{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In most data science tutorials I have seen, a lot of the data clean-up is done in what seems to me casually, an annoying obstacle to get to the sexy Machine Learning bit. I was curious to see what if any difference it made in my Kaggle ranking if I used a somewhat more cautious approach in my data cleanup. My approach was to dumbly follow [Datacamp's tutorial](https://www.datacamp.com/community/open-courses/kaggle-python-tutorial-on-machine-learning#gs.EkI78Vw) and submit my test set labels as a benchmark. The second step is then to use a more elaborate data cleanup process and see whether taking the extra time actually moves my ranking up, or maybe down.\n",
    "<!-- TEASER_END -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Titanic data set\n",
    "\n",
    "This data set can be used to train a machine learning algorithm to correctly classify passengers of the Titanic's first and last voyage as having survived the disaster or not. To that end, the data set available at [kaggle](https://www.kaggle.com/c/titanic) contains various data type pertaining to each passenger. The label to be predicted is the Survived feature (0 for died, 1 for survived). The data comes pre-divided; a training set that includes survival data and a testing set that does not. The goal is to produce survival data for the test set, and upload the result to kaggle for scoring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as pl\n",
    "from sklearn.preprocessing import Imputer\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sb.set_context(\"notebook\", font_scale=1.2, rc={\"lines.linewidth\": 1.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "\n",
       "   Parch     Ticket     Fare Cabin Embarked  \n",
       "0      0  A/5 21171   7.2500   NaN        S  \n",
       "1      0   PC 17599  71.2833   C85        C  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset = '/home/madhatter106/DATA/Titanic/train.csv'\n",
    "testset = '/home/madhatter106/DATA/Titanic/test.csv'\n",
    "dfTrain = pd.read_csv(trainset)\n",
    "dfTest = pd.read_csv(testset)\n",
    "\n",
    "dfTrain.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                              Name     Sex   Age  SibSp  \\\n",
       "0          892       3                  Kelly, Mr. James    male  34.5      0   \n",
       "1          893       3  Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
       "\n",
       "   Parch  Ticket    Fare Cabin Embarked  \n",
       "0      0  330911  7.8292   NaN        Q  \n",
       "1      0  363272  7.0000   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTest.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
      "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
      "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
      "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
      "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
      "25%     223.500000    0.000000    2.000000         NaN    0.000000   \n",
      "50%     446.000000    0.000000    3.000000         NaN    0.000000   \n",
