'center': 'housing_median_age'}>]], dtype=object)"]},"execution_count":5,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["california_housing_dataframe.hist('housing_median_age')"]},{"cell_type":"markdown","metadata":{"id":"X5WTx27a6lUZ"},"source":["Another example, `DataFrame.plot.scatter` lets you quickly study the possition of houses:"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":466},"executionInfo":{"elapsed":607,"status":"ok","timestamp":1692083892650,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"Pmbq70pb512S","outputId":"8c7f5e3a-7bb6-4e10-8b01-73d3502247c0"},"outputs":[{"data":{"text/plain":["<Axes: xlabel='longitude', ylabel='latitude'>"]},"execution_count":6,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["california_housing_dataframe.plot.scatter(x='longitude',\n","                      y='latitude',\n","                      c='DarkBlue')"]},{"cell_type":"markdown","metadata":{},"source":["Boxplots can also be displayed with `DataFrame.boxplot`."]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1692083893142,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"FirdkMFF-k4u","outputId":"749079df-2831-4d01-c904-60d237763035"},"outputs":[{"data":{"text/plain":["<Axes: >"]},"execution_count":7,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAn8klEQVR4nO3de3DU9b3/8dcm2Sy5S4KGRAJJQxHkWvBS5T5HwgGlOLRab4DW09qSghRBpD20otYcWsRwxMuxnRHGFsEplzp4S6ZFLgWUBEG8QAwGQQgHiJDNBZdN8v39wS97iAmQwPf72ezm+Zhhst/P973f73sz882++F5dlmVZAgAAMCQi2A0AAICOhfABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwKioYDfwbQ0NDTpy5IgSEhLkcrmC3Q4AAGgFy7JUVVWl9PR0RURceN9GuwsfR44cUUZGRrDbAAAAl+DQoUPq1q3bBWvaXfhISEiQdLb5xMTEIHcDwE5+v18FBQXKycmR2+0OdjsAbOT1epWRkRH4Hr+Qdhc+Gg+1JCYmEj6AMOP3+xUbG6vExETCBxCmWnPKBCecAgAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAw4vnnn1d0dLRuv/12RUdH6/nnnw92SwCCxGVZlhXsJs7l9XqVlJSkyspK7nAKhIkL3fGwnf0JAnCJ2vL9zZ4PAI76dvBITk6+4HwA4Y/wAcAx5x5aWbNmjQoLC3X//fersLBQa9asabEOQPjjsAsAx5y7VyMzM1MHDhw473Q7+1MEoI047AKg3enfv782b96s1157TZs3b1b//v2D3RKAIIkKdgMAOoZ169apvr5eFRUVuvHGG7Vu3TpFRkYGuy0AQcCeDwCOmTZtWuD1+vXrm8w7d/rcOgDhjz0fABwzbNgwvfDCC5KkiRMnSlLgmPC36wB0HOz5AOCYtLS0ZmPfDh7nqwMQvggfABwzfPhwZWZmasKECVqyZEmTeUuWLNGECROUlZWl4cOHB6lDAMHApbYAHLVmzRr98Ic/VExMjE6fPh0Yb5xevXq1Jk2aFMQOAdiBS20BtCst3cXU5XJxd1OggyJ8AHBMfX29HnnkEd12222qrKxUYWGhZs2apcLCQp06dUq33XabZs+erfr6+mC3CsAgwgcAx2zevFkHDhzQr3/9a1mWpd27d2vv3r3avXu3LMvSvHnzVFZWps2bNwe7VQAGcaktAMeUl5dLklauXKnhw4errq5OkvTWW2/pscceU25ubpM6AB0D4QOAYxovoV2yZIlSU1O1YMECeTwe+Xw+/e53vwtcAcOltkDHwmEXAI658cYbJUnR0dEqKytTdna29uzZo+zsbJWVlSk6OrpJHYCOgT0fABzzP//zP5KkM2fOKCUlJXCp7eLFixUTE6MzZ84E6mbOnBmsNgEYxp4PAI7Zv3//eeede5ntheoAhB/CBwDHZGVlSZIGDhzY4qW2AwYMaFIHoGPgsAsAx/Tv31+SdPDgQblcLo0cOVI1NTUaOXKkXC6XDh061KQOQMfAng8Ajjlx4oQk6eTJk+rWrZv+/Oc/6+uvv9af//xndevWTSdPnmxSB6BjYM8HAMc0XkJ77733atWqVZo2bVpgXlRUlO655x6tWLGCS22BDqbNez42bdqkCRMmKD09XS6XS+vWrTtv7UMPPSSXy6X8/PzLaBFAqGp8qq3X61VVVZUWLVqk8ePHa9GiRaqqqlJVVRVPtQU6oDaHj5qaGg0cOFBLly69YN26dev0/vvvKz09/ZKbAxDaIiMj9cwzz2j9+vW644475Ha79Z3vfEdut1t33HGH1q9fr0WLFikyMjLYrQIwqM2HXcaNG6dx48ZdsObw4cP65S9/qXfffVe33nrrJTcHIPRNmjRJs2fP1rPPPqv169cHxqOiojR79mxNmjQpiN0BCAbbz/loaGjQ5MmTNWfOHPXt29fuxQMIMWvWrAkcbsnKylJJSYl69eqlsrIyLVq0SN///vcJIEAHY3v4WLhwoaKiojRjxoxW1ft8Pvl8vsC01+uVJPn9fvn9frvbA2BQfX29HnnkEQ0ePFh79uzRm2++KUkqKChQ9+7dNXjwYM2ePVvjx4/n0AsQ4trynW1r+CguLtaSJUu0c+fOJncvvJC8vDwtWLCg2XhBQYFiY2PtbA+AYXv27NGBAwd04MCBwHNcGh09elQHDx6UJC1atIh7fQAhrra2ttW1toaPzZs369ixY+revXtgrPF/Pvn5+Tpw4ECz98ybN0+zZs0KTHu9XmVkZCgnJ0eJiYl2tgfAsFOnTgVejxkzRrNnz9bRo0fVtWtXLVq0KLAnJCMjQ+PHjw9SlwDs0HjkojVsDR+TJ0/WLbfc0mRs7Nixmjx5sh544IEW3+PxeOTxeJqNu91uud1uO9sDYFhFRYWks7dXX7t2rTZu3KgdO3Zo3LhxWrt2ra677jp99NFHqqioYHsHQlxbtuE2h4/q6mqVlpYGpsvKyrRr1y4lJyere/fuSklJadZM165ddc0117R1VQBCXGP4OH36tHr16hXY+7l48WJlZmYGDsU01gHoGNocPoqKijR69OjAdOMhk6lTp2rZsmW2NQYg9EVEnL2VUElJiVJTU/WrX/1KNTU1iouL04oVKwJhpLEOQMfQ5vAxatQoWZbV6vqWzvMA0DGMGDFC0tnDq8ePH9ezzz4bmBcZGSmPxyOfzxeoA9Ax8GwXAI5p3KPh8/mUkpKivn376sSJE+rSpYs++eSTwOEW9nwAHQvhA4Bjjh49GnhdUVGhTZs2XbQOQPjjvxsAHHP8+HFb6wCEB8IHAMd07tw58PrbdzA9d/rcOgDhj/ABwDHbt28PvK6vr28y79zpc+sAhD/CBwDHHDlyJPD62yeVnjt9bh2A8Ef4AOCY6urqwOuGhoYm886dPrcOQPgjfAAAAKMIHwAc06lTp8DrCx12ObcOQPgjfABwzJdffhl4faHDLufWAQh/hA8AAGAU4QOAYxITE22tAxAeCB8AHNOtWzdb6wCEB8IHAMe09v4d3OcD6FgIHwAc0717d1vrAIQHwgcAx/Tv39/WOgDhgfABwDEff/yxrXUAwgPhA4BjWnv/Du7zAXQshA8AjvF4PJKkyMhIuVyuJvMiIiIUGRnZpA5Ax0D4AOCY1NRUSVJ9fX2zeZZlBcYb6wB0DIQPAI7JzMwMvLYsq8m8c6fPrQMQ/ggfABzTuXNnW+sAhAfCBwDHnDp1ytY6AOGB8AEAAIwifABwDA+WA9CSqGA3ACB87d69O/D6yiuvVFpamioqKpSSkqLy8nIdP368WR2A8Ef4AOCYjz76KPD6+PHjgbBx+PDh89YBCH8cdgHgmG/fWOxy6wCEB8IHAMf06dPH1joA4YHwAcAxNTU1ttYBCA+EDwCOKSkpsbUOQHggfABwTHV1ta11AMID4QOAY3r06GFrHYDwQPgA4JgrrrjC1joA4YHwAcAxhw4dsrUOQHggfABwTGVlpa11AMJDm8PHpk2bNGHCBKWnp8vlcmndunWBeX6/X3PnzlX//v0VFxen9PR0TZkyRUeOHLGzZwAh4sorr7S1DkB4aHP4qKmp0cCBA7V06dJm82pra7Vz507Nnz9fO3fu1Jo1a1RSUqIf/OAHtjQLILT06tXL1joA4aHNz3YZN26cxo0b1+K8pKQkFRYWNhl77rnndMMNN+jgwYPq3r37pXUJICRFRkbaWgcgPDj+YLnKykq5XK7zns3u8/nk8/kC016vV9LZQzh+v9/p9gA46OjRo62uY3sHQltbtmFHw8c333yjxx57TPfcc48SExNbrMnLy9OCBQuajRcUFCg2NtbJ9gA4rLy8vNV1b731lsPdAHBSbW1tq2sdCx9+v1933XWXGhoa9MILL5y3bt68eZo1a1Zg2uv1KiMjQzk5OecNLABCw4oVK7R3796L1g0YMEDjx4830BEApzQeuWgNR8KH3+/XnXfeqbKyMv3zn/+8YIjweDzyeDzNxt1ut9xutxPtATCkc+fOgdcRERFqaGhocbpz585s70CIa8s2bHv4aAwen3/+uTZs2KCUlBS7VwEgRERE/N8FdecGj29Pn1sHIPy1OXxUV1ertLQ0MF1WVqZdu3YpOTlZ6enp+tGPfqSdO3dq/fr1qq+vD5xwlpycrOjoaPs6B9DutTZUED6AjqXN4aOoqEijR48OTDeerzF16lQ9/vjjeuONNyRJgwYNavK+DRs2aNSoUZfeKYCQ873vfc/WOgDhoc3hY9SoUbIs67zzLzQPQMeydu3aVtc9+OCDDncDoL1gXycAx3z44Ye21gEID47fZAxAx1VdXR14nZKSoquvvloVFRVKSUnR4cOHVVFR0awOQPgjfABwzBVXXBG49r+ioiIQNg4fPtysDkDHwWEXAI7Jzs5uMt2tWzf17NlT3bp1u2AdgPBG+ADgmCFDhjSZ/uqrr1RaWqqvvvrqgnUAwhvhA4BjampqbK0DEB4IHwAcU19fb2sdgPBA+ADgmG8fXrncOgDhgfABwDGffPKJrXUAwgPhA4BjWvuI7bY8ihtA6CN8AHCMy+WytQ5AeCB8AHCMx+OxtQ5AeCB8AHDM6dOnba0DEB4IHwAc4/f7ba0DEB4IHwAcU1dXZ2sdgPBA+ADgGMIHgJYQPgA4pqGhwdY6AOGB8AHAMZZl2VoHIDwQPgA4hvt8AGgJ4QOAY9xut611AMID4QOAY86cOWNrHYDwQPgAAABGET4AAIBRhA8AAGAU4QOAYyIiWvcnprV1AMIDWzwAx3CfDwAtIXwAAACjCB8AHMOeDwAtIXwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMKrN4WPTpk2aMGGC0tPT5XK5tG7duibzLcvS448/rvT0dMXExGjUqFH65JNP7OoXAACEuDaHj5qaGg0cOFBLly5tcf4f/vAHLV68WEuXLtWOHTvUtWtXjRkzRlVVVZfdLAAACH1RbX3DuHHjNG7cuBbnWZal/Px8/eY3v9GkSZMkScuXL1dqaqpWrFihhx566PK6BQAAIa/N4eNCysrKdPToUeXk5ATGPB6PRo4cqa1bt7YYPnw+n3w+X2Da6/VKkvx+v/x+v53tAWjH2N6B0NaWbdjW8HH06FFJUmpqapPx1NRUffnlly2+Jy8vTwsWLGg2XlBQoNjYWDvbA9COvfXWW8FuAcBlqK2tbXWtreGjkcvlajJtWVazsUbz5s3TrFmzAtNer1cZGRnKyclRYmKiE+0BMCQ+Pl7V1dWtqhs/fryBjgA4pfHIRWvYGj66du0q6ewekLS0tMD4sWPHmu0NaeTxeOTxeJqNu91uud1uO9sDYFhbwgfbOxDa2rIN23qfj6ysLHXt2lWFhYWBsTNnzmjjxo26+eab7VwVgBBw/PhxW+sAhIc27/morq5WaWlpYLqsrEy7du1ScnKyunfvrpkzZ+rpp5/Wd7/7XX33u9/V008/rdjYWN1zzz22Ng6g/auvr7e1DkB4aHP4KCoq0ujRowPTjedrTJ06VcuWLdOjjz6q06dPa9q0aTp58qRuvPFGFRQUKCEhwb6uAQBAyHJZlmUFu4lzeb1eJSUlqbKykhNOgRAXERGh1vyJcblcamhoMNARAKe05fubZ7sAcExrL5fnsnqgYyF8AHBMZGSkrXUAwgPhA4BjWrqM/nLqAIQHwgcAx5w6dcrWOgDhgfABAACMInwAcAznfABoCeEDgGOio6NtrQMQHggfABzT2qdctuVpmABCH+EDgGO4vTqAlhA+ADimtTdQbmc3WgbgMMIHAAAwivABAACMInwAcExEROv+xLS2DkB4YIsH4Bju8wGgJYQPAI7x+/221gEID4QPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYJTt4aOurk7/+Z//qaysLMXExOg73/mOnnjiCTU0NNi9KgAAEIKi7F7gwoUL9dJLL2n58uXq27evioqK9MADDygpKUkPP/yw3asDYEBtba327t3r6Dp27tx5Se/r3bu3YmNjbe4GgJNsDx/btm3TxIkTdeutt0qSMjMz9dprr6moqMjuVQEwZO/evRoyZIij67jU5RcXF2vw4ME2dwPASbaHj2HDhumll15SSUmJevXqpd27d2vLli3Kz89vsd7n88nn8wWmvV6vJMnv98vv99vdHoBLkJ2drffff7/N77vrrrtUVlZ20bqsrCytXLnyUlpTdnY2fyuAdqAt26Ht4WPu3LmqrKxU7969FRkZqfr6ev3+97/X3Xff3WJ9Xl6eFixY0Gy8oKCAXalAiHvyySd13333taquvLz8ktZxqe8DYK/a2tpW17osy7LsXPnKlSs1Z84c/fGPf1Tfvn21a9cuzZw5U4sXL9bUqVOb1be05yMjI0MnTpxQYmKina0BCII+ffpo//79552fnZ2tzz77zGBHAJzg9XrVpUsXVVZWXvT72/bwkZGRoccee0y5ubmBsaeeekp/+ctfWnXCmtfrVVJSUquaBxAaevbs2WIAyc7OVmlpaRA6AmC3tnx/236pbW1trSIimi42MjKSS22BDqy0tFSnTp3SwCE3KDKhiwYOuUGnTp0ieAAdlO3nfEyYMEG///3v1b17d/Xt21cffvihFi9erJ/85Cd2rwpACElKStKy1W/p9he3a9kvvq+kpKRgtwQgSGwPH88995zmz5+vadOm6dixY0pPT9dDDz2k3/72t3avCgAAhCDbw0dCQoLy8/PPe2ktAADo2Hi2CwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMcCR+HDx/Wfffdp5SUFMXGxmrQoEEqLi52YlUAACDERNm9wJMnT2ro0KEaPXq03n77bV111VXav3+/rrjiCrtXBQAAQpDt4WPhwoXKyMjQK6+8EhjLzMy0ezUAACBE2R4+3njjDY0dO1Z33HGHNm7cqKuvvlrTpk3TT3/60xbrfT6ffD5fYNrr9UqS/H6//H6/3e0BCKK6urrAT7ZvILy0ZZu2PXx88cUXevHFFzVr1iz9+te/1gcffKAZM2bI4/FoypQpzerz8vK0YMGCZuMFBQWKjY21uz0AQXSoWpKitH37dh3+ONjdALBTbW1tq2tdlmVZdq48Ojpa1113nbZu3RoYmzFjhnbs2KFt27Y1q29pz0dGRoZOnDihxMREO1sDEGS7D36tH/2pSH/76XUa2D052O0AsJHX61WXLl1UWVl50e9v2/d8pKWl6dprr20y1qdPH61evbrFeo/HI4/H02zc7XbL7Xbb3R6AIIqKigr8ZPsGwktbtmnbL7UdOnSo9u3b12SspKREPXr0sHtVAAAgBNkePn71q19p+/btevrpp1VaWqoVK1bo5ZdfVm5urt2rAgAAIcj28HH99ddr7dq1eu2119SvXz89+eSTys/P17333mv3qgAAQAiy/ZwPSbrtttt02223ObFoAAAQ4ni2CwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMCoqGA3AMA5ZSdqVOOrC3YbAfuP1wR+RkW1rz8/cZ4oZXWJC3YbQIfQvrZ+ALYpO1Gj0YveC3YbLXrkb3uC3UKLNsweRQABDCB8AGGqcY9H/o8HqedV8UHu5qya0z6tf2+bbht1k+JiPMFuJ6D0WLVmrtrVrvYSAeGM8AGEuZ5Xxavf1UnBbkOS5Pf7dfRKaXCPznK73cFuB0CQcMIpAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAoxwPH3l5eXK5XJo5c6bTqwIAACHA0fCxY8cOvfzyyxowYICTqwEAACHEsfBRXV2te++9V3/605/UuXNnp1YDAABCTJRTC87NzdWtt96qW265RU899dR563w+n3w+X2Da6/VKOvvobb/f71R7QNirq6sL/Gwv21JjH+2ln0bt8XcFhJq2bDuOhI+VK1dq586d2rFjx0Vr8/LytGDBgmbjBQUFio2NdaI9oEM4VC1JUdqyZYu+jA92N00VFhYGu4Um2vPvCggVtbW1ra61PXwcOnRIDz/8sAoKCtSpU6eL1s+bN0+zZs0KTHu9XmVkZCgnJ0eJiYl2twd0GJ8c8WrRnu0aNmyY+qa3j23J7/ersLBQY8aMkdvtDnY7Ae3xdwWEmsYjF61he/goLi7WsWPHNGTIkMBYfX29Nm3apKVLl8rn8ykyMjIwz+PxyOPxNFuO2+1uV3+cgFATFRUV+NnetqX2tn23598VECrasu3YHj7+7d/+TXv27Gky9sADD6h3796aO3duk+ABAAA6HtvDR0JCgvr169dkLC4uTikpKc3GAQBAx8MdTgEAgFGOXWp7rvfee8/EagAAQAhgzwcAADCK8AEAAIwifAAAAKOMnPMBwDxf/TeK6HRYZd59iujUPm7bWVdXpyN1R/TZ158F7q3RHpR5qxXR6bB89d9ISgp2O0DYaz9bPwBbHan5UnFZz+nXHwS7k+ZeeOeFYLfQTFyWdKRmkIYoNditAGGP8AGEqfS4Hqopm64lPx6k7Kvaz56Pf235l4YOG9qu9nzsP1ath1ftUvroHsFuBegQ2s/WD8BWnshOavjmamUlXqNrU9rHoQS/36+yqDL1Se7Trm5j3vBNpRq+OS5P5MWfRwXg8nHCKQAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMigp2AwCccdpfL0n6+HBlkDv5PzWnfSo6LnX98qTiYjzBbieg9Fh1sFsAOhTCBxCm9v//L9TH1uwJciffFqVXS3cEu4kWxXn4kwiYwJYGhKmcvl0lSdlXxSvGHRnkbs7aV16pR/62R8/8qL+uSUsKdjtNxHmilNUlLthtAB0C4QMIU8lx0brrhu7BbqOJuro6SVL2lXHqd3X7Ch8AzOGEUwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABG2R4+8vLydP311yshIUFXXXWVbr/9du3bt8/u1QAAgBBle/jYuHGjcnNztX37dhUWFqqurk45OTmqqamxe1UAACAE2X6H03feeafJ9CuvvKKrrrpKxcXFGjFihN2rAwAAIcbx26tXVp59omZycnKL830+n3w+X2Da6/VKkvx+v/x+v9PtATCo8fbqdXV1bN9AmGnLNu1o+LAsS7NmzdKwYcPUr1+/Fmvy8vK0YMGCZuMFBQWKjY11sj0Ahh2qlqQobd++XYc/DnY3AOxUW1vb6lqXZVmWU43k5ubqzTff1JYtW9StW7cWa1ra85GRkaETJ04oMTHRqdYABMHug1/rR38q0t9+ep0Gdm95byiA0OT1etWlSxdVVlZe9PvbsT0f06dP1xtvvKFNmzadN3hIksfjkcfjaTbudrvldrudag9AEERFRQV+sn0D4aUt27Tt4cOyLE2fPl1r167Ve++9p6ysLLtXAQAAQpjt4SM3N1crVqzQ3//+dyUkJOjo0aOSpKSkJMXExNi9OgAAEGJsv8/Hiy++qMrKSo0aNUppaWmBf6tWrbJ7VQAAIAQ5ctgFAADgfHi2CwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjIoKdgMA2r/a2lrt3bv3spezr/yUfEdL9dnHMWqouOLyG5PUu3dvxcbG2rIsAGYQPgBc1N69ezVkyBDblnfPctsWpeLiYg0ePNi+BQJwHOEDwEX17t1bxcXFl72c6tM+vblhm24dfZPiYzw2dHa2NwChhfAB4KJiY2Nt2bvg9/t18sQx3XTDdXK73TZ0BiAUccIpAAAwivABAACMInwAAACjCB8AAMAowgcAADDKsfDxwgsvKCsrS506ddKQIUO0efNmp1YFAABCiCPhY9WqVZo5c6Z+85vf6MMPP9Tw4cM1btw4HTx40InVAQCAEOJI+Fi8eLEefPBB/cd//If69Omj/Px8ZWRk6MUXX3RidQAAIITYfpOxM2fOqLi4WI899liT8ZycHG3durVZvc/nk8/nC0x7vV5JZ29G5Pf77W4PQBA1btNs20D4act2bXv4OHHihOrr65WamtpkPDU1VUePHm1Wn5eXpwULFjQbLygo4GFRQJgqLCwMdgsAbFZbW9vqWsdur+5yuZpMW5bVbEyS5s2bp1mzZgWmvV6vMjIylJOTo8TERKfaAxAEfr9fhYWFGjNmDLdXB8JM45GL1rA9fHTp0kWRkZHN9nIcO3as2d4QSfJ4PPJ4mj9gyu1288cJCFNs30D4acs2bfsJp9HR0RoyZEiz3aqFhYW6+eab7V4dAAAIMY4cdpk1a5YmT56s6667TjfddJNefvllHTx4UD//+c8v+l7LsiS1bfcNgNDg9/tVW1srr9fLng8gzDR+bzd+j1+II+Hjxz/+sSoqKvTEE0+ovLxc/fr101tvvaUePXpc9L1VVVWSpIyMDCdaAwAADqqqqlJSUtIFa1xWayKKQQ0NDTpy5IgSEhJaPEEVQOhqPKH80KFDnFAOhBnLslRVVaX09HRFRFz4rI52Fz4AhC+v16ukpCRVVlYSPoAOjAfLAQAAowgfAADAKMIHAGM8Ho9+97vftXhvHwAdB+d8AAAAo9jzAQAAjCJ8AAAAowgfAADAKMIHAI0aNUozZ84MTGdmZio/P9/Y+u+//37dfvvtxtYHILgcub06gNC2Y8cOxcXFGVvfkiVLWvU8CADhgfABoJkrr7zS6Pou9hwIAOGFwy5AOzZq1ChNnz5dM2fOVOfOnZWamqqXX35ZNTU1euCBB5SQkKDs7Gy9/fbbgfd8+umnGj9+vOLj45WamqrJkyfrxIkTgfk1NTWaMmWK4uPjlZaWpmeeeabZer992GXx4sXq37+/4uLilJGRoWnTpqm6ujowf9myZbriiiv07rvvqk+fPoqPj9e///u/q7y8vFWf89uHXUaNGqUZM2bo0UcfVXJysrp27arHH3+8yXtOnTqln/3sZ0pNTVWnTp3Ur18/rV+/PjB/9erV6tu3rzwejzIzM5t9zszMTD311FOB30WPHj3097//XcePH9fEiRMVHx+v/v37q6ioqMn7tm7dqhEjRigmJkYZGRmaMWOGampqWvU5AZxF+ADaueXLl6tLly764IMPNH36dP3iF7/QHXfcoZtvvlk7d+7U2LFjNXnyZNXW1qq8vFwjR47UoEGDVFRUpHfeeUf/+7//qzvvvDOwvDlz5mjDhg1au3atCgoK9N5776m4uPiCPUREROi///u/9fHHH2v58uX65z//qUcffbRJTW1trRYtWqRXX31VmzZt0sGDBzV79uzL+txxcXF6//339Yc//EFPPPGECgsLJZ19AOW4ceO0detW/eUvf9Gnn36q//qv/1JkZKQkqbi4WHfeeafuuusu7dmzR48//rjmz5+vZcuWNVnHs88+q6FDh+rDDz/UrbfeqsmTJ2vKlCm67777tHPnTvXs2VNTpkwJHBLas2ePxo4dq0mTJumjjz7SqlWrtGXLFv3yl7+85M8JdEgWgHZr5MiR1rBhwwLTdXV1VlxcnDV58uTAWHl5uSXJ2rZtmzV//nwrJyenyTIOHTpkSbL27dtnVVVVWdHR0dbKlSsD8ysqKqyYmBjr4YcfDoz16NHDevbZZ8/b1+uvv26lpKQEpl955RVLklVaWhoYe/75563U1NRWfc6pU6daEydOPO/ntizLuv766625c+dalmVZ7777rhUREWHt27evxeXdc8891pgxY5qMzZkzx7r22msD0z169LDuu+++wHTj73H+/PmBsW3btlmSrPLycsuyLGvy5MnWz372sybL3bx5sxUREWGdPn26VZ8VgGVxzgfQzg0YMCDwOjIyUikpKerfv39gLDU1VZJ07NgxFRcXa8OGDYqPj2+2nP379+v06dM6c+aMbrrppsB4cnKyrrnmmgv2sGHDBj399NP69NNP5fV6VVdXp2+++UY1NTWBE1NjY2OVnZ0deE9aWpqOHTt2aR9aTT/3t5e3a9cudevWTb169WrxvZ999pkmTpzYZGzo0KHKz89XfX19YA/Jueto/D2e73fbtWtXFRcXq7S0VH/9618DNZZlqaGhQWVlZerTp8+lflygQyF8AO2c2+1uMu1yuZqMuVwuSWcPRTQ0NGjChAlauHBhs+WkpaXp888/b/P6v/zyS40fP14///nP9eSTTyo5OVlbtmzRgw8+KL/ff8E+rcu4gqWl5TU0NEiSYmJiLvhey7ICv5dzxy60jsb68/1uG38+9NBDmjFjRrNlde/e/YI9Afg/hA8gjAwePFirV69WZmamoqKab949e/aU2+3W9u3bA1+WJ0+eVElJiUaOHNniMouKilRXV6dnnnlGERFnTxN7/fXXnfsQrTBgwAB99dVXKikpaXHvx7XXXqstW7Y0Gdu6dat69eoV2OtxKQYPHqxPPvlEPXv2vORlAOCEUyCs5Obm6uuvv9bdd9+tDz74QF988YUKCgr0k5/8RPX19YqPj9eDDz6oOXPm6B//+Ic+/vhj3X///YFQ0ZLs7GzV1dXpueee0xdffKFXX31VL730ksFP1dzIkSM1YsQI/fCHP1RhYaHKysr09ttv65133pEkPfLII/rHP/6hJ598UiUlJVq+fLmWLl16WSfAStLcuXO1bds25ebmateuXfr888/1xhtvaPr06XZ8LKDDIHwAYSQ9PV3/+te/VF9fr7Fjx6pfv356+OGHlZSUFAgYf/zjHzVixAj94Ac/0C233KJhw4ZpyJAh513moEGDtHjxYi1cuFD9+vXTX//6V+Xl5Zn6SOe1evVqXX/99br77rt17bXX6tFHH1V9fb2ks3soXn/9da1cuVL9+vXTb3/7Wz3xxBO6//77L2udAwYM0MaNG/X5559r+PDh+t73vqf58+crLS3Nhk8EdBwu63IOygIAALQRez4AAIBRhA8AjouPjz/vv82bNwe7PQCGcdgFgONKS0vPO+/qq6++6KWzAMIL4QMAABjFYRcAAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUf8PYexWQyPSqH4AAAAASUVORK5CYII=","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["california_housing_dataframe.boxplot(column=[\"median_income\"])"]},{"cell_type":"markdown","metadata":{},"source":["You