      "75%     668.500000    1.000000    3.000000         NaN    1.000000   \n",
      "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
      "\n",
      "            Parch        Fare  \n",
      "count  891.000000  891.000000  \n",
      "mean     0.381594   32.204208  \n",
      "std      0.806057   49.693429  \n",
      "min      0.000000    0.000000  \n",
      "25%      0.000000    7.910400  \n",
      "50%      0.000000   14.454200  \n",
      "75%      0.000000   31.000000  \n",
      "max      6.000000  512.329200  \n",
      "----------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n",
      "None\n",
      "----------------------------------------\n",
      "PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            177\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             0\n",
      "Cabin          687\n",
      "Embarked         2\n",
      "dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/madhatter106/anaconda3/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "print(dfTrain.describe())\n",
    "print('-' * 40)\n",
    "print(dfTrain.info())\n",
    "print('-' * 40)\n",
    "print(dfTrain.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problems in the training dataset:\n",
    "* Missing data in **Age**; almost 20% in fact, so we're definitely imputing that one.\n",
    "* Missing data in **Cabin**; a whoping 687/891 but this feature doesn't seem useable to me without some information on the cabin layout, so I will drop this\n",
    "* Missing data in **Embarked**; just 2 entries missing, maybe assign whatever's more popular?\n",
    "* Some of the data appears spurious, e.g. *min(**Fare**)* is 0. I'll assume that to be an omission, these are passengers after all and none seem related to the crew. The goal then is to figure out a scheme to replace the free tickets with more meaningful data.  \n",
    "* Some of the data seem useless; as a first cut: **PassengerId**, **Ticket**, and as mentioned before, **Cabin**.\n",
    "* There are some categorical data that need to be transformed; **Sex**, **Embarked**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
      "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
      "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
      "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
      "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
      "25%     996.250000    1.000000         NaN    0.000000    0.000000         NaN\n",
      "50%    1100.500000    3.000000         NaN    0.000000    0.000000         NaN\n",
      "75%    1204.750000    3.000000         NaN    1.000000    0.000000         NaN\n",
      "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200\n",
      "----------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n",
      "None\n",
      "----------------------------------------\n",
      "PassengerId      0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age             86\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             1\n",
      "Cabin          327\n",
      "Embarked         0\n",
      "dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/madhatter106/anaconda3/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "print(dfTest.describe())\n",
    "print('-' * 40)\n",
    "print(dfTest.info())\n",
    "print('-' * 40)\n",
    "print(dfTest.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problems in the test set:\n",
    "* **Age** is missing 86 entries\n",
    "* **Fare** is missing 1 entry and there are some free tickets in this set too.\n",
    "* **Cabin** is missing 327 entries (but we don't care)\n",
    "\n",
    "\n",
    "### Getting rid of features, first cut:\n",
    "First I'm going to get rid of PassengerId, Ticket and Cabin. While dropping the Name data is tempting, I'm going to hold on to it for now, in case I can use titles to help infer missing Age data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfTrain.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1, inplace=True)\n",
    "dfTest.drop(['PassengerId', 'Ticket', 'Cabin'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we impute/correct any of the Fare, Age and Embarked data, let's see if they appear to be a factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0e926587f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0cpfLV+6GxjIMFzYbH0pnOD01R66Czl0OGJuaZ2xqnm8/WF65O1h2yl0+hOpI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\nkiRJkqTa/gOwpA1nPw3GBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e92658828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.factorplot(y='Age', x='Survived' ,data=dfTrain, aspect=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age is clearly a factor, what about Fare?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qbrbvFnYAn7npWBNAjPFrIYRPUN6T6U2zn5okSZJmq1RK6Ks0wnXnCxQvXq06\nZl3L8uuNcPu2t9oIJ0mSbstsQ6YxYPW0v18ENk37+zeB357tpCRJknT7yo1wA3TnCxzqLTJ8eaLq\nmI0bVl1vhNt97zob4SRJ0qzNNmR6Anh3COF0jDECvcC/Br5QOX8Q8FdfkiRJd9mVsUkOHy83wh0+\n0c/YePVGuO2b1tDVUQ6WtmRbbISTJEl3xGxDpseAxylv7v164JPAH4UQOoHzwMuBv7kjM5QkSdIN\nLo6O09NboCdf5JnTNTTCpSC3dQOduSxdHRkyNsJJkqS7YFYhU4zxiRBCF9BROfQRyvs0vYXyY3Of\nA35pNq8dQkgBv0Z5ZdQeYBT4e+DfxRjPVPZ7+jkgodyie00CZGOMA7O5ryRJUiO7MHiZ7nyR7t4C\nx5+9SJVCOJqb0jyws5WuXJYDHRnWrbYRTpIk3V2ppFpnbUUI4TeBT8QYL8xw7hXAd2KMdTfKhRA+\nCPxb4BcpP5a3F/ho5fQ+4GOUw603cmPIxExzezGFwkhtX7gkSdI8SJKEMy9coqe3vHH3s4XRqmNW\nrWjiwJ7yxt0P7m5j5XIb4SRJUv2y2bU1PVt/O+88/i/gS8BMQc4/AJ3A4dt4vX8mhNBEOTx6X4zx\nLyqHT4cQ3gP8GbC/cmw8xlio516SJEmNplRK6H12iO58kZ7e2hrh1rcsLz8Gl8uwb3srzU1uiylJ\nkubH7YRMt0qt7shukTHGKWDXDKeurTqqXpEiSZK0gExMTnH01OD1RrhLV2pohGu9qRHOjbslSVID\naPg11JXNxN8FfDbG+HQIYb6nJEmSVJfLVyc5fLxId2+Rp2tshNtxz1q6OsqPwt2bsRFOkiQ1noYN\nmUIIvwe8HUhT3lj816ed3ljZAPxRYB3wLeBdMcan5nqekiRJtRi6NMah3iLd+QLPnB5kqlS9ES5s\nu9YIl6V9/co5mqkkSdLsNGzIBPw+8AnKez39HhBCCP8SuEg5ePoq8D5gK/A7wBMhhIMxxt5aXjyd\nTpFO+xtASZJ097wwcJnvxAs8GWtrhFvWnObB3W08EjZysCPDWhvhJEnSAtKwIVOMcQAYAI6FEPLA\nt4GfiDG+7aZLnwkh/BNwFngH8Au1vH5bm8vMJUnSnZUkCcefu8g/Pn2ebx45z5nnR6qOaVm1jO+7\nfxMve3AzXWEjK1c07NszSZKkW7rddzH7brEn0n0hhBteL8bYfTsvHkJoB14NfCXGOL3F7kjl4/0z\njYsxXgzO/va/AAAftklEQVQhnAa21HqvgYFRVzJJkqS6TZVK5M8M8WQs8GQs0D9cvRFuw5rldIWN\nPBKy7NvxvUa4K5fHuHJ57G5PWZIk6ba0trbUdN3thkyfvMW5P5/hWNNtvv4q4FPAbwLvn3b8YOXj\n+RDCnwCPxxg/c+1kCKEN2AN8udYblUoJpSp7IUiSJM1kfGKKo6cG6MkXOdRXWyPcprbVdOXKG3fv\n2jytES6BycnSXZ6xJEnS3Xc7IdO/v2uzqIgxPhtC+M/Au0IIReBrwE7gQ8A54NPAjwIfCyGsBr4B\nbAbeC0wCH77bc5QkSUvT5asTPHW8n+58gSMnBhibqN4It/OetXTlstcb4SRJkhazVJI01mqeEMIy\n4N3Az1De1Pt5ymHTu2KMZ0IIK4F3Aj8JbAOuAF8H3h1jPFzrfQqFkcb6wiVJUsMZHBnjUG+B7nyB\nY2eGqjbCpVMpwvYNdOWydHZkaFtnI5wkSVr4stm1Ne031HAh01wxZJIkSTN5fuAy3flysHTi3HDV\n65c3p3lgVxtduSwH9mZYs2rZHMxSkiRp7tQaMllfIkmSlrQkSTj1/Ajd+QI9vUXOFUerjmlZ2cz+\nPeX9lR7c3caKZbe7DaUkSdLiY8gkSZKWnGuNcN35It29BQZHqje6ta5dQWdHOVjKbdtwvRFOkiRJ\nZYZMkiRpSRibmOLoyQF68gUO9RUZvTpZdczm9tXXN+7eec9aUqmaVopLkiQtSYZMkiRp0bp0ZYLD\nx4t054scOdnP+ESp6phdm9fRlSuvWNrcbiOcJElSrQyZJEnSojI4MnZ94+54ZohSlZKTpvT3GuEO\n7rURTpIkabYMmSRJ0oJ3vn+0EiwVOXm+tka4B3e305XLcGBvhpaVNsJJkiTVy5BJkiQtOEmScPL8\nCD295RVL5/svVx3TsrKZg3vLj8Hdv8tGOEmSpDvNkEmSJC0Ik1Ml8meH6M4X6Okt1tQI17ZuBZ0d\n2Uoj3Hqa0jbCSZIk3S2GTJIkqWGNjU9x5OQA3fkCh4/X1gh3b6aFrlyGzg4b4SRJkuaSIZMkSWoo\nl65M8FRfke58gaMnBxifrN4It+fedXTlsnTmstzTtnoOZilJkqSbGTJJkqR5NzB89fpjcLU2wu27\n1gjXkaV17Yo5mqkkSZJejCGTJEmac0mScK7/Mj358sbdp54fqTpm+bI0D+1upyuX5cCedlbbCCdJ\nktRQDJkkSdKcKCUJJ88P050v0J0v8sJA9Ua4NauWfa8Rbmcry22EkyRJaliGTJIk6a6ZnCoRz1xr\nhCswdGm86pj2dSvozGXp6sjSYSOcJEnSgmHIJEmS7qix8SmePtFPd2+Bp/r6uTJWvRFuS7aFro4s\nXbks2zetsRFOkiRpATJkkiRJdRu5PM6hviI9+SJHTw0wUaURLgXs3lJuhOvKZdnUaiOcJEnSQmfI\nJEmSZqX/4lW6ewv05AvEs0NUKYSjKZ3ivh2tlUa4DBvW2AgnSZK0mBgySZKkmiRJwrni6PWNu0+/\nUL0RbsWyJh7a005XLsP+3RlWr/SthyRJ0mLlOz1JkvSiSknCiXPXGuEKXBi8UnXM2tXlRrjOXJYH\ndrayrNlGOEmSpKXAkEmSJN1gcqrEsdODlUa4IhdHa2mEW1nZXylDx9YNpNNu3C1JkrTUNFzIFEJI\nAb8G/GtgDzAK/D3w72KMZyrXPAo8BjwCjANfBN4eYzw/H3OWJGmhuzo+ydMnBujJF3jqeG2NcFuz\nLdc37t620UY4SZKkpS6VVNulc46FED4I/FvgF4EngL3ARyun91EOnrqBTwG/B2SADwKrga4Y41Qt\n9ykURhrrC5ckaY4NXx7nqd4i3fkCR08NMjlVvRFuz9b1dHWUVyxttBFOkiRpSchm19b028SGWskU\nQmgC3gi8L8b4F5XDp0MI7wH+DNgP/DJQAN4aYywBvSGEtwDHgDcBn577mUuStDAUh67QXQmWep+t\n3gjX3JTivh1tdOUyHOzIsr5l+dxMVJIkSQtOQ4VMlVVIu2Y4de0t8ATwI8DnKgHTtXH5EMJJ4HUY\nMkmSdF2SJDxXGL2+cfeZC5eqjlm5vIn9e9rp7Miyf087q1Y01NsFSZIkNaiGf9cYQugE3gV8FjgB\n3Ascn+HSPsqP00mStKSVkoTjz10sb9ydL3JhqLZGuM6ODF25LPftaGNZc3oOZipJkqTFpGFDphDC\n7wFvB9LAR4BfBzZWTo/MMGQY2D43s5MkqbFMTJZ45vQgPb3lRrjhGhrhMutXXt+4e++W9TbCSZIk\nqS4NGzIBvw98AuikvMF3oLwh+B2RTqd8My1JWtCujE1y+Hg/Tx67wFPHi1wZq959sX3TGh4OG3k4\n2AgnSZKkO6thQ6YY4wAwABwLIeSBbwOvqZxeN8OQ9cBgra/f1tbiG2tJ0oIzNDLGt44+zz8eOc+h\nfKF6I1wK7t/Vzksf3MxLH7yHe9pb5mimkiRJWmoaKmQKIbQDrwa+EmO8MO3UkcrHncBZYO8Mw3PA\nl2q918DAqCuZJEkLQmHwCt+JF3gyFug9O0SVQjiam1I8sKudR0KWzlyWddMa4QYHR+/uZCVJkrTo\ntLbW9ovKhgqZgFXAp4DfBN4/7fjBysfngM8BbwghNFXa6K5tDr6d8ubgNSmVEkqlam/TJUmae0mS\ncPbCpfLG3b1Fzt5GI1xXLstDu29shJucvPVqJ0mSJOlOSCVJYwUtIYSPA/8KeBvwNcqrlz4EtAH3\nVz4eAv4aeAxoBT4KXI4xvrzW+xQKI431hUuSlrRSKaGv0gjXnS9QvHi16ph1LcuvN8Lt295qI5wk\nSZLuimx2bU2PgjXaSiaAX6S8YuldwFbgecph07tijMPAcAjhVcAHKIdNVyivYPr1+ZmuJEmzU26E\nG6A7X+BQb5HhyxNVx2zcsOp6I9zuLetIu7+gJEmSGkTDrWSaK65kkiTNh2uNcN35AodP9DM2Xlsj\n3LVgaUvG4gpJkiTNrYW8kkmSpEXl4ug4Pb0FevJFnjk9wOTUrX/PkUpBbusGunJZOjsyZDasmqOZ\nSpIkSbNnyCRJ0l1wYfAy3fki3b0Fjj97sYZGuDQP7GylK5flQEeGdauXVxkhSZIkNRZDJkmS7oAk\nSTjzwiV6essbdz9bGK06ZtWKZg5UGuEe3N3GyuX+WJYkSdLC5btZSZJmqVRK6H12iO58kZ7e2hrh\n1rcspzOXpSuXYd/2VpqbbISTJEnS4mDIJEnSbZiYnOLoqcHrjXCXrlRvhNvUWm6E68xl2X2vjXCS\nJElanAyZJEmq4vLVSQ4fL9LdW+TpGhvhdtyzttwI15HhXhvhJEmStAQYMkmSNIOhS2P09BbpyRd4\n5vQgU6XqjXBh24byo3AdWdrXr5yjmUqSJEmNwZBJkqSKFwYu013ZuPvEc8NVG+GWNad5YGdbuRFu\nbztrbYSTJEnSEmbIJElasq41wj2ZL9CTL/BcsXoj3OoVzRzYW2mE29XOiuVNczBTSZIkqfEZMkmS\nlpSpUonesxfpzhfo6S3QPzxWdcyGNcuvPwYXtm+wEU6SJEmagSGTJGnRG5+Y4uipAXryRQ711dYI\nd0/b6kojXIZdm22EkyRJkqoxZJIkLUqXr07w1PF+uvMFjpwYYGyieiPczmuNcLks92Za5mCWkiRJ\n0uJhyCRJWjQGR8Y4VNm4+9iZoaqNcOlUirB9Q3nFUkeGtnU2wkmSJEmzZcgkSVrQnh+4THe+0gh3\nbrjq9cub0zyw61ojXIY1q5bNwSwlSZKkxc+QSZK0oCRJwqnnR64HS+f7L1cd07KymQN7M3R2ZHlw\ndxsrltkIJ0mSJN1phkySpIY3VSqRPzNEd75Id2+BwZHqjXCta1fQ1VHeuDu3zUY4SZIk6W4zZJIk\nNaSxiSmOnhygJ1/gUF+R0auTVcdsbl99fePunfesJWUjnCRJkjRnDJkkSQ3j0pUJDh8v0p0vcuRk\nP+MTpapjdm1eR1cuQ1cuy+Z2G+EkSZKk+WLIJEmaV4MjY9f3V4pnhiglt26Ea0pPb4TL0rp2xRzN\nVJIkSdKtGDJJkubc+f7RSrBU5OT5GhrhlqV5aFc7nbkMB/ZmaFlpI5wkSZLUaBoyZAoh/DzwK8Be\noB94HHhnjLEQQvgE8HNAAkzfbCMBsjHGgTmeriSpilKScOp8uRGup7f2RriDHRm6OrLcv8tGOEmS\nJKnRNVzIFEL4NeD3gd8APks5aPqPQABeSTlMegJ4IzeGTBgwSVLjmJwqEc8O0ZMv0NNbrKkRrm3d\nCjo7yht357atpyltI5wkSZK0UDRcyEQ5XPpEjPFDlb+fCCH8LvDREMJDlWPjMcbC/ExPkvRixsan\nOHJygO58gcPHa2uEuzfTcn3j7h2bbISTJEmSFqpGDJnuB6ZuOvZc5eOaOZ6LJKmKS1cmeKqvSHe+\nwNGTA4xPVm+E23PvuvLG3bks97StnoNZSpIkSbrbGi5kijEOzXD4x4FR4MgcT0eSNIOB4auV/ZWK\nNTfC7dvRSldHhoM2wkmSJEmLUsOFTDcLIbwBeCvwf8QYR0IIABsrG4A/CqwDvgW8K8b41LxNVJIW\nsSRJONd/mZ58ge58gVPPj1Qds3xZmod2t9OVy3JgTzurbYSTJEmSFrWGDplCCG8GPgl8Msb4vsrh\ni0Aa+CrwPmAr8DvAEyGEgzHG3lpeO51OkU6774ek2TvfP8onvxgB+J9/NLC5vWWeZ3RnlZKEk+eG\n+U68wJPHCjw/UL0Rbs2qZXTmMjwSNvLArjaW2wgnSZIkLRmppMojDvMlhPArwB8CH44xvq3KteuB\ns8D/E2P8hVpeP0mSxM1lJdXjPR/7Jt3HLgDw8L6N/M5bXzbPM6rfxGSJI8eLfPPIeb515HkGhq9W\nHZNtXcXLHtzMSx/azP0722hqshFOkiRJWmRqClAaciVTCOEXgQ8Bvxlj/EC162OMF0MIp4Ettd5j\nYGDUlUyS6nLm/PD1z0+fH2ZwcHQeZzN7Y+NTHD5e5MlY4FBvkctj1RvhtmZbeDhs5OF9NzbCDQ9f\nudvTlSRJkjTHWltre2qj4UKmEMKrgA8Db48x/vFN55YDfww8HmP8zLTjbcAe4Mu13qdUSiiVGnMV\nl6SFYfpC0CSByRpa1RrFyOVxDvUV6ckXOXpqgIkqc08Be7aspzOXoSuXZVPr9xrhpqYSwO+nkiRJ\n0lLXcCET5YDpG8CnQwibbjp3CcgAHwshrK5ctxl4LzBZGStJmkH/xat09xboyReIZ4eo9rR0UzrF\nfTta6cpl6ezIsH6NjXCSJEmSXlxDhUwhhO1AqPw5N8Ml/x74WeCdwLuBbcAV4OvAozHGvjmaqiQ1\nvCRJOFccpTtfoDtf5PQL1RvhVixvqjTCZdi/O8PqlQ31Y0KSJElSA2uo/z3EGM8AtVQRvbvyR5I0\nTSlJOHFuuBIsFbgwWH2PpLWrl3Fwb/kxuPt3trKs2UY4SZIkSbevoUImSdLtm5wqcez0IN35Aj29\nRS6Ojlcdk1m/kq5clq5clr1b1luEIEmSJKluhkyStABdHZ/k6RMD9OQLPHW8nys1NcKtoauycfe2\njWuuN8JJkiRJ0p1gyCRJC8Tw5XGe6i3SnS9w9NQgk1M1NMJtXU9XR5auXIaN0xrhJEmSJOlOM2SS\npAZWHLpCdyVY6n22eiNcc1OK+3a00ZXLcLAjy/qW5XMzUUmSJElLniGTJDWQJEl4rjB6fePuMxcu\nVR2zcnkT+/e005XL8tDudlat8Fu7JEmSpLnn/0QkaZ6VkoTjz10sb9ydL3JhqHoj3LrVyzjYUd64\n+74drSxrTs/BTCVJkiTpxRkySdI8mJgs8czpQXp6y41wwzU0wmU3lBvhOjtshJMkSZLUeAyZJGkW\nTp4f5tKV7wVDgyNX+fO/y/NDXVvY3N4y45grY5M8faKf7nyBw8f7uTo+VfU+2zauoStXXrG0Ndti\nI5wkSZKkhpVKqu0iu0gVCiNL8wuXVJckSfjLrxznb791Zsbz6VSKn/nhDl7VtRWA4dFxDvWVN+7+\n7qkBJqdu/a0nBXRsXV9esZTLkt2w6k5/CZIkSZJ0W7LZtTX9ttuQSZJuw988cYrPfO1E1ete9sAm\nihev0vfsRap9s2luSnH/zja6clkO7s2wzkY4SZIkSQ2k1pDJx+UkqUajVyf470+cqunabx594Zbn\nV61oYv+eDJ0dGRvhJEmSJC0K/q9Gkmr0xJHnmZgszXr8+pbldHZk6Mxl2bfdRjhJkiRJi4shkyTV\n6NT54dses3HDqusbd+/eso60G3dLkiRJWqQMmSSpRlOl29vK7RUHNvOW1+6zEU6SJEnSkuCzGpJU\no42tq2/r+r1bNhgwSZIkSVoyDJkkqUaPPnRPzdeuXN7EwyF7F2cjSZIkSY3FkEmSarSxdTUvuX9T\nTde+5pFtNsZJkiRJWlIMmSTpNrzltYHctg23vOYl92/if3h01xzNSJIkSZIaQypJbm8j28WiUBhZ\nml+4pLpNTJb48pPP8pdf6WP6XuDbN67h1Q9v5eX7N9siJ0mSJGnRyGbX1vQfHFcySdJtWtac5rUv\n2U7r2pXXj21Ys5z3/Jvv418cuNeASZIkSdKS1JAbhoQQfh74FWAv0A88DrwzxlionH8UeAx4BBgH\nvgi8PcZ4fn5mLGmpa0qnbZKTJEmStKQ13EqmEMKvAX8K/BfgAPC/AK8F/rJyPlAOlfqAg8CPATuA\nL4QQmuZjzpIkSZIkSUtdI65k+g3gEzHGD1X+fiKE8LvAR0MIDwFvBwrAW2OMJaA3hPAW4BjwJuDT\n8zFpSZIkSZKkpazhVjIB91MOkqZ7rvJxDfAjwBcrARMAMcY8cBJ43ZzMUJIkSZIkSTdouJVMMcah\nGQ7/ODAKHAXuBY7PcE0fsO8uTk2SJEmSJEkvohFXMt0ghPAG4K2UN/puqRwemeHSYWD9XM1LkiRJ\nkiRJ39NwK5mmCyG8Gfgk8MkY4/tCCJvv1Gun0ynSaZugJM3e9DK5VAqamxs+t5ckSZKku6ZhQ6YQ\nwq8Afwh8OMb4tsrhi5WP62YYsh4YrPX129parBuXVJfpQXU6naK1teUWV0uSJEnS4taQIVMI4ReB\nDwG/GWP8wLXjMcbLIYSzwN4ZhuWAL9V6j4GBUVcySapLqZTc8Png4Og8zkaSJEmS7o5af6HecCFT\nCOFVwIeBt8cY/3iGSz4HvCGE0BRjnKqM6QS2A5+t9T6lUnLDfxAl6XYlyY2fT06WXvxiSZIkSVrk\nGi5kohwwfQP4dAhh003nLgG/D/wM8PEQwmNAK/BR4JsxxppDJkmq1+bMavqHrwJwb8ZH5SRJkiQt\nbQ21S20IYTsQgEeBczP8+fUY4yngVcAO4BDweaAHeP08TFnSEvbTr+7ggV1tPLCrjZ969UxP8UqS\nJEnS0pFKkqX5yFihMLI0v3BJkiRJkqTbkM2urWlT64ZaySRJkiRJkqSFyZBJkiRJkiRJdTNkkiRJ\nkiRJUt0MmSRJkiRJklQ3QyZJkiRJkiTVzZBJkiRJkiRJdTNkkiRJkiRJUt0MmSRJkiRJklQ3QyZJ\nkiRJkiTVzZBJkiRJkiRJdTNkkiRJkiRJUt0MmSRJkiRJklQ3QyZJkiRJkiTVzZBJkiRJkiRJdTNk\nkiRJkiRJUt0MmSRJkiRJklQ3QyZJkiRJkiTVzZBJkiRJkiRJdTNkkiRJkiRJUt0MmSRJkiRJklS3\n5vmewExCCG8Hfg/4qxj///buPUausozj+HdbqLW1LQW5LlAE4oP/EJtoTCpCvBRQUsoqFq0IkRC8\nYCIkGBC5GaNCQkAJGsFGrtsCgSpylUu42DZoTRUSgScVmw3YKK1Aw6VQWsY/zlkcp7N0Z4+Zs+t8\nP8lkOu/lnGf61+S37/ueXNzUfg1wMtAA+pqmNIDdM/OFrhYqSZIkSZIkYJyFTBExG7gOmAu8NsKw\nVcAA/x0yYcAkSZIkSZJUn3EVMgGLgWkUIdPqEcZsycwN3StJkiRJkiRJOzLezmS6E5ifmRvrLkSS\nJEmSJEmjN65WMmXmUN01SJIkSZIkqXPjKmQapT0i4lrgMGAm8HvgvMx8vNaqJEmSJEmSethEC5k2\nUWzxewS4BNgXuAhYFREfzMy1o73QpEl9TJrUt+OBkiRJkiRJ2qEJFTJl5hktTU9FxB+AZ4GzgVNH\ne63ddnuPCZMkSZIkSdL/yHg7+LtjmbkJGAL6665FkiRJkiSpV02YlUwRMQW4ArgvM5c3te8KHAQ8\nWFdtkiRJkiRJvW5chUwRMRuYAvQBk4GpEbFn2b0JeC/wi4iYBqwE9gZ+CGwFrux+xZIkSZIkSYJx\nFjIBy4HDmz73AwvLf38FOBH4LnA+sB+wGVgBHJaZf+1inZIkSZIkSWrS12g06q5BkiRJkiRJE9yE\nP/hbkiRJkiRJ9TNkkiRJkiRJUmWGTJIkSZIkSarMkEmSJEmSJEmVGTJJkiRJkiSpsp3qLkCSJqqI\nOBU4EzgI2AgsBc7NzK21FiZJknpeRJwJXAzclpmL665HUm8wZJKkMYiIk4CrKEKm24FDgSXAdOD0\nGkuTJEk9LCJmA9cBc4HXai5HUo9xu5wkjc0FwNLMvCIzhzLzDuB84LSI2Lvm2iRJUu9aDEyjCJle\nqrkWST3GkEmSOhQRBwMHAne3dN0DTAaO6npRkiRJhTuB+Zm5se5CJPUet8tJUucCaADPNDdm5rMR\nsQU4pJaqJElSz8vMobprkNS7XMkkSZ2bWb6/3KbvZWBWF2uRJEmSpHHBkEmSJEmSJEmVGTJJUueG\nD9Gc2aZvBvBiF2uRJEmSpHHBkEmSOvc00Acc3NwYEQcAU4Ana6hJkiRJkmplyCRJHcrMdRRB04KW\nrgHgTeDerhclSZIkSTXz6XKSNDbnAzdHxJnAbcDcsu1yHxksSZLqEhGzKVZW9wGTgakRsWfZvSkz\nX6+tOEn/91zJJEljkJm3AV8GTgES+AlwWWaeU2thkiSp1y0H1gN/B/qBheXn9cCiGuuS1AP6Go1G\n3TVIkiRJkiRpgnMlkyRJkiRJkiozZJIkSZIkSVJlhkySJEmSJEmqzJBJkiRJkiRJlRkySZIkSZIk\nqTJDJkmSJEmSJFVmyCRJkiRJkqTKDJkkSZIkSZJUmSGTJEmSJEmSKtup7gIkSZLGi4jYHTgdGAD2\nBWYALwGPAz/OzLu6XM9DwJzMPLCL95wFvAhcm5mndOu+kiRp4jNkkiRJ4u2AaQ0wBbgMWAW8Bbwf\n+Cbwm4g4OTNv7GJZp5X1SJIkjXuGTJIkSYXTgH2AIzPzwab2lRFxE/AX4BygayFTZq7t1r0kSZKq\nMmSSJEkq9Jfvf2vtyMzNEXFoZr4CEBEXARcAH8rMNcPjIuJk4Brg+MxcHhFzgHXAucAcYDHwDeBq\n4P7MPK75PhExHdgA/DYzByLiYWD/zDwwIm4AvgDslZn/apl3A3ACsE9mbixXZf0A+DSwB/BP4FfA\nhZn5UtO8OcBPgSOArcB9wIWd/bdJkiQVPPhbkiSp8ATQB/w8IvZp7RwOmEqN8tVOu/YBYBZwFPAA\ncAdwZBkqNVsIvAu4vs21Bil+uw00T4iIKcCxwD1lwDQdWAEsAL4PfBK4BDgReCAiJpfzJgP3AB8F\nzgKOo9guOPgO302SJGlErmSSJEkq/BL4PPApYCgiVgAry9fDmbm5wrUPAOZl5jaAiBgEFlEEQTc1\njVtEcdD4nW2ucT+wETgeWNLU/hmKA8pvKD9/FTgYOCIzV5RtKyLieeDm8h7LKFY5HQKckZlXleMe\nKUOruWP+ppIkqWe5kkmSJAnIzC0UAdNJwEPAR4DvAHcBGyLiZxExc4yX/91wwFS6F3iBIjACICJm\nUKx0uiUz32xT3zbgFuDjEbFLU9cJwCaK1VEARwP/aAqYht0FbAPmlZ8/TLFi6cGWcbd38L0kSZLe\nZsgkSZJUysxGZg5m5pHALsDhFGcvPQd8jeLMorHY0HKfN4FbgaMj4t1l83EUT5IbfIfrDAI7l2OJ\niKnAMcCtmflGOWY/YK+IeKv5BbxC8dtv33LcXu1qA9Z3+uUkSZLA7XKSJEltlSubVlI8Xe5iilBo\nQUTMe4dpfSO0b7cyCVhK8US7Y8prLwKG2qxAaq7psYhYR7EC6tpy7nT+s1Vu2HqKbXTt6hk+W2qk\nWv0jpCRJGhNDJkmS1PMiYmeKA7Bfz8zHWvszc2tELKM4Q6kfeKvs2rllaD+jlJmPRsRzwOci4n5g\nPsUB3TuyDDir3F63CHg2Mx9t6h8CPgY82bJFr9XwCqY9gOeb2vcf7XeQJElq5l+qJEmSit9ENwPX\nR8SsEcbML9//DLxIsRJoTsuYATqzjOIcpgGKwOrGUcwZ3jJ3LMVqpdY59wFTKQKot0VEf0QsiYhD\nyqbVFN/hqJb5n+3kC0iSJA1zJZMkSep5mflGRHydYgvb6oi4EvgTsJXiDKMvAguBSzNzbXnG0Vbg\noojYDLwOnEpxAHcnlgLfBs4DVmfm2lHU+lREPAF8D5jG9iHTVRTb8K6OiF2BNcBB5T1mAOeW4+4G\n1gEXRsSrwFPAJ9g+dJIkSRoVVzJJkiQBmbmc4slrjwHfolgR9BBwOcWKnwWZeXY59hngSxRPa7sJ\nuBr4I/CjNpdulK9293wceBJ4H9ufq9Q8v9VgOWdNZj7dcs1XgcMowqdzgIeBSynOl5qXmc+X47ZQ\nPIluVdn/a+ADFFsCt41UsyRJ0kj6Gg1/P0iSJEmSJKkaVzJJkiRJkiSpMkMmSZIkSZIkVWbIJEmS\nJEmSpMoMmSRJkiRJklSZIZMkSZIkSZIqM2SSJEmSJElSZYZMkiRJkiRJqsyQSZIkSZIkSZUZMkmS\nJEmSJKkyQyZJkiRJkiRVZsgkSZIkSZKkygyZJEmSJEmSVNm/AabG6iFe7CuDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e90090c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.factorplot(y='Fare', x='Survived', data=dfTrain, aspect=3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare is clearly also a factor, but is it because it's a proxy for class?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PwNK6mP7swQHe99kfV2XeukJuTpfT9OtSp9O2JuoK+arMI0mSJElr5OvAa0g3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\nAMEcAAAAACxAMAcAAAAAC/gfjfLyCjQCy6wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e8fb95fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.factorplot(x='Survived', y='Fare', hue='Pclass', data=dfTrain, aspect=3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, Fare appears to have an effect in $1^{st}$ class only. Let's look at 'Embarked'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G3uO7/XmvtfTpGYIlSZKkmujvzSn6uyxCkiRJGnSGV0mSJJVef+/I9sW+9MvM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\nPEOvJEmSmmfolSRJUvO+A+R6MJ9gksZ9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e8fadb1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(x='Embarked', hue='Survived', data=dfTrain);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Embarked', 'Age', and 'Fare' all seem to have an effect on survival so I'll clean up all three features. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correcting spurrious entries and imputing missing data\n",
    "\n",
    "First, since a lot of the transformation are common to both the training and the test set I'm going to combine both sets to ensure imputation does not drive a statistical wedge between the two sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfTemp = pd.concat((dfTrain,dfTest),join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Pclass          Age        SibSp        Parch         Fare\n",