can learn other plot options in the plot section of the pandas API [here](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html)."]},{"cell_type":"markdown","metadata":{},"source":["## Split-apply-combine on Tidy Data\n","\n","### Tidy Data\n","\n","Hadley Wickham wrote a great [article](https://www.jstatsoft.org/article/view/v059i10) in favor of “tidy data.” Tidy data frames follow the rules:\n","\n","* Each variable is a column.\n","\n","* Each observation is a row.\n","\n","* Each type of observation has its own separate data frame.\n","\n","This is less pretty to visualize as a table (you may notice too many rows with repeated values in certain columns), but the representation of data which is convenient for visualization is different from that which is convenient for analysis. A tidy data frame is almost always much easier to work with than non-tidy formats.\n","\n","Let's look at the titanic dataset below."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":2348,"status":"ok","timestamp":1692083896280,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"Y8tK6D1Q4qGB","outputId":"1c722b54-e3fb-43f7-9d80-32c814ea33ba"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>survived</th>\n","      <th>pclass</th>\n","      <th>sex</th>\n","      <th>age</th>\n","      <th>sibsp</th>\n","      <th>parch</th>\n","      <th>fare</th>\n","      <th>embarked</th>\n","      <th>class</th>\n","      <th>who</th>\n","      <th>adult_male</th>\n","      <th>deck</th>\n","      <th>embark_town</th>\n","      <th>alive</th>\n","      <th>alone</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7.2500</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>71.2833</td>\n","      <td>C</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>C</td>\n","      <td>Cherbourg</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.9250</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>S</td>\n","      <td>First</td>\n","      <td>woman</td>\n","      <td>False</td>\n","      <td>C</td>\n","      <td>Southampton</td>\n","      <td>yes</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>S</td>\n","      <td>Third</td>\n","      <td>man</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","      <td>Southampton</td>\n","      <td>no</td>\n","      <td>True</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n","0         0       3    male  22.0      1      0   7.2500        S  Third   \n","1         1       1  female  38.0      1      0  71.2833        C  First   \n","2         1       3  female  26.0      0      0   7.9250        S  Third   \n","3         1       1  female  35.0      1      0  53.1000        S  First   \n","4         0       3    male  35.0      0      0   8.0500        S  Third   \n","\n","     who  adult_male deck  embark_town alive  alone  \n","0    man        True  NaN  Southampton    no  False  \n","1  woman       False    C    Cherbourg   yes  False  \n","2  woman       False  NaN  Southampton   yes   True  \n","3  woman       False    C  Southampton   yes  False  \n","4    man        True  NaN  Southampton    no   True  "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["# load dataset titanic from seaborn package\n","titanic = sns.load_dataset('titanic')\n","titanic.head()"]},{"cell_type":"markdown","metadata":{},"source":["According to the statements above, this dataframe is tidy, so it is easier to process."]},{"cell_type":"markdown","metadata":{},"source":["### Split-apply-combine"]},{"cell_type":"markdown","metadata":{},"source":["We might be interested in computing a statistic, let's say the survival mean, not for the entire data, but according to subgroups.\n","Basically, we want to:\n","\n","* Split the data according to the 'sex' criterion field, i.e., split it up so we have a separate data set for the two classes, you for 'male' and one for 'female'.\n","\n","* Apply a function (`mean`) to the 'survived' field in these split data sets.\n","\n","* Combine the results of these averages on the split data set into a new, summary data set that contains the two classes ('male' and 'female') and mean survival rate for each.\n","\n","The first step is to apply a `groupby` operation."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"text/plain":["<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001D743336850>"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["grouped = titanic.groupby('sex')\n","grouped"]},{"cell_type":"markdown","metadata":{},"source":["There is not much to see in the DataFrameGroupBy object that resulted. But there is a lot we can do with this object.\n","Now, we apply the `mean` function and check the combined result of this operation."]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>survived</th>\n","      <th>pclass</th>\n","      <th>age</th>\n","      <th>sibsp</th>\n","      <th>parch</th>\n","      <th>fare</th>\n","      <th>adult_male</th>\n","      <th>alone</th>\n","    </tr>\n","    <tr>\n","      <th>sex</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>female</th>\n","      <td>0.742038</td>\n","      <td>2.159236</td>\n","      <td>27.915709</td>\n","      <td>0.694268</td>\n","      <td>0.649682</td>\n","      <td>44.479818</td>\n","      <td>0.000000</td>\n","      <td>0.401274</td>\n","    </tr>\n","    <tr>\n","      <th>male</th>\n","      <td>0.188908</td>\n","      <td>2.389948</td>\n","      <td>30.726645</td>\n","      <td>0.429809</td>\n","      <td>0.235702</td>\n","      <td>25.523893</td>\n","      <td>0.930676</td>\n","      <td>0.712305</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        survived    pclass        age     sibsp     parch       fare  \\\n","sex                                                                    \n","female  0.742038  2.159236  27.915709  0.694268  0.649682  44.479818   \n","male    0.188908  2.389948  30.726645  0.429809  0.235702  25.523893   \n","\n","        adult_male     alone  \n","sex                           \n","female    0.000000  0.401274  \n","male      0.930676  0.712305  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["mean_measurements_per_sex = grouped.mean(numeric_only = True)\n","mean_measurements_per_sex"]},{"cell_type":"markdown","metadata":{},"source":["Here the `numeric_only` option is set to disconsider for calculating mean over columns that contain strings like 'embark_town' for example.\n","\n","The 'sex' field is now the index of our dataframe. We can put it back as a column with the `.reset_index` method."]},{"cell_type":"code","execution_count":11,"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>sex</th>\n","      <th>survived</th>\n","      <th>pclass</th>\n","      <th>age</th>\n","      <th>sibsp</th>\n","      <th>parch</th>\n","      <th>fare</th>\n","      <th>adult_male</th>\n","      <th>alone</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>female</td>\n","      <td>0.742038</td>\n","      <td>2.159236</td>\n","      <td>27.915709</td>\n","      <td>0.694268</td>\n","      <td>0.649682</td>\n","      <td>44.479818</td>\n","      <td>0.000000</td>\n","      <td>0.401274</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>male</td>\n","      <td>0.188908</td>\n","      <td>2.389948</td>\n","      <td>30.726645</td>\n","      <td>0.429809</td>\n","      <td>0.235702</td>\n","      <td>25.523893</td>\n","      <td>0.930676</td>\n","      <td>0.712305</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      sex  survived    pclass        age     sibsp     parch       fare  \\\n","0  female  0.742038  2.159236  27.915709  0.694268  0.649682  44.479818   \n","1    male  0.188908  2.389948  30.726645  0.429809  0.235702  25.523893   \n","\n","   adult_male     alone  \n","0    0.000000  0.401274  \n","1    0.930676  0.712305  "]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["mean_measurements_per_sex = mean_measurements_per_sex.reset_index()\n","mean_measurements_per_sex"]},{"cell_type":"markdown","metadata":{},"source":["We may choose to display only the 'sex' and 'survived' columns."]},{"cell_type":"code","execution_count":12,"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>sex</th>\n","      <th>survived</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>female</td>\n","      <td>0.742038</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>male</td>\n","      <td>0.188908</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      sex  survived\n","0  female  0.742038\n","1    male  0.188908"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["mean_measurements_per_sex[['sex', 'survived']]"]},{"cell_type":"markdown","metadata":{},"source":["Now that you know all the individual steps, here is all the above steps in one shot."]},{"cell_type":"code","execution_count":13,"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>sex</th>\n","      <th>survived</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>female</td>\n","      <td>0.742038</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>male</td>\n","      <td>0.188908</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      sex  survived\n","0  female  0.742038\n","1    male  0.188908"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["mean_survival_per_sex = titanic.groupby('sex').mean(numeric_only = True).reset_index()[['sex', 'survived']]\n","mean_survival_per_sex"]},{"cell_type":"markdown","metadata":{"id":"TKjzuMdh4CTV"},"source":["## Pivot Tables"]},{"cell_type":"markdown","metadata":{"id":"G73YCh66wgj2"},"source":["This is useful, but we might like to go one step deeper and look at survival rates by both sex and, say, class.\n","Using the vocabulary of `groupby`, we might proceed using a process like this:\n","we first *group by* 'class' **and** 'sex', then *select* survival, *apply* a mean aggregate, *combine* the resulting groups, and finally *unstack* the hierarchical index to reveal the hidden multidimensionality. In code:"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1692083896281,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"EEPIu7lywgj4","jupyter":{"outputs_hidden":false},"outputId":"349922a9-e39d-4eeb-d78d-a3e27306b2ef"},"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>class</th>\n","      <th>First</th>\n","      <th>Second</th>\n","      <th>Third</th>\n","    </tr>\n","    <tr>\n","      <th>sex</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>female</th>\n","      <td>0.968085</td>\n","      <td>0.921053</td>\n","      <td>0.500000</td>\n","    </tr>\n","    <tr>\n","      <th>male</th>\n","      <td>0.368852</td>\n","      <td>0.157407</td>\n","      <td>0.135447</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["class      First    Second     Third\n","sex                                 \n","female  0.968085  0.921053  0.500000\n","male    0.368852  0.157407  0.135447"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack()"]},{"cell_type":"markdown","metadata":{"id":"Zypmm-yZwgj7"},"source":["This gives us a better idea of how both sex and class affected survival, but the code is starting to look a bit garbled.\n","While each step of this pipeline makes sense in light of the tools we've previously discussed, the long string of code is not particularly easy to read or use.\n","This two-dimensional `groupby` is common enough that Pandas includes a convenience routine, `pivot_table`, which succinctly handles this type of multidimensional aggregation."]},{"cell_type":"markdown","metadata":{"id":"6bpHkQ0dwgj9"},"source":["### Pivot Table Syntax\n","\n","Here is the equivalent to the preceding operation using the `DataFrame.pivot_table` method:"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1692083898512,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"qIIz7i8Mwgj_","jupyter":{"outputs_hidden":false},"outputId":"64236ada-3edf-4ed8-af81-6b1d85da2ead"},"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>class</th>\n","      <th>First</th>\n","      <th>Second</th>\n","      <th>Third</th>\n","    </tr>\n","    <tr>\n","      <th>sex</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>female</th>\n","      <td>0.968085</td>\n","      <td>0.921053</td>\n","      <td>0.500000</td>\n","    </tr>\n","    <tr>\n","      <th>male</th>\n","      <td>0.368852</td>\n","      <td>0.157407</td>\n","      <td>0.135447</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["class      First    Second     Third\n","sex                                 \n","female  0.968085  0.921053  0.500000\n","male    0.368852  0.157407  0.135447"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["titanic.pivot_table('survived', index='sex', columns='class', aggfunc='mean')"]},{"cell_type":"markdown","metadata":{"id":"N4nkDbjuwgkA"},"source":["This is eminently more readable than the manual `groupby` approach, and produces the same result.\n","As you might expect of an early 20th-century transatlantic cruise, the survival gradient favors both higher classes and people recorded as females in the\n","data. First-class females survived with near certainty (hi, Rose!), while only one in eight or so third-class males survived (sorry, Jack!)."]},{"cell_type":"markdown","metadata":{"id":"eBwLC8X8CD4U"},"source":["## Pivot Table Widgets"]},{"cell_type":"markdown","metadata":{"id":"cUxzWxTwCYdk"},"source":["Some of the widgets needs to be specificaly tweaked for Google Colab or Jupyter Notebooks"]},{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":10,"status":"ok","timestamp":1692083921584,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"uzfXtb1AsoII"},"outputs":[],"source":["from pivottablejs import pivot_ui\n","from IPython.display import HTML\n","from IPython.display import IFrame\n","import json, io\n","\n","# Google colab alternative template\n","\n","TEMPLATE = u\"\"\"\n","<!DOCTYPE html>\n","<html>\n","    <head>\n","        <meta charset=\"UTF-8\">\n","        <title>PivotTable.js</title>\n","\n","        <!-- external libs from cdnjs -->\n","        <link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdnjs.cloudflare.com/ajax/libs/c3/0.4.11/c3.min.css\">\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/c3/0.4.11/c3.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/1.11.2/jquery.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.11.4/jquery-ui.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery-csv/0.71/jquery.csv-0.71.min.js\"></script>\n","\n","\n","        <link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.19.0/pivot.min.css\">\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.19.0/pivot.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.19.0/d3_renderers.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.19.0/c3_renderers.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.19.0/export_renderers.min.js\"></script>\n","\n","        <style>\n","            body {font-family: Verdana;}\n","            .node {\n","              border: solid 1px white;\n","              font: 10px sans-serif;\n","              line-height: 12px;\n","              overflow: hidden;\n","              position: absolute;\n","              text-indent: 2px;\n","            }\n","            .c3-line, .c3-focused {stroke-width: 3px !important;}\n","            .c3-bar {stroke: white !important; stroke-width: 1;}\n","            .c3 text { font-size: 12px; color: grey;}\n","            .tick line {stroke: white;}\n","            .c3-axis path {stroke: grey;}\n","            .c3-circle { opacity: 1 !important; }\n","            .c3-xgrid-focus {visibility: hidden !important;}\n","        </style>\n","    </head>\n","    <body>\n","        <script type=\"text/javascript\">\n","            $(function(){\n","\n","                $(\"#output\").pivotUI(\n","                    $.csv.toArrays($(\"#output\").text())\n","                    , $.extend({\n","                        renderers: $.extend(\n","                            $.pivotUtilities.renderers,\n","                            $.pivotUtilities.c3_renderers,\n","                            $.pivotUtilities.d3_renderers,\n","                            $.pivotUtilities.export_renderers\n","                            ),\n","                        hiddenAttributes: [\"\"]\n","                    }\n","                    , {\n","                        onRefresh: function(config) {\n","                            var config_copy = JSON.parse(JSON.stringify(config));\n","                            //delete some values which are functions\n","                            delete config_copy[\"aggregators\"];\n","                            delete config_copy[\"renderers\"];\n","                            //delete some bulky default values\n","                            delete config_copy[\"rendererOptions\"];\n","                            delete config_copy[\"localeStrings\"];\n","                            $(\"#output2\").text(JSON.stringify(config_copy, undefined, 2));\n","                        }\n","                    }\n","                    , %(kwargs)s\n","                    , %(json_kwargs)s)\n","                ).show();\n","             });\n","        </script>\n","        <div id=\"output\" style=\"display: none;\">%(csv)s</div>\n","\n","        <textarea id=\"output2\"\n","        style=\"float: left; width: 0px; height: 0px; margin: 0px; opacity:0;\" readonly>\n","        </textarea>\n","\n","        <button onclick=\"copyTextFunction()\">Copy settings</button>\n","        <script>\n","        function copyTextFunction() {\n","                    var copyText = document.getElementById(\"output2\");\n","                    copyText.select();\n","                    document.execCommand(\"copy\");\n","                    }\n","        </script>\n","\n","    </body>\n","</html>\n","\"\"\"\n","\n","\n","def pivot_cht_html(df, outfile_path = \"pivottablejs.html\", url=\"\",\n","    width=\"100%\", height=\"500\",json_kwargs='',  **kwargs):\n","  with io.open(outfile_path, 'wt', encoding='utf8') as outfile:\n","      csv = df.to_csv(encoding='utf8')\n","      if hasattr(csv, 'decode'):\n","          csv = csv.decode('utf8')\n","      outfile.write(TEMPLATE %\n","            dict(csv=csv, kwargs=json.dumps(kwargs),json_kwargs=json_kwargs))\n","\n","  IFrame(src=url or outfile_path, width=width, height=height)\n","  return HTML(outfile_path)"]},{"cell_type":"markdown","metadata":{"id":"kcqb0jA7ClZE"},"source":["Calling the function 'pivot_ui' with Pandas DataFrame as input will allow you interactively explore and plot its values"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":311},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1692083921584,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"GXd47Au2sj4i","outputId":"1e6472cb-aa0a-4bf8-c865-c51f8e38eedb"},"outputs":[{"data":{"text/html":["\n","<!DOCTYPE html>\n","<html>\n","    <head>\n","        <meta charset=\"UTF-8\">\n","        <title>PivotTable.js</title>\n","\n","        <!-- external libs from cdnjs -->\n","        <link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdnjs.cloudflare.com/ajax/libs/c3/0.4.11/c3.min.css\">\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/c3/0.4.11/c3.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/1.11.2/jquery.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.11.4/jquery-ui.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery-csv/0.71/jquery.csv-0.71.min.js\"></script>\n","\n","\n","        <link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.19.0/pivot.min.css\">\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.19.0/pivot.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.19.0/d3_renderers.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.19.0/c3_renderers.min.js\"></script>\n","        <script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.19.0/export_renderers.min.js\"></script>\n","\n","        <style>\n","            body {font-family: Verdana;}\n","            .node {\n","              border: solid 1px white;\n","              font: 10px sans-serif;\n","              line-height: 12px;\n","              overflow: hidden;\n","              position: absolute;\n","              text-indent: 2px;\n","            }\n","            .c3-line, .c3-focused {stroke-width: 3px !important;}\n","            .c3-bar {stroke: white !important; stroke-width: 1;}\n","            .c3 text { font-size: 12px; color: grey;}\n","            .tick line {stroke: white;}\n","            .c3-axis path 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none;\">,survived,pclass,sex,age,sibsp,parch,fare,embarked,class,who,adult_male,deck,embark_town,alive,alone\n","\n","0,0,3,male,22.0,1,0,7.25,S,Third,man,True,,Southampton,no,False\n","\n","1,1,1,female,38.0,1,0,71.2833,C,First,woman,False,C,Cherbourg,yes,False\n","\n","2,1,3,female,26.0,0,0,7.925,S,Third,woman,False,,Southampton,yes,True\n","\n","3,1,1,female,35.0,1,0,53.1,S,First,woman,False,C,Southampton,yes,False\n","\n","4,0,3,male,35.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","5,0,3,male,,0,0,8.4583,Q,Third,man,True,,Queenstown,no,True\n","\n","6,0,1,male,54.0,0,0,51.8625,S,First,man,True,E,Southampton,no,True\n","\n","7,0,3,male,2.0,3,1,21.075,S,Third,child,False,,Southampton,no,False\n","\n","8,1,3,female,27.0,0,2,11.1333,S,Third,woman,False,,Southampton,yes,False\n","\n","9,1,2,female,14.0,1,0,30.0708,C,Second,child,False,,Cherbourg,yes,False\n","\n","10,1,3,female,4.0,1,1,16.7,S,Third,child,False,G,Southampton,yes,False\n","\n","11,1,1,female,58.0,0,0,26.55,S,First,woman,False,C,Southampton,yes,True\n","\n","12,0,3,male,20.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","13,0,3,male,39.0,1,5,31.275,S,Third,man,True,,Southampton,no,False\n","\n","14,0,3,female,14.0,0,0,7.8542,S,Third,child,False,,Southampton,no,True\n","\n","15,1,2,female,55.0,0,0,16.0,S,Second,woman,False,,Southampton,yes,True\n","\n","16,0,3,male,2.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False\n","\n","17,1,2,male,,0,0,13.0,S,Second,man,True,,Southampton,yes,True\n","\n","18,0,3,female,31.0,1,0,18.0,S,Third,woman,False,,Southampton,no,False\n","\n","19,1,3,female,,0,0,7.225,C,Third,woman,False,,Cherbourg,yes,True\n","\n","20,0,2,male,35.0,0,0,26.0,S,Second,man,True,,Southampton,no,True\n","\n","21,1,2,male,34.0,0,0,13.0,S,Second,man,True,D,Southampton,yes,True\n","\n","22,1,3,female,15.0,0,0,8.0292,Q,Third,child,False,,Queenstown,yes,True\n","\n","23,1,1,male,28.0,0,0,35.5,S,First,man,True,A,Southampton,yes,True\n","\n","24,0,3,female,8.0,3,1,21.075,S,Third,child,False,,Southampton,no,False\n","\n","25,1,3,female,38.0,1,5,31.3875,S,Third,woman,False,,Southampton,yes,False\n","\n","26,0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n","\n","27,0,1,male,19.0,3,2,263.0,S,First,man,True,C,Southampton,no,False\n","\n","28,1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True\n","\n","29,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","30,0,1,male,40.0,0,0,27.7208,C,First,man,True,,Cherbourg,no,True\n","\n","31,1,1,female,,1,0,146.5208,C,First,woman,False,B,Cherbourg,yes,False\n","\n","32,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n","\n","33,0,2,male,66.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","34,0,1,male,28.0,1,0,82.1708,C,First,man,True,,Cherbourg,no,False\n","\n","35,0,1,male,42.0,1,0,52.0,S,First,man,True,,Southampton,no,False\n","\n","36,1,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,yes,True\n","\n","37,0,3,male,21.