      "count  1309.000000  1046.000000  1309.000000  1309.000000  1308.000000\n",
      "mean      2.294882    29.881138     0.498854     0.385027    33.295479\n",
      "std       0.837836    14.413493     1.041658     0.865560    51.758668\n",
      "min       1.000000     0.170000     0.000000     0.000000     0.000000\n",
      "25%       2.000000          NaN     0.000000     0.000000          NaN\n",
      "50%       3.000000          NaN     0.000000     0.000000          NaN\n",
      "75%       3.000000          NaN     1.000000     0.000000          NaN\n",
      "max       3.000000    80.000000     8.000000     9.000000   512.329200\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 8 columns):\n",
      "Pclass      1309 non-null int64\n",
      "Name        1309 non-null object\n",
      "Sex         1309 non-null object\n",
      "Age         1046 non-null float64\n",
      "SibSp       1309 non-null int64\n",
      "Parch       1309 non-null int64\n",
      "Fare        1308 non-null float64\n",
      "Embarked    1307 non-null object\n",
      "dtypes: float64(2), int64(3), object(3)\n",
      "memory usage: 92.0+ KB\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/madhatter106/anaconda3/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "print(dfTemp.describe())\n",
    "print('-' * 50)\n",
    "print(dfTemp.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're ready to do some imputation/corrections.\n",
    "### Dealing with the \"free\" tickets in the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>3</td>\n",
       "      <td>Leonard, Mr. Lionel</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>1</td>\n",
       "      <td>Harrison, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>3</td>\n",
       "      <td>Tornquist, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>2</td>\n",
       "      <td>Parkes, Mr. Francis \"Frank\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mr. William Cahoone Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>2</td>\n",
       "      <td>Cunningham, Mr. Alfred Fleming</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>2</td>\n",
       "      <td>Campbell, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>2</td>\n",
       "      <td>Frost, Mr. Anthony Wood \"Archie\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mr. Alfred</td>\n",
       "      <td>male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>1</td>\n",
       "      <td>Parr, Mr. William Henry Marsh</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>2</td>\n",
       "      <td>Watson, Mr. Ennis Hastings</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>2</td>\n",
       "      <td>Knight, Mr. Robert J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>1</td>\n",
       "      <td>Andrews, Mr. Thomas Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>1</td>\n",
       "      <td>Fry, Mr. Richard</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>1</td>\n",
       "      <td>Reuchlin