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","38,0,3,female,18.0,2,0,18.0,S,Third,woman,False,,Southampton,no,False\n","\n","39,1,3,female,14.0,1,0,11.2417,C,Third,child,False,,Cherbourg,yes,False\n","\n","40,0,3,female,40.0,1,0,9.475,S,Third,woman,False,,Southampton,no,False\n","\n","41,0,2,female,27.0,1,0,21.0,S,Second,woman,False,,Southampton,no,False\n","\n","42,0,3,male,,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True\n","\n","43,1,2,female,3.0,1,2,41.5792,C,Second,child,False,,Cherbourg,yes,False\n","\n","44,1,3,female,19.0,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True\n","\n","45,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","46,0,3,male,,1,0,15.5,Q,Third,man,True,,Queenstown,no,False\n","\n","47,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n","\n","48,0,3,male,,2,0,21.6792,C,Third,man,True,,Cherbourg,no,False\n","\n","49,0,3,female,18.0,1,0,17.8,S,Third,woman,False,,Southampton,no,False\n","\n","50,0,3,male,7.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False\n","\n","51,0,3,male,21.0,0,0,7.8,S,Third,man,True,,Southampton,no,True\n","\n","52,1,1,female,49.0,1,0,76.7292,C,First,woman,False,D,Cherbourg,yes,False\n","\n","53,1,2,female,29.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n","\n","54,0,1,male,65.0,0,1,61.9792,C,First,man,True,B,Cherbourg,no,False\n","\n","55,1,1,male,,0,0,35.5,S,First,man,True,C,Southampton,yes,True\n","\n","56,1,2,female,21.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True\n","\n","57,0,3,male,28.5,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True\n","\n","58,1,2,female,5.0,1,2,27.75,S,Second,child,False,,Southampton,yes,False\n","\n","59,0,3,male,11.0,5,2,46.9,S,Third,child,False,,Southampton,no,False\n","\n","60,0,3,male,22.0,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True\n","\n","61,1,1,female,38.0,0,0,80.0,,First,woman,False,B,,yes,True\n","\n","62,0,1,male,45.0,1,0,83.475,S,First,man,True,C,Southampton,no,False\n","\n","63,0,3,male,4.0,3,2,27.9,S,Third,child,False,,Southampton,no,False\n","\n","64,0,1,male,,0,0,27.7208,C,First,man,True,,Cherbourg,no,True\n","\n","65,1,3,male,,1,1,15.2458,C,Third,man,True,,Cherbourg,yes,False\n","\n","66,1,2,female,29.0,0,0,10.5,S,Second,woman,False,F,Southampton,yes,True\n","\n","67,0,3,male,19.0,0,0,8.1583,S,Third,man,True,,Southampton,no,True\n","\n","68,1,3,female,17.0,4,2,7.925,S,Third,woman,False,,Southampton,yes,False\n","\n","69,0,3,male,26.0,2,0,8.6625,S,Third,man,True,,Southampton,no,False\n","\n","70,0,2,male,32.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","71,0,3,female,16.0,5,2,46.9,S,Third,woman,False,,Southampton,no,False\n","\n","72,0,2,male,21.0,0,0,73.5,S,Second,man,True,,Southampton,no,True\n","\n","73,0,3,male,26.0,1,0,14.4542,C,Third,man,True,,Cherbourg,no,False\n","\n","74,1,3,male,32.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True\n","\n","75,0,3,male,25.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True\n","\n","76,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","77,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","78,1,2,male,0.83,0,2,29.0,S,Second,child,False,,Southampton,yes,False\n","\n","79,1,3,female,30.0,0,0,12.475,S,Third,woman,False,,Southampton,yes,True\n","\n","80,0,3,male,22.0,0,0,9.0,S,Third,man,True,,Southampton,no,True\n","\n","81,1,3,male,29.0,0,0,9.5,S,Third,man,True,,Southampton,yes,True\n","\n","82,1,3,female,,0,0,7.7875,Q,Third,woman,False,,Queenstown,yes,True\n","\n","83,0,1,male,28.0,0,0,47.1,S,First,man,True,,Southampton,no,True\n","\n","84,1,2,female,17.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True\n","\n","85,1,3,female,33.0,3,0,15.85,S,Third,woman,False,,Southampton,yes,False\n","\n","86,0,3,male,16.0,1,3,34.375,S,Third,man,True,,Southampton,no,False\n","\n","87,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","88,1,1,female,23.0,3,2,263.0,S,First,woman,False,C,Southampton,yes,False\n","\n","89,0,3,male,24.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","90,0,3,male,29.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","91,0,3,male,20.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True\n","\n","92,0,1,male,46.0,1,0,61.175,S,First,man,True,E,Southampton,no,False\n","\n","93,0,3,male,26.0,1,2,20.575,S,Third,man,True,,Southampton,no,False\n","\n","94,0,3,male,59.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n","\n","95,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","96,0,1,male,71.0,0,0,34.6542,C,First,man,True,A,Cherbourg,no,True\n","\n","97,1,1,male,23.0,0,1,63.3583,C,First,man,True,D,Cherbourg,yes,False\n","\n","98,1,2,female,34.0,0,1,23.0,S,Second,woman,False,,Southampton,yes,False\n","\n","99,0,2,male,34.0,1,0,26.0,S,Second,man,True,,Southampton,no,False\n","\n","100,0,3,female,28.0,0,0,7.8958,S,Third,woman,False,,Southampton,no,True\n","\n","101,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","102,0,1,male,21.0,0,1,77.2875,S,First,man,True,D,Southampton,no,False\n","\n","103,0,3,male,33.0,0,0,8.6542,S,Third,man,True,,Southampton,no,True\n","\n","104,0,3,male,37.0,2,0,7.925,S,Third,man,True,,Southampton,no,False\n","\n","105,0,3,male,28.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","106,1,3,female,21.0,0,0,7.65,S,Third,woman,False,,Southampton,yes,True\n","\n","107,1,3,male,,0,0,7.775,S,Third,man,True,,Southampton,yes,True\n","\n","108,0,3,male,38.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","109,1,3,female,,1,0,24.15,Q,Third,woman,False,,Queenstown,yes,False\n","\n","110,0,1,male,47.0,0,0,52.0,S,First,man,True,C,Southampton,no,True\n","\n","111,0,3,female,14.5,1,0,14.4542,C,Third,child,False,,Cherbourg,no,False\n","\n","112,0,3,male,22.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","113,0,3,female,20.0,1,0,9.825,S,Third,woman,False,,Southampton,no,False\n","\n","114,0,3,female,17.0,0,0,14.4583,C,Third,woman,False,,Cherbourg,no,True\n","\n","115,0,3,male,21.0,0,0,7.925,S,Third,man,True,,Southampton,no,True\n","\n","116,0,3,male,70.5,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n","\n","117,0,2,male,29.0,1,0,21.0,S,Second,man,True,,Southampton,no,False\n","\n","118,0,1,male,24.0,0,1,247.5208,C,First,man,True,B,Cherbourg,no,False\n","\n","119,0,3,female,2.0,4,2,31.275,S,Third,child,False,,Southampton,no,False\n","\n","120,0,2,male,21.0,2,0,73.5,S,Second,man,True,,Southampton,no,False\n","\n","121,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","122,0,2,male,32.5,1,0,30.0708,C,Second,man,True,,Cherbourg,no,False\n","\n","123,1,2,female,32.5,0,0,13.0,S,Second,woman,False,E,Southampton,yes,True\n","\n","124,0,1,male,54.0,0,1,77.2875,S,First,man,True,D,Southampton,no,False\n","\n","125,1,3,male,12.0,1,0,11.2417,C,Third,child,False,,Cherbourg,yes,False\n","\n","126,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n","\n","127,1,3,male,24.0,0,0,7.1417,S,Third,man,True,,Southampton,yes,True\n","\n","128,1,3,female,,1,1,22.3583,C,Third,woman,False,F,Cherbourg,yes,False\n","\n","129,0,3,male,45.0,0,0,6.975,S,Third,man,True,,Southampton,no,True\n","\n","130,0,3,male,33.0,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True\n","\n","131,0,3,male,20.0,0,0,7.05,S,Third,man,True,,Southampton,no,True\n","\n","132,0,3,female,47.0,1,0,14.5,S,Third,woman,False,,Southampton,no,False\n","\n","133,1,2,female,29.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n","\n","134,0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","135,0,2,male,23.0,0,0,15.0458,C,Second,man,True,,Cherbourg,no,True\n","\n","136,1,1,female,19.0,0,2,26.2833,S,First,woman,False,D,Southampton,yes,False\n","\n","137,0,1,male,37.0,1,0,53.1,S,First,man,True,C,Southampton,no,False\n","\n","138,0,3,male,16.0,0,0,9.2167,S,Third,man,True,,Southampton,no,True\n","\n","139,0,1,male,24.0,0,0,79.2,C,First,man,True,B,Cherbourg,no,True\n","\n","140,0,3,female,,0,2,15.2458,C,Third,woman,False,,Cherbourg,no,False\n","\n","141,1,3,female,22.0,0,0,7.75,S,Third,woman,False,,Southampton,yes,True\n","\n","142,1,3,female,24.0,1,0,15.85,S,Third,woman,False,,Southampton,yes,False\n","\n","143,0,3,male,19.0,0,0,6.75,Q,Third,man,True,,Queenstown,no,True\n","\n","144,0,2,male,18.0,0,0,11.5,S,Second,man,True,,Southampton,no,True\n","\n","145,0,2,male,19.0,1,1,36.75,S,Second,man,True,,Southampton,no,False\n","\n","146,1,3,male,27.0,0,0,7.7958,S,Third,man,True,,Southampton,yes,True\n","\n","147,0,3,female,9.0,2,2,34.375,S,Third,child,False,,Southampton,no,False\n","\n","148,0,2,male,36.5,0,2,26.0,S,Second,man,True,F,Southampton,no,False\n","\n","149,0,2,male,42.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","150,0,2,male,51.0,0,0,12.525,S,Second,man,True,,Southampton,no,True\n","\n","151,1,1,female,22.0,1,0,66.6,S,First,woman,False,C,Southampton,yes,False\n","\n","152,0,3,male,55.5,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","153,0,3,male,40.5,0,2,14.5,S,Third,man,True,,Southampton,no,False\n","\n","154,0,3,male,,0,0,7.3125,S,Third,man,True,,Southampton,no,True\n","\n","155,0,1,male,51.0,0,1,61.3792,C,First,man,True,,Cherbourg,no,False\n","\n","156,1,3,female,16.0,0,0,7.7333,Q,Third,woman,False,,Queenstown,yes,True\n","\n","157,0,3,male,30.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","158,0,3,male,,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n","\n","159,0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False\n","\n","160,0,3,male,44.0,0,1,16.1,S,Third,man,True,,Southampton,no,False\n","\n","161,1,2,female,40.0,0,0,15.75,S,Second,woman,False,,Southampton,yes,True\n","\n","162,0,3,male,26.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n","\n","163,0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n","\n","164,0,3,male,1.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False\n","\n","165,1,3,male,9.0,0,2,20.525,S,Third,child,False,,Southampton,yes,False\n","\n","166,1,1,female,,0,1,55.0,S,First,woman,False,E,Southampton,yes,False\n","\n","167,0,3,female,45.0,1,4,27.9,S,Third,woman,False,,Southampton,no,False\n","\n","168,0,1,male,,0,0,25.925,S,First,man,True,,Southampton,no,True\n","\n","169,0,3,male,28.0,0,0,56.4958,S,Third,man,True,,Southampton,no,True\n","\n","170,0,1,male,61.0,0,0,33.5,S,First,man,True,B,Southampton,no,True\n","\n","171,0,3,male,4.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False\n","\n","172,1,3,female,1.0,1,1,11.1333,S,Third,child,False,,Southampton,yes,False\n","\n","173,0,3,male,21.0,0,0,7.925,S,Third,man,True,,Southampton,no,True\n","\n","174,0,1,male,56.0,0,0,30.6958,C,First,man,True,A,Cherbourg,no,True\n","\n","175,0,3,male,18.0,1,1,7.8542,S,Third,man,True,,Southampton,no,False\n","\n","176,0,3,male,,3,1,25.4667,S,Third,man,True,,Southampton,no,False\n","\n","177,0,1,female,50.0,0,0,28.7125,C,First,woman,False,C,Cherbourg,no,True\n","\n","178,0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","179,0,3,male,36.0,0,0,0.0,S,Third,man,True,,Southampton,no,True\n","\n","180,0,3,female,,8,2,69.55,S,Third,woman,False,,Southampton,no,False\n","\n","181,0,2,male,,0,0,15.05,C,Second,man,True,,Cherbourg,no,True\n","\n","182,0,3,male,9.0,4,2,31.3875,S,Third,child,False,,Southampton,no,False\n","\n","183,1,2,male,1.0,2,1,39.0,S,Second,child,False,F,Southampton,yes,False\n","\n","184,1,3,female,4.0,0,2,22.025,S,Third,child,False,,Southampton,yes,False\n","\n","185,0,1,male,,0,0,50.0,S,First,man,True,A,Southampton,no,True\n","\n","186,1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False\n","\n","187,1,1,male,45.0,0,0,26.55,S,First,man,True,,Southampton,yes,True\n","\n","188,0,3,male,40.0,1,1,15.5,Q,Third,man,True,,Queenstown,no,False\n","\n","189,0,3,male,36.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","190,1,2,female,32.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n","\n","191,0,2,male,19.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","192,1,3,female,19.0,1,0,7.8542,S,Third,woman,False,,Southampton,yes,False\n","\n","193,1,2,male,3.0,1,1,26.0,S,Second,child,False,F,Southampton,yes,False\n","\n","194,1,1,female,44.0,0,0,27.7208,C,First,woman,False,B,Cherbourg,yes,True\n","\n","195,1,1,female,58.0,0,0,146.5208,C,First,woman,False,B,Cherbourg,yes,True\n","\n","196,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n","\n","197,0,3,male,42.0,0,1,8.4042,S,Third,man,True,,Southampton,no,False\n","\n","198,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n","\n","199,0,2,female,24.0,0,0,13.0,S,Second,woman,False,,Southampton,no,True\n","\n","200,0,3,male,28.0,0,0,9.5,S,Third,man,True,,Southampton,no,True\n","\n","201,0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False\n","\n","202,0,3,male,34.0,0,0,6.4958,S,Third,man,True,,Southampton,no,True\n","\n","203,0,3,male,45.5,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n","\n","204,1,3,male,18.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True\n","\n","205,0,3,female,2.0,0,1,10.4625,S,Third,child,False,G,Southampton,no,False\n","\n","206,0,3,male,32.0,1,0,15.85,S,Third,man,True,,Southampton,no,False\n","\n","207,1,3,male,26.0,0,0,18.7875,C,Third,man,True,,Cherbourg,yes,True\n","\n","208,1,3,female,16.0,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n","\n","209,1,1,male,40.0,0,0,31.0,C,First,man,True,A,Cherbourg,yes,True\n","\n","210,0,3,male,24.0,0,0,7.05,S,Third,man,True,,Southampton,no,True\n","\n","211,1,2,female,35.0,0,0,21.0,S,Second,woman,False,,Southampton,yes,True\n","\n","212,0,3,male,22.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n","\n","213,0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","214,0,3,male,,1,0,7.75,Q,Third,man,True,,Queenstown,no,False\n","\n","215,1,1,female,31.0,1,0,113.275,C,First,woman,False,D,Cherbourg,yes,False\n","\n","216,1,3,female,27.0,0,0,7.925,S,Third,woman,False,,Southampton,yes,True\n","\n","217,0,2,male,42.0,1,0,27.0,S,Second,man,True,,Southampton,no,False\n","\n","218,1,1,female,32.0,0,0,76.2917,C,First,woman,False,D,Cherbourg,yes,True\n","\n","219,0,2,male,30.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","220,1,3,male,16.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True\n","\n","221,0,2,male,27.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","222,0,3,male,51.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","223,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","224,1,1,male,38.0,1,0,90.0,S,First,man,True,C,Southampton,yes,False\n","\n","225,0,3,male,22.0,0,0,9.35,S,Third,man,True,,Southampton,no,True\n","\n","226,1,2,male,19.0,0,0,10.5,S,Second,man,True,,Southampton,yes,True\n","\n","227,0,3,male,20.5,0,0,7.25,S,Third,man,True,,Southampton,no,True\n","\n","228,0,2,male,18.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","229,0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False\n","\n","230,1,1,female,35.0,1,0,83.475,S,First,woman,False,C,Southampton,yes,False\n","\n","231,0,3,male,29.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n","\n","232,0,2,male,59.0,0,0,13.5,S,Second,man,True,,Southampton,no,True\n","\n","233,1,3,female,5.0,4,2,31.3875,S,Third,child,False,,Southampton,yes,False\n","\n","234,0,2,male,24.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","235,0,3,female,,0,0,7.55,S,Third,woman,False,,Southampton,no,True\n","\n","236,0,2,male,44.0,1,0,26.0,S,Second,man,True,,Southampton,no,False\n","\n","237,1,2,female,8.0,0,2,26.25,S,Second,child,False,,Southampton,yes,False\n","\n","238,0,2,male,19.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","239,0,2,male,33.0,0,0,12.275,S,Second,man,True,,Southampton,no,True\n","\n","240,0,3,female,,1,0,14.4542,C,Third,woman,False,,Cherbourg,no,False\n","\n","241,1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False\n","\n","242,0,2,male,29.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","243,0,3,male,22.0,0,0,7.125,S,Third,man,True,,Southampton,no,True\n","\n","244,0,3,male,30.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n","\n","245,0,1,male,44.0,2,0,90.0,Q,First,man,True,C,Queenstown,no,False\n","\n","246,0,3,female,25.0,0,0,7.775,S,Third,woman,False,,Southampton,no,True\n","\n","247,1,2,female,24.0,0,2,14.5,S,Second,woman,False,,Southampton,yes,False\n","\n","248,1,1,male,37.0,1,1,52.5542,S,First,man,True,D,Southampton,yes,False\n","\n","249,0,2,male,54.0,1,0,26.0,S,Second,man,True,,Southampton,no,False\n","\n","250,0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True\n","\n","251,0,3,female,29.0,1,1,10.4625,S,Third,woman,False,G,Southampton,no,False\n","\n","252,0,1,male,62.0,0,0,26.55,S,First,man,True,C,Southampton,no,True\n","\n","253,0,3,male,30.0,1,0,16.1,S,Third,man,True,,Southampton,no,False\n","\n","254,0,3,female,41.0,0,2,20.2125,S,Third,woman,False,,Southampton,no,False\n","\n","255,1,3,female,29.0,0,2,15.2458,C,Third,woman,False,,Cherbourg,yes,False\n","\n","256,1,1,female,,0,0,79.2,C,First,woman,False,,Cherbourg,yes,True\n","\n","257,1,1,female,30.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True\n","\n","258,1,1,female,35.0,0,0,512.3292,C,First,woman,False,,Cherbourg,yes,True\n","\n","259,1,2,female,50.0,0,1,26.0,S,Second,woman,False,,Southampton,yes,False\n","\n","260,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n","\n","261,1,3,male,3.0,4,2,31.3875,S,Third,child,False,,Southampton,yes,False\n","\n","262,0,1,male,52.0,1,1,79.65,S,First,man,True,E,Southampton,no,False\n","\n","263,0,1,male,40.0,0,0,0.0,S,First,man,True,B,Southampton,no,True\n","\n","264,0,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True\n","\n","265,0,2,male,36.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","266,0,3,male,16.0,4,1,39.6875,S,Third,man,True,,Southampton,no,False\n","\n","267,1,3,male,25.0,1,0,7.775,S,Third,man,True,,Southampton,yes,False\n","\n","268,1,1,female,58.0,0,1,153.4625,S,First,woman,False,C,Southampton,yes,False\n","\n","269,1,1,female,35.0,0,0,135.6333,S,First,woman,False,C,Southampton,yes,True\n","\n","270,0,1,male,,0,0,31.0,S,First,man,True,,Southampton,no,True\n","\n","271,1,3,male,25.0,0,0,0.0,S,Third,man,True,,Southampton,yes,True\n","\n","272,1,2,female,41.0,0,1,19.5,S,Second,woman,False,,Southampton,yes,False\n","\n","273,0,1,male,37.0,0,1,29.7,C,First,man,True,C,Cherbourg,no,False\n","\n","274,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n","\n","275,1,1,female,63.0,1,0,77.9583,S,First,woman,False,D,Southampton,yes,False\n","\n","276,0,3,female,45.0,0,0,7.75,S,Third,woman,False,,Southampton,no,True\n","\n","277,0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True\n","\n","278,0,3,male,7.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False\n","\n","279,1,3,female,35.0,1,1,20.25,S,Third,woman,False,,Southampton,yes,False\n","\n","280,0,3,male,65.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n","\n","281,0,3,male,28.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True\n","\n","282,0,3,male,16.0,0,0,9.5,S,Third,man,True,,Southampton,no,True\n","\n","283,1,3,male,19.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True\n","\n","284,0,1,male,,0,0,26.0,S,First,man,True,A,Southampton,no,True\n","\n","285,0,3,male,33.0,0,0,8.6625,C,Third,man,True,,Cherbourg,no,True\n","\n","286,1,3,male,30.0,0,0,9.5,S,Third,man,True,,Southampton,yes,True\n","\n","287,0,3,male,22.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","288,1,2,male,42.0,0,0,13.0,S,Second,man,True,,Southampton,yes,True\n","\n","289,1,3,female,22.0,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n","\n","290,1,1,female,26.0,0,0,78.85,S,First,woman,False,,Southampton,yes,True\n","\n","291,1,1,female,19.0,1,0,91.0792,C,First,woman,False,B,Cherbourg,yes,False\n","\n","292,0,2,male,36.0,0,0,12.875,C,Second,man,True,D,Cherbourg,no,True\n","\n","293,0,3,female,24.0,0,0,8.85,S,Third,woman,False,,Southampton,no,True\n","\n","294,0,3,male,24.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","295,0,1,male,,0,0,27.7208,C,First,man,True,,Cherbourg,no,True\n","\n","296,0,3,male,23.5,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True\n","\n","297,0,1,female,2.0,1,2,151.55,S,First,child,False,C,Southampton,no,False\n","\n","298,1,1,male,,0,0,30.5,S,First,man,True,C,Southampton,yes,True\n","\n","299,1,1,female,50.0,0,1,247.5208,C,First,woman,False,B,Cherbourg,yes,False\n","\n","300,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n","\n","301,1,3,male,,2,0,23.25,Q,Third,man,True,,Queenstown,yes,False\n","\n","302,0,3,male,19.0,0,0,0.0,S,Third,man,True,,Southampton,no,True\n","\n","303,1,2,female,,0,0,12.35,Q,Second,woman,False,E,Queenstown,yes,True\n","\n","304,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","305,1,1,male,0.92,1,2,151.55,S,First,child,False,C,Southampton,yes,False\n","\n","306,1,1,female,,0,0,110.8833,C,First,woman,False,,Cherbourg,yes,True\n","\n","307,1,1,female,17.0,1,0,108.9,C,First,woman,False,C,Cherbourg,yes,False\n","\n","308,0,2,male,30.0,1,0,24.0,C,Second,man,True,,Cherbourg,no,False\n","\n","309,1,1,female,30.0,0,0,56.9292,C,First,woman,False,E,Cherbourg,yes,True\n","\n","310,1,1,female,24.0,0,0,83.1583,C,First,woman,False,C,Cherbourg,yes,True\n","\n","311,1,1,female,18.0,2,2,262.375,C,First,woman,False,B,Cherbourg,yes,False\n","\n","312,0,2,female,26.0,1,1,26.0,S,Second,woman,False,,Southampton,no,False\n","\n","313,0,3,male,28.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","314,0,2,male,43.0,1,1,26.25,S,Second,man,True,,Southampton,no,False\n","\n","315,1,3,female,26.0,0,0,7.8542,S,Third,woman,False,,Southampton,yes,True\n","\n","316,1,2,female,24.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n","\n","317,0,2,male,54.0,0,0,14.0,S,Second,man,True,,Southampton,no,True\n","\n","318,1,1,female,31.0,0,2,164.8667,S,First,woman,False,C,Southampton,yes,False\n","\n","319,1,1,female,40.0,1,1,134.5,C,First,woman,False,E,Cherbourg,yes,False\n","\n","320,0,3,male,22.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n","\n","321,0,3,male,27.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","322,1,2,female,30.0,0,0,12.35,Q,Second,woman,False,,Queenstown,yes,True\n","\n","323,1,2,female,22.0,1,1,29.0,S,Second,woman,False,,Southampton,yes,False\n","\n","324,0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False\n","\n","325,1,1,female,36.0,0,0,135.6333,C,First,woman,False,C,Cherbourg,yes,True\n","\n","326,0,3,male,61.0,0,0,6.2375,S,Third,man,True,,Southampton,no,True\n","\n","327,1,2,female,36.0,0,0,13.0,S,Second,woman,False,D,Southampton,yes,True\n","\n","328,1,3,female,31.0,1,1,20.525,S,Third,woman,False,,Southampton,yes,False\n","\n","329,1,1,female,16.0,0,1,57.9792,C,First,woman,False,B,Cherbourg,yes,False\n","\n","330,1,3,female,,2,0,23.25,Q,Third,woman,False,,Queenstown,yes,False\n","\n","331,0,1,male,45.5,0,0,28.5,S,First,man,True,C,Southampton,no,True\n","\n","332,0,1,male,38.0,0,1,153.4625,S,First,man,True,C,Southampton,no,False\n","\n","333,0,3,male,16.0,2,0,18.0,S,Third,man,True,,Southampton,no,False\n","\n","334,1,1,female,,1,0,133.65,S,First,woman,False,,Southampton,yes,False\n","\n","335,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","336,0,1,male,29.0,1,0,66.6,S,First,man,True,C,Southampton,no,False\n","\n","337,1,1,female,41.0,0,0,134.5,C,First,woman,False,E,Cherbourg,yes,True\n","\n","338,1,3,male,45.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True\n","\n","339,0,1,male,45.0,0,0,35.5,S,First,man,True,,Southampton,no,True\n","\n","340,1,2,male,2.0,1,1,26.0,S,Second,child,False,F,Southampton,yes,False\n","\n","341,1,1,female,24.0,3,2,263.0,S,First,woman,False,C,Southampton,yes,False\n","\n","342,0,2,male,28.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","343,0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","344,0,2,male,36.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","345,1,2,female,24.0,0,0,13.0,S,Second,woman,False,F,Southampton,yes,True\n","\n","346,1,2,female,40.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n","\n","347,1,3,female,,1,0,16.1,S,Third,woman,False,,Southampton,yes,False\n","\n","348,1,3,male,3.0,1,1,15.9,S,Third,child,False,,Southampton,yes,False\n","\n","349,0,3,male,42.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n","\n","350,0,3,male,23.0,0,0,9.225,S,Third,man,True,,Southampton,no,True\n","\n","351,0,1,male,,0,0,35.0,S,First,man,True,C,Southampton,no,True\n","\n","352,0,3,male,15.0,1,1,7.2292,C,Third,child,False,,Cherbourg,no,False\n","\n","353,0,3,male,25.0,1,0,17.8,S,Third,man,True,,Southampton,no,False\n","\n","354,0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n","\n","355,0,3,male,28.0,0,0,9.5,S,Third,man,True,,Southampton,no,True\n","\n","356,1,1,female,22.0,0,1,55.0,S,First,woman,False,E,Southampton,yes,False\n","\n","357,0,2,female,38.0,0,0,13.0,S,Second,woman,False,,Southampton,no,True\n","\n","358,1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True\n","\n","359,1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True\n","\n","360,0,3,male,40.0,1,4,27.9,S,Third,man,True,,Southampton,no,False\n","\n","361,0,2,male,29.0,1,0,27.7208,C,Second,man,True,,Cherbourg,no,False\n","\n","362,0,3,female,45.0,0,1,14.4542,C,Third,woman,False,,Cherbourg,no,False\n","\n","363,0,3,male,35.0,0,0,7.05,S,Third,man,True,,Southampton,no,True\n","\n","364,0,3,male,,1,0,15.5,Q,Third,man,True,,Queenstown,no,False\n","\n","365,0,3,male,30.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n","\n","366,1,1,female,60.0,1,0,75.25,C,First,woman,False,D,Cherbourg,yes,False\n","\n","367,1,3,female,,0,0,7.2292,C,Third,woman,False,,Cherbourg,yes,True\n","\n","368,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n","\n","369,1,1,female,24.0,0,0,69.3,C,First,woman,False,B,Cherbourg,yes,True\n","\n","370,1,1,male,25.0,1,0,55.4417,C,First,man,True,E,Cherbourg,yes,False\n","\n","371,0,3,male,18.0,1,0,6.4958,S,Third,man,True,,Southampton,no,False\n","\n","372,0,3,male,19.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","373,0,1,male,22.0,0,0,135.6333,C,First,man,True,,Cherbourg,no,True\n","\n","374,0,3,female,3.0,3,1,21.075,S,Third,child,False,,Southampton,no,False\n","\n","375,1,1,female,,1,0,82.1708,C,First,woman,False,,Cherbourg,yes,False\n","\n","376,1,3,female,22.0,0,0,7.25,S,Third,woman,False,,Southampton,yes,True\n","\n","377,0,1,male,27.0,0,2,211.5,C,First,man,True,C,Cherbourg,no,False\n","\n","378,0,3,male,20.0,0,0,4.0125,C,Third,man,True,,Cherbourg,no,True\n","\n","379,0,3,male,19.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n","\n","380,1,1,female,42.0,0,0,227.525,C,First,woman,False,,Cherbourg,yes,True\n","\n","381,1,3,female,1.0,0,2,15.7417,C,Third,child,False,,Cherbourg,yes,False\n","\n","382,0,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,no,True\n","\n","383,1,1,female,35.0,1,0,52.0,S,First,woman,False,,Southampton,yes,False\n","\n","384,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","385,0,2,male,18.0,0,0,73.5,S,Second,man,True,,Southampton,no,True\n","\n","386,0,3,male,1.0,5,2,46.9,S,Third,child,False,,Southampton,no,False\n","\n","387,1,2,female,36.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n","\n","388,0,3,male,,0,0,7.7292,Q,Third,man,True,,Queenstown,no,True\n","\n","389,1,2,female,17.0,0,0,12.0,C,Second,woman,False,,Cherbourg,yes,True\n","\n","390,1,1,male,36.0,1,2,120.0,S,First,man,True,B,Southampton,yes,False\n","\n","391,1,3,male,21.0,0,0,7.7958,S,Third,man,True,,Southampton,yes,True\n","\n","392,0,3,male,28.0,2,0,7.925,S,Third,man,True,,Southampton,no,False\n","\n","393,1,1,female,23.0,1,0,113.275,C,First,woman,False,D,Cherbourg,yes,False\n","\n","394,1,3,female,24.0,0,2,16.7,S,Third,woman,False,G,Southampton,yes,False\n","\n","395,0,3,male,22.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True\n","\n","396,0,3,female,31.0,0,0,7.8542,S,Third,woman,False,,Southampton,no,True\n","\n","397,0,2,male,46.0,0,0,26.0,S,Second,man,True,,Southampton,no,True\n","\n","398,0,2,male,23.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","399,1,2,female,28.0,0,0,12.65,S,Second,woman,False,,Southampton,yes,True\n","\n","400,1,3,male,39.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True\n","\n","401,0,3,male,26.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","402,0,3,female,21.0,1,0,9.825,S,Third,woman,False,,Southampton,no,False\n","\n","403,0,3,male,28.0,1,0,15.85,S,Third,man,True,,Southampton,no,False\n","\n","404,0,3,female,20.0,0,0,8.6625,S,Third,woman,False,,Southampton,no,True\n","\n","405,0,2,male,34.0,1,0,21.0,S,Second,man,True,,Southampton,no,False\n","\n","406,0,3,male,51.0,0,0,7.75,S,Third,man,True,,Southampton,no,True\n","\n","407,1,2,male,3.0,1,1,18.75,S,Second,child,False,,Southampton,yes,False\n","\n","408,0,3,male,21.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n","\n","409,0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False\n","\n","410,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","411,0,3,male,,0,0,6.8583,Q,Third,man,True,,Queenstown,no,True\n","\n","412,1,1,female,33.0,1,0,90.0,Q,First,woman,False,C,Queenstown,yes,False\n","\n","413,0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True\n","\n","414,1,3,male,44.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True\n","\n","415,0,3,female,,0,0,8.05,S,Third,woman,False,,Southampton,no,True\n","\n","416,1,2,female,34.0,1,1,32.5,S,Second,woman,False,,Southampton,yes,False\n","\n","417,1,2,female,18.0,0,2,13.0,S,Second,woman,False,,Southampton,yes,False\n","\n","418,0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","419,0,3,female,10.0,0,2,24.15,S,Third,child,False,,Southampton,no,False\n","\n","420,0,3,male,,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True\n","\n","421,0,3,male,21.0,0,0,7.7333,Q,Third,man,True,,Queenstown,no,True\n","\n","422,0,3,male,29.0,0,0,7.875,S,Third,man,True,,Southampton,no,True\n","\n","423,0,3,female,28.0,1,1,14.4,S,Third,woman,False,,Southampton,no,False\n","\n","424,0,3,male,18.0,1,1,20.2125,S,Third,man,True,,Southampton,no,False\n","\n","425,0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True\n","\n","426,1,2,female,28.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n","\n","427,1,2,female,19.0,0,0,26.0,S,Second,woman,False,,Southampton,yes,True\n","\n","428,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n","\n","429,1,3,male,32.0,0,0,8.05,S,Third,man,True,E,Southampton,yes,True\n","\n","430,1,1,male,28.0,0,0,26.55,S,First,man,True,C,Southampton,yes,True\n","\n","431,1,3,female,,1,0,16.1,S,Third,woman,False,,Southampton,yes,False\n","\n","432,1,2,female,42.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n","\n","433,0,3,male,17.0,0,0,7.125,S,Third,man,True,,Southampton,no,True\n","\n","434,0,1,male,50.0,1,0,55.9,S,First,man,True,E,Southampton,no,False\n","\n","435,1,1,female,14.0,1,2,120.0,S,First,child,False,B,Southampton,yes,False\n","\n","436,0,3,female,21.0,2,2,34.375,S,Third,woman,False,,Southampton,no,False\n","\n","437,1,2,female,24.0,2,3,18.75,S,Second,woman,False,,Southampton,yes,False\n","\n","438,0,1,male,64.0,1,4,263.0,S,First,man,True,C,Southampton,no,False\n","\n","439,0,2,male,31.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","440,1,2,female,45.0,1,1,26.25,S,Second,woman,False,,Southampton,yes,False\n","\n","441,0,3,male,20.0,0,0,9.5,S,Third,man,True,,Southampton,no,True\n","\n","442,0,3,male,25.0,1,0,7.775,S,Third,man,True,,Southampton,no,False\n","\n","443,1,2,female,28.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n","\n","444,1,3,male,,0,0,8.1125,S,Third,man,True,,Southampton,yes,True\n","\n","445,1,1,male,4.0,0,2,81.8583,S,First,child,False,A,Southampton,yes,False\n","\n","446,1,2,female,13.0,0,1,19.5,S,Second,child,False,,Southampton,yes,False\n","\n","447,1,1,male,34.0,0,0,26.55,S,First,man,True,,Southampton,yes,True\n","\n","448,1,3,female,5.0,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False\n","\n","449,1,1,male,52.0,0,0,30.5,S,First,man,True,C,Southampton,yes,True\n","\n","450,0,2,male,36.0,1,2,27.75,S,Second,man,True,,Southampton,no,False\n","\n","451,0,3,male,,1,0,19.9667,S,Third,man,True,,Southampton,no,False\n","\n","452,0,1,male,30.0,0,0,27.75,C,First,man,True,C,Cherbourg,no,True\n","\n","453,1,1,male,49.0,1,0,89.1042,C,First,man,True,C,Cherbourg,yes,False\n","\n","454,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","455,1,3,male,29.0,0,0,7.8958,C,Third,man,True,,Cherbourg,yes,True\n","\n","456,0,1,male,65.0,0,0,26.55,S,First,man,True,E,Southampton,no,True\n","\n","457,1,1,female,,1,0,51.8625,S,First,woman,False,D,Southampton,yes,False\n","\n","458,1,2,female,50.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True\n","\n","459,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n","\n","460,1,1,male,48.0,0,0,26.55,S,First,man,True,E,Southampton,yes,True\n","\n","461,0,3,male,34.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","462,0,1,male,47.0,0,0,38.5,S,First,man,True,E,Southampton,no,True\n","\n","463,0,2,male,48.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","464,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","465,0,3,male,38.0,0,0,7.05,S,Third,man,True,,Southampton,no,True\n","\n","466,0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True\n","\n","467,0,1,male,56.0,0,0,26.55,S,First,man,True,,Southampton,no,True\n","\n","468,0,3,male,,0,0,7.725,Q,Third,man,True,,Queenstown,no,True\n","\n","469,1,3,female,0.75,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False\n","\n","470,0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True\n","\n","471,0,3,male,38.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n","\n","472,1,2,female,33.0,1,2,27.75,S,Second,woman,False,,Southampton,yes,False\n","\n","473,1,2,female,23.0,0,0,13.7917,C,Second,woman,False,D,Cherbourg,yes,True\n","\n","474,0,3,female,22.0,0,0,9.8375,S,Third,woman,False,,Southampton,no,True\n","\n","475,0,1,male,,0,0,52.0,S,First,man,True,A,Southampton,no,True\n","\n","476,0,2,male,34.0,1,0,21.0,S,Second,man,True,,Southampton,no,False\n","\n","477,0,3,male,29.0,1,0,7.0458,S,Third,man,True,,Southampton,no,False\n","\n","478,0,3,male,22.0,0,0,7.5208,S,Third,man,True,,Southampton,no,True\n","\n","479,1,3,female,2.0,0,1,12.2875,S,Third,child,False,,Southampton,yes,False\n","\n","480,0,3,male,9.0,5,2,46.9,S,Third,child,False,,Southampton,no,False\n","\n","481,0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True\n","\n","482,0,3,male,50.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","483,1,3,female,63.0,0,0,9.5875,S,Third,woman,False,,Southampton,yes,True\n","\n","484,1,1,male,25.0,1,0,91.0792,C,First,man,True,B,Cherbourg,yes,False\n","\n","485,0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False\n","\n","486,1,1,female,35.0,1,0,90.0,S,First,woman,False,C,Southampton,yes,False\n","\n","487,0,1,male,58.0,0,0,29.7,C,First,man,True,B,Cherbourg,no,True\n","\n","488,0,3,male,30.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","489,1,3,male,9.0,1,1,15.9,S,Third,child,False,,Southampton,yes,False\n","\n","490,0,3,male,,1,0,19.9667,S,Third,man,True,,Southampton,no,False\n","\n","491,0,3,male,21.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n","\n","492,0,1,male,55.0,0,0,30.5,S,First,man,True,C,Southampton,no,True\n","\n","493,0,1,male,71.0,0,0,49.5042,C,First,man,True,,Cherbourg,no,True\n","\n","494,0,3,male,21.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","495,0,3,male,,0,0,14.4583,C,Third,man,True,,Cherbourg,no,True\n","\n","496,1,1,female,54.0,1,0,78.2667,C,First,woman,False,D,Cherbourg,yes,False\n","\n","497,0,3,male,,0,0,15.1,S,Third,man,True,,Southampton,no,True\n","\n","498,0,1,female,25.0,1,2,151.55,S,First,woman,False,C,Southampton,no,False\n","\n","499,0,3,male,24.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True\n","\n","500,0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n","\n","501,0,3,female,21.0,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True\n","\n","502,0,3,female,,0,0,7.6292,Q,Third,woman,False,,Queenstown,no,True\n","\n","503,0,3,female,37.0,0,0,9.5875,S,Third,woman,False,,Southampton,no,True\n","\n","504,1,1,female,16.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True\n","\n","505,0,1,male,18.0,1,0,108.9,C,First,man,True,C,Cherbourg,no,False\n","\n","506,1,2,female,33.0,0,2,26.0,S,Second,woman,False,,Southampton,yes,False\n","\n","507,1,1,male,,0,0,26.55,S,First,man,True,,Southampton,yes,True\n","\n","508,0,3,male,28.0,0,0,22.525,S,Third,man,True,,Southampton,no,True\n","\n","509,1,3,male,26.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True\n","\n","510,1,3,male,29.0,0,0,7.75,Q,Third,man,True,,Queenstown,yes,True\n","\n","511,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","512,1,1,male,36.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True\n","\n","513,1,1,female,54.0,1,0,59.4,C,First,woman,False,,Cherbourg,yes,False\n","\n","514,0,3,male,24.0,0,0,7.4958,S,Third,man,True,,Southampton,no,True\n","\n","515,0,1,male,47.0,0,0,34.0208,S,First,man,True,D,Southampton,no,True\n","\n","516,1,2,female,34.0,0,0,10.5,S,Second,woman,False,F,Southampton,yes,True\n","\n","517,0,3,male,,0,0,24.15,Q,Third,man,True,,Queenstown,no,True\n","\n","518,1,2,female,36.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n","\n","519,0,3,male,32.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","520,1,1,female,30.0,0,0,93.5,S,First,woman,False,B,Southampton,yes,True\n","\n","521,0,3,male,22.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","522,0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n","\n","523,1,1,female,44.0,0,1,57.9792,C,First,woman,False,B,Cherbourg,yes,False\n","\n","524,0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True\n","\n","525,0,3,male,40.5,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n","\n","526,1,2,female,50.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True\n","\n","527,0,1,male,,0,0,221.7792,S,First,man,True,C,Southampton,no,True\n","\n","528,0,3,male,39.0,0,0,7.925,S,Third,man,True,,Southampton,no,True\n","\n","529,0,2,male,23.0,2,1,11.5,S,Second,man,True,,Southampton,no,False\n","\n","530,1,2,female,2.0,1,1,26.0,S,Second,child,False,,Southampton,yes,False\n","\n","531,0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True\n","\n","532,0,3,male,17.0,1,1,7.2292,C,Third,man,True,,Cherbourg,no,False\n","\n","533,1,3,female,,0,2,22.3583,C,Third,woman,False,,Cherbourg,yes,False\n","\n","534,0,3,female,30.0,0,0,8.6625,S,Third,woman,False,,Southampton,no,True\n","\n","535,1,2,female,7.0,0,2,26.25,S,Second,child,False,,Southampton,yes,False\n","\n","536,0,1,male,45.0,0,0,26.55,S,First,man,True,B,Southampton,no,True\n","\n","537,1,1,female,30.0,0,0,106.425,C,First,woman,False,,Cherbourg,yes,True\n","\n","538,0,3,male,,0,0,14.5,S,Third,man,True,,Southampton,no,True\n","\n","539,1,1,female,22.0,0,2,49.5,C,First,woman,False,B,Cherbourg,yes,False\n","\n","540,1,1,female,36.0,0,2,71.0,S,First,woman,False,B,Southampton,yes,False\n","\n","541,0,3,female,9.0,4,2,31.275,S,Third,child,False,,Southampton,no,False\n","\n","542,0,3,female,11.0,4,2,31.275,S,Third,child,False,,Southampton,no,False\n","\n","543,1,2,male,32.0,1,0,26.0,S,Second,man,True,,Southampton,yes,False\n","\n","544,0,1,male,50.0,1,0,106.425,C,First,man,True,C,Cherbourg,no,False\n","\n","545,0,1,male,64.0,0,0,26.0,S,First,man,True,,Southampton,no,True\n","\n","546,1,2,female,19.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False\n","\n","547,1,2,male,,0,0,13.8625,C,Second,man,True,,Cherbourg,yes,True\n","\n","548,0,3,male,33.0,1,1,20.525,S,Third,man,True,,Southampton,no,False\n","\n","549,1,2,male,8.0,1,1,36.75,S,Second,child,False,,Southampton,yes,False\n","\n","550,1,1,male,17.0,0,2,110.8833,C,First,man,True,C,Cherbourg,yes,False\n","\n","551,0,2,male,27.0,0,0,26.0,S,Second,man,True,,Southampton,no,True\n","\n","552,0,3,male,,0,0,7.8292,Q,Third,man,True,,Queenstown,no,True\n","\n","553,1,3,male,22.0,0,0,7.225,C,Third,man,True,,Cherbourg,yes,True\n","\n","554,1,3,female,22.0,0,0,7.775,S,Third,woman,False,,Southampton,yes,True\n","\n","555,0,1,male,62.0,0,0,26.55,S,First,man,True,,Southampton,no,True\n","\n","556,1,1,female,48.0,1,0,39.6,C,First,woman,False,A,Cherbourg,yes,False\n","\n","557,0,1,male,,0,0,227.525,C,First,man,True,,Cherbourg,no,True\n","\n","558,1,1,female,39.0,1,1,79.65,S,First,woman,False,E,Southampton,yes,False\n","\n","559,1,3,female,36.0,1,0,17.4,S,Third,woman,False,,Southampton,yes,False\n","\n","560,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n","\n","561,0,3,male,40.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","562,0,2,male,28.0,0,0,13.5,S,Second,man,True,,Southampton,no,True\n","\n","563,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","564,0,3,female,,0,0,8.05,S,Third,woman,False,,Southampton,no,True\n","\n","565,0,3,male,24.0,2,0,24.15,S,Third,man,True,,Southampton,no,False\n","\n","566,0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","567,0,3,female,29.0,0,4,21.075,S,Third,woman,False,,Southampton,no,False\n","\n","568,0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True\n","\n","569,1,3,male,32.0,0,0,7.8542,S,Third,man,True,,Southampton,yes,True\n","\n","570,1,2,male,62.0,0,0,10.5,S,Second,man,True,,Southampton,yes,True\n","\n","571,1,1,female,53.0,2,0,51.4792,S,First,woman,False,C,Southampton,yes,False\n","\n","572,1,1,male,36.0,0,0,26.3875,S,First,man,True,E,Southampton,yes,True\n","\n","573,1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True\n","\n","574,0,3,male,16.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","575,0,3,male,19.0,0,0,14.5,S,Third,man,True,,Southampton,no,True\n","\n","576,1,2,female,34.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n","\n","577,1,1,female,39.0,1,0,55.9,S,First,woman,False,E,Southampton,yes,False\n","\n","578,0,3,female,,1,0,14.4583,C,Third,woman,False,,Cherbourg,no,False\n","\n","579,1,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True\n","\n","580,1,2,female,25.0,1,1,30.0,S,Second,woman,False,,Southampton,yes,False\n","\n","581,1,1,female,39.0,1,1,110.8833,C,First,woman,False,C,Cherbourg,yes,False\n","\n","582,0,2,male,54.0,0,0,26.0,S,Second,man,True,,Southampton,no,True\n","\n","583,0,1,male,36.0,0,0,40.125,C,First,man,True,A,Cherbourg,no,True\n","\n","584,0,3,male,,0,0,8.7125,C,Third,man,True,,Cherbourg,no,True\n","\n","585,1,1,female,18.0,0,2,79.65,S,First,woman,False,E,Southampton,yes,False\n","\n","586,0,2,male,47.0,0,0,15.0,S,Second,man,True,,Southampton,no,True\n","\n","587,1,1,male,60.0,1,1,79.2,C,First,man,True,B,Cherbourg,yes,False\n","\n","588,0,3,male,22.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","589,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","590,0,3,male,35.0,0,0,7.125,S,Third,man,True,,Southampton,no,True\n","\n","591,1,1,female,52.0,1,0,78.2667,C,First,woman,False,D,Cherbourg,yes,False\n","\n","592,0,3,male,47.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n","\n","593,0,3,female,,0,2,7.75,Q,Third,woman,False,,Queenstown,no,False\n","\n","594,0,2,male,37.0,1,0,26.0,S,Second,man,True,,Southampton,no,False\n","\n","595,0,3,male,36.0,1,1,24.15,S,Third,man,True,,Southampton,no,False\n","\n","596,1,2,female,,0,0,33.0,S,Second,woman,False,,Southampton,yes,True\n","\n","597,0,3,male,49.0,0,0,0.0,S,Third,man,True,,Southampton,no,True\n","\n","598,0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n","\n","599,1,1,male,49.0,1,0,56.9292,C,First,man,True,A,Cherbourg,yes,False\n","\n","600,1,2,female,24.0,2,1,27.0,S,Second,woman,False,,Southampton,yes,False\n","\n","601,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","602,0,1,male,,0,0,42.4,S,First,man,True,,Southampton,no,True\n","\n","603,0,3,male,44.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","604,1,1,male,35.0,0,0,26.55,C,First,man,True,,Cherbourg,yes,True\n","\n","605,0,3,male,36.0,1,0,15.55,S,Third,man,True,,Southampton,no,False\n","\n","606,0,3,male,30.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","607,1,1,male,27.0,0,0,30.5,S,First,man,True,,Southampton,yes,True\n","\n","608,1,2,female,22.0,1,2,41.5792,C,Second,woman,False,,Cherbourg,yes,False\n","\n","609,1,1,female,40.0,0,0,153.4625,S,First,woman,False,C,Southampton,yes,True\n","\n","610,0,3,female,39.0,1,5,31.275,S,Third,woman,False,,Southampton,no,False\n","\n","611,0,3,male,,0,0,7.05,S,Third,man,True,,Southampton,no,True\n","\n","612,1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False\n","\n","613,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n","\n","614,0,3,male,35.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","615,1,2,female,24.0,1,2,65.0,S,Second,woman,False,,Southampton,yes,False\n","\n","616,0,3,male,34.0,1,1,14.4,S,Third,man,True,,Southampton,no,False\n","\n","617,0,3,female,26.0,1,0,16.1,S,Third,woman,False,,Southampton,no,False\n","\n","618,1,2,female,4.0,2,1,39.0,S,Second,child,False,F,Southampton,yes,False\n","\n","619,0,2,male,26.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","620,0,3,male,27.0,1,0,14.4542,C,Third,man,True,,Cherbourg,no,False\n","\n","621,1,1,male,42.0,1,0,52.5542,S,First,man,True,D,Southampton,yes,False\n","\n","622,1,3,male,20.0,1,1,15.7417,C,Third,man,True,,Cherbourg,yes,False\n","\n","623,0,3,male,21.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True\n","\n","624,0,3,male,21.0,0,0,16.1,S,Third,man,True,,Southampton,no,True\n","\n","625,0,1,male,61.0,0,0,32.3208,S,First,man,True,D,Southampton,no,True\n","\n","626,0,2,male,57.0,0,0,12.35,Q,Second,man,True,,Queenstown,no,True\n","\n","627,1,1,female,21.0,0,0,77.9583,S,First,woman,False,D,Southampton,yes,True\n","\n","628,0,3,male,26.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","629,0,3,male,,0,0,7.7333,Q,Third,man,True,,Queenstown,no,True\n","\n","630,1,1,male,80.0,0,0,30.0,S,First,man,True,A,Southampton,yes,True\n","\n","631,0,3,male,51.0,0,0,7.0542,S,Third,man,True,,Southampton,no,True\n","\n","632,1,1,male,32.0,0,0,30.5,C,First,man,True,B,Cherbourg,yes,True\n","\n","633,0,1,male,,0,0,0.0,S,First,man,True,,Southampton,no,True\n","\n","634,0,3,female,9.0,3,2,27.9,S,Third,child,False,,Southampton,no,False\n","\n","635,1,2,female,28.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n","\n","636,0,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,no,True\n","\n","637,0,2,male,31.0,1,1,26.25,S,Second,man,True,,Southampton,no,False\n","\n","638,0,3,female,41.0,0,5,39.6875,S,Third,woman,False,,Southampton,no,False\n","\n","639,0,3,male,,1,0,16.1,S,Third,man,True,,Southampton,no,False\n","\n","640,0,3,male,20.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True\n","\n","641,1,1,female,24.0,0,0,69.3,C,First,woman,False,B,Cherbourg,yes,True\n","\n","642,0,3,female,2.0,3,2,27.9,S,Third,child,False,,Southampton,no,False\n","\n","643,1,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,yes,True\n","\n","644,1,3,female,0.75,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False\n","\n","645,1,1,male,48.0,1,0,76.7292,C,First,man,True,D,Cherbourg,yes,False\n","\n","646,0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","647,1,1,male,56.0,0,0,35.5,C,First,man,True,A,Cherbourg,yes,True\n","\n","648,0,3,male,,0,0,7.55,S,Third,man,True,,Southampton,no,True\n","\n","649,1,3,female,23.0,0,0,7.55,S,Third,woman,False,,Southampton,yes,True\n","\n","650,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","651,1,2,female,18.0,0,1,23.0,S,Second,woman,False,,Southampton,yes,False\n","\n","652,0,3,male,21.0,0,0,8.4333,S,Third,man,True,,Southampton,no,True\n","\n","653,1,3,female,,0,0,7.8292,Q,Third,woman,False,,Queenstown,yes,True\n","\n","654,0,3,female,18.0,0,0,6.75,Q,Third,woman,False,,Queenstown,no,True\n","\n","655,0,2,male,24.0,2,0,73.5,S,Second,man,True,,Southampton,no,False\n","\n","656,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","657,0,3,female,32.0,1,1,15.5,Q,Third,woman,False,,Queenstown,no,False\n","\n","658,0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","659,0,1,male,58.0,0,2,113.275,C,First,man,True,D,Cherbourg,no,False\n","\n","660,1,1,male,50.0,2,0,133.65,S,First,man,True,,Southampton,yes,False\n","\n","661,0,3,male,40.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n","\n","662,0,1,male,47.0,0,0,25.5875,S,First,man,True,E,Southampton,no,True\n","\n","663,0,3,male,36.0,0,0,7.4958,S,Third,man,True,,Southampton,no,True\n","\n","664,1,3,male,20.0,1,0,7.925,S,Third,man,True,,Southampton,yes,False\n","\n","665,0,2,male,32.0,2,0,73.5,S,Second,man,True,,Southampton,no,False\n","\n","666,0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","667,0,3,male,,0,0,7.775,S,Third,man,True,,Southampton,no,True\n","\n","668,0,3,male,43.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","669,1,1,female,,1,0,52.0,S,First,woman,False,C,Southampton,yes,False\n","\n","670,1,2,female,40.0,1,1,39.0,S,Second,woman,False,,Southampton,yes,False\n","\n","671,0,1,male,31.0,1,0,52.0,S,First,man,True,B,Southampton,no,False\n","\n","672,0,2,male,70.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","673,1,2,male,31.0,0,0,13.0,S,Second,man,True,,Southampton,yes,True\n","\n","674,0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True\n","\n","675,0,3,male,18.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n","\n","676,0,3,male,24.5,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","677,1,3,female,18.0,0,0,9.8417,S,Third,woman,False,,Southampton,yes,True\n","\n","678,0,3,female,43.0,1,6,46.9,S,Third,woman,False,,Southampton,no,False\n","\n","679,1,1,male,36.0,0,1,512.3292,C,First,man,True,B,Cherbourg,yes,False\n","\n","680,0,3,female,,0,0,8.1375,Q,Third,woman,False,,Queenstown,no,True\n","\n","681,1,1,male,27.0,0,0,76.7292,C,First,man,True,D,Cherbourg,yes,True\n","\n","682,0,3,male,20.0,0,0,9.225,S,Third,man,True,,Southampton,no,True\n","\n","683,0,3,male,14.0,5,2,46.9,S,Third,child,False,,Southampton,no,False\n","\n","684,0,2,male,60.0,1,1,39.0,S,Second,man,True,,Southampton,no,False\n","\n","685,0,2,male,25.0,1,2,41.5792,C,Second,man,True,,Cherbourg,no,False\n","\n","686,0,3,male,14.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False\n","\n","687,0,3,male,19.0,0,0,10.1708,S,Third,man,True,,Southampton,no,True\n","\n","688,0,3,male,18.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True\n","\n","689,1,1,female,15.0,0,1,211.3375,S,First,child,False,B,Southampton,yes,False\n","\n","690,1,1,male,31.0,1,0,57.0,S,First,man,True,B,Southampton,yes,False\n","\n","691,1,3,female,4.0,0,1,13.4167,C,Third,child,False,,Cherbourg,yes,False\n","\n","692,1,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,yes,True\n","\n","693,0,3,male,25.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n","\n","694,0,1,male,60.0,0,0,26.55,S,First,man,True,,Southampton,no,True\n","\n","695,0,2,male,52.0,0,0,13.5,S,Second,man,True,,Southampton,no,True\n","\n","696,0,3,male,44.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","697,1,3,female,,0,0,7.7333,Q,Third,woman,False,,Queenstown,yes,True\n","\n","698,0,1,male,49.0,1,1,110.8833,C,First,man,True,C,Cherbourg,no,False\n","\n","699,0,3,male,42.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True\n","\n","700,1,1,female,18.0,1,0,227.525,C,First,woman,False,C,Cherbourg,yes,False\n","\n","701,1,1,male,35.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True\n","\n","702,0,3,female,18.0,0,1,14.4542,C,Third,woman,False,,Cherbourg,no,False\n","\n","703,0,3,male,25.0,0,0,7.7417,Q,Third,man,True,,Queenstown,no,True\n","\n","704,0,3,male,26.0,1,0,7.8542,S,Third,man,True,,Southampton,no,False\n","\n","705,0,2,male,39.0,0,0,26.0,S,Second,man,True,,Southampton,no,True\n","\n","706,1,2,female,45.0,0,0,13.5,S,Second,woman,False,,Southampton,yes,True\n","\n","707,1,1,male,42.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True\n","\n","708,1,1,female,22.0,0,0,151.55,S,First,woman,False,,Southampton,yes,True\n","\n","709,1,3,male,,1,1,15.2458,C,Third,man,True,,Cherbourg,yes,False\n","\n","710,1,1,female,24.0,0,0,49.5042,C,First,woman,False,C,Cherbourg,yes,True\n","\n","711,0,1,male,,0,0,26.55,S,First,man,True,C,Southampton,no,True\n","\n","712,1,1,male,48.0,1,0,52.0,S,First,man,True,C,Southampton,yes,False\n","\n","713,0,3,male,29.0,0,0,9.4833,S,Third,man,True,,Southampton,no,True\n","\n","714,0,2,male,52.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","715,0,3,male,19.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True\n","\n","716,1,1,female,38.0,0,0,227.525,C,First,woman,False,C,Cherbourg,yes,True\n","\n","717,1,2,female,27.0,0,0,10.5,S,Second,woman,False,E,Southampton,yes,True\n","\n","718,0,3,male,,0,0,15.5,Q,Third,man,True,,Queenstown,no,True\n","\n","719,0,3,male,33.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n","\n","720,1,2,female,6.0,0,1,33.0,S,Second,child,False,,Southampton,yes,False\n","\n","721,0,3,male,17.0,1,0,7.0542,S,Third,man,True,,Southampton,no,False\n","\n","722,0,2,male,34.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","723,0,2,male,50.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","724,1,1,male,27.0,1,0,53.1,S,First,man,True,E,Southampton,yes,False\n","\n","725,0,3,male,20.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n","\n","726,1,2,female,30.0,3,0,21.0,S,Second,woman,False,,Southampton,yes,False\n","\n","727,1,3,female,,0,0,7.7375,Q,Third,woman,False,,Queenstown,yes,True\n","\n","728,0,2,male,25.0,1,0,26.0,S,Second,man,True,,Southampton,no,False\n","\n","729,0,3,female,25.0,1,0,7.925,S,Third,woman,False,,Southampton,no,False\n","\n","730,1,1,female,29.0,0,0,211.3375,S,First,woman,False,B,Southampton,yes,True\n","\n","731,0,3,male,11.0,0,0,18.7875,C,Third,child,False,,Cherbourg,no,True\n","\n","732,0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True\n","\n","733,0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","734,0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","735,0,3,male,28.5,0,0,16.1,S,Third,man,True,,Southampton,no,True\n","\n","736,0,3,female,48.0,1,3,34.375,S,Third,woman,False,,Southampton,no,False\n","\n","737,1,1,male,35.0,0,0,512.3292,C,First,man,True,B,Cherbourg,yes,True\n","\n","738,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","739,0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","740,1,1,male,,0,0,30.0,S,First,man,True,D,Southampton,yes,True\n","\n","741,0,1,male,36.0,1,0,78.85,S,First,man,True,C,Southampton,no,False\n","\n","742,1,1,female,21.0,2,2,262.375,C,First,woman,False,B,Cherbourg,yes,False\n","\n","743,0,3,male,24.0,1,0,16.1,S,Third,man,True,,Southampton,no,False\n","\n","744,1,3,male,31.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True\n","\n","745,0,1,male,70.0,1,1,71.0,S,First,man,True,B,Southampton,no,False\n","\n","746,0,3,male,16.0,1,1,20.25,S,Third,man,True,,Southampton,no,False\n","\n","747,1,2,female,30.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True\n","\n","748,0,1,male,19.0,1,0,53.1,S,First,man,True,D,Southampton,no,False\n","\n","749,0,3,male,31.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n","\n","750,1,2,female,4.0,1,1,23.0,S,Second,child,False,,Southampton,yes,False\n","\n","751,1,3,male,6.0,0,1,12.475,S,Third,child,False,E,Southampton,yes,False\n","\n","752,0,3,male,33.0,0,0,9.5,S,Third,man,True,,Southampton,no,True\n","\n","753,0,3,male,23.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","754,1,2,female,48.0,1,2,65.0,S,Second,woman,False,,Southampton,yes,False\n","\n","755,1,2,male,0.67,1,1,14.5,S,Second,child,False,,Southampton,yes,False\n","\n","756,0,3,male,28.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True\n","\n","757,0,2,male,18.0,0,0,11.5,S,Second,man,True,,Southampton,no,True\n","\n","758,0,3,male,34.0,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","759,1,1,female,33.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True\n","\n","760,0,3,male,,0,0,14.5,S,Third,man,True,,Southampton,no,True\n","\n","761,0,3,male,41.0,0,0,7.125,S,Third,man,True,,Southampton,no,True\n","\n","762,1,3,male,20.0,0,0,7.2292,C,Third,man,True,,Cherbourg,yes,True\n","\n","763,1,1,female,36.0,1,2,120.0,S,First,woman,False,B,Southampton,yes,False\n","\n","764,0,3,male,16.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n","\n","765,1,1,female,51.0,1,0,77.9583,S,First,woman,False,D,Southampton,yes,False\n","\n","766,0,1,male,,0,0,39.6,C,First,man,True,,Cherbourg,no,True\n","\n","767,0,3,female,30.5,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True\n","\n","768,0,3,male,,1,0,24.15,Q,Third,man,True,,Queenstown,no,False\n","\n","769,0,3,male,32.0,0,0,8.3625,S,Third,man,True,,Southampton,no,True\n","\n","770,0,3,male,24.0,0,0,9.5,S,Third,man,True,,Southampton,no,True\n","\n","771,0,3,male,48.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True\n","\n","772,0,2,female,57.0,0,0,10.5,S,Second,woman,False,E,Southampton,no,True\n","\n","773,0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True\n","\n","774,1,2,female,54.0,1,3,23.0,S,Second,woman,False,,Southampton,yes,False\n","\n","775,0,3,male,18.0,0,0,7.75,S,Third,man,True,,Southampton,no,True\n","\n","776,0,3,male,,0,0,7.75,Q,Third,man,True,F,Queenstown,no,True\n","\n","777,1,3,female,5.0,0,0,12.475,S,Third,child,False,,Southampton,yes,True\n","\n","778,0,3,male,,0,0,7.7375,Q,Third,man,True,,Queenstown,no,True\n","\n","779,1,1,female,43.0,0,1,211.3375,S,First,woman,False,B,Southampton,yes,False\n","\n","780,1,3,female,13.0,0,0,7.2292,C,Third,child,False,,Cherbourg,yes,True\n","\n","781,1,1,female,17.0,1,0,57.0,S,First,woman,False,B,Southampton,yes,False\n","\n","782,0,1,male,29.0,0,0,30.0,S,First,man,True,D,Southampton,no,True\n","\n","783,0,3,male,,1,2,23.45,S,Third,man,True,,Southampton,no,False\n","\n","784,0,3,male,25.0,0,0,7.05,S,Third,man,True,,Southampton,no,True\n","\n","785,0,3,male,25.0,0,0,7.25,S,Third,man,True,,Southampton,no,True\n","\n","786,1,3,female,18.0,0,0,7.4958,S,Third,woman,False,,Southampton,yes,True\n","\n","787,0,3,male,8.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False\n","\n","788,1,3,male,1.0,1,2,20.575,S,Third,child,False,,Southampton,yes,False\n","\n","789,0,1,male,46.0,0,0,79.2,C,First,man,True,B,Cherbourg,no,True\n","\n","790,0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True\n","\n","791,0,2,male,16.0,0,0,26.0,S,Second,man,True,,Southampton,no,True\n","\n","792,0,3,female,,8,2,69.55,S,Third,woman,False,,Southampton,no,False\n","\n","793,0,1,male,,0,0,30.6958,C,First,man,True,,Cherbourg,no,True\n","\n","794,0,3,male,25.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True\n","\n","795,0,2,male,39.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","796,1,1,female,49.0,0,0,25.9292,S,First,woman,False,D,Southampton,yes,True\n","\n","797,1,3,female,31.0,0,0,8.6833,S,Third,woman,False,,Southampton,yes,True\n","\n","798,0,3,male,30.0,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True\n","\n","799,0,3,female,30.0,1,1,24.15,S,Third,woman,False,,Southampton,no,False\n","\n","800,0,2,male,34.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","801,1,2,female,31.0,1,1,26.25,S,Second,woman,False,,Southampton,yes,False\n","\n","802,1,1,male,11.0,1,2,120.0,S,First,child,False,B,Southampton,yes,False\n","\n","803,1,3,male,0.42,0,1,8.5167,C,Third,child,False,,Cherbourg,yes,False\n","\n","804,1,3,male,27.0,0,0,6.975,S,Third,man,True,,Southampton,yes,True\n","\n","805,0,3,male,31.0,0,0,7.775,S,Third,man,True,,Southampton,no,True\n","\n","806,0,1,male,39.0,0,0,0.0,S,First,man,True,A,Southampton,no,True\n","\n","807,0,3,female,18.0,0,0,7.775,S,Third,woman,False,,Southampton,no,True\n","\n","808,0,2,male,39.0,0,0,13.0,S,Second,man,True,,Southampton,no,True\n","\n","809,1,1,female,33.0,1,0,53.1,S,First,woman,False,E,Southampton,yes,False\n","\n","810,0,3,male,26.0,0,0,7.8875,S,Third,man,True,,Southampton,no,True\n","\n","811,0,3,male,39.0,0,0,24.15,S,Third,man,True,,Southampton,no,True\n","\n","812,0,2,male,35.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","813,0,3,female,6.0,4,2,31.275,S,Third,child,False,,Southampton,no,False\n","\n","814,0,3,male,30.5,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","815,0,1,male,,0,0,0.0,S,First,man,True,B,Southampton,no,True\n","\n","816,0,3,female,23.0,0,0,7.925,S,Third,woman,False,,Southampton,no,True\n","\n","817,0,2,male,31.0,1,1,37.0042,C,Second,man,True,,Cherbourg,no,False\n","\n","818,0,3,male,43.0,0,0,6.45,S,Third,man,True,,Southampton,no,True\n","\n","819,0,3,male,10.0,3,2,27.9,S,Third,child,False,,Southampton,no,False\n","\n","820,1,1,female,52.0,1,1,93.5,S,First,woman,False,B,Southampton,yes,False\n","\n","821,1,3,male,27.0,0,0,8.6625,S,Third,man,True,,Southampton,yes,True\n","\n","822,0,1,male,38.0,0,0,0.0,S,First,man,True,,Southampton,no,True\n","\n","823,1,3,female,27.0,0,1,12.475,S,Third,woman,False,E,Southampton,yes,False\n","\n","824,0,3,male,2.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False\n","\n","825,0,3,male,,0,0,6.95,Q,Third,man,True,,Queenstown,no,True\n","\n","826,0,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,no,True\n","\n","827,1,2,male,1.0,0,2,37.0042,C,Second,child,False,,Cherbourg,yes,False\n","\n","828,1,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,yes,True\n","\n","829,1,1,female,62.0,0,0,80.0,,First,woman,False,B,,yes,True\n","\n","830,1,3,female,15.0,1,0,14.4542,C,Third,child,False,,Cherbourg,yes,False\n","\n","831,1,2,male,0.83,1,1,18.75,S,Second,child,False,,Southampton,yes,False\n","\n","832,0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True\n","\n","833,0,3,male,23.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True\n","\n","834,0,3,male,18.0,0,0,8.3,S,Third,man,True,,Southampton,no,True\n","\n","835,1,1,female,39.0,1,1,83.1583,C,First,woman,False,E,Cherbourg,yes,False\n","\n","836,0,3,male,21.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n","\n","837,0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True\n","\n","838,1,3,male,32.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True\n","\n","839,1,1,male,,0,0,29.7,C,First,man,True,C,Cherbourg,yes,True\n","\n","840,0,3,male,20.0,0,0,7.925,S,Third,man,True,,Southampton,no,True\n","\n","841,0,2,male,16.0,0,0,10.5,S,Second,man,True,,Southampton,no,True\n","\n","842,1,1,female,30.0,0,0,31.0,C,First,woman,False,,Cherbourg,yes,True\n","\n","843,0,3,male,34.5,0,0,6.4375,C,Third,man,True,,Cherbourg,no,True\n","\n","844,0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True\n","\n","845,0,3,male,42.0,0,0,7.55,S,Third,man,True,,Southampton,no,True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   </body>\n","</html>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["pivot_ui(titanic)\n","HTML('pivottablejs.html')"]},{"cell_type":"markdown","metadata":{"id":"fH1zusN7GKCx"},"source":["**Watermark**"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1692083924725,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"iH1jL0baGMJW","outputId":"6cf38826-d24c-4eea-f8aa-5a31d7b31125"},"outputs":[{"name":"stdout","output_type":"stream","text":["Last updated: 2023-08-25T10:51:16.460182+02:00\n","\n","Python implementation: CPython\n","Python version       : 3.9.17\n","IPython version      : 8.14.0\n","\n","Compiler    : MSC v.1929 64 bit (AMD64)\n","OS          : Windows\n","Release     : 10\n","Machine     : AMD64\n","Processor   : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel\n","CPU cores   : 16\n","Architecture: 64bit\n","\n","watermark   : 2.4.3\n","numpy       : 1.23.5\n","pandas      : 2.0.3\n","seaborn     : 0.12.2\n","pivottablejs: 0.9.0\n","\n"]}],"source":["from watermark import watermark\n","watermark(iversions=True, globals_=globals())\n","print(watermark())\n","print(watermark(packages=\"watermark,numpy,pandas,seaborn,pivottablejs\"))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jVM6jiTaD6t4"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.17"}},"nbformat":4,"nbformat_minor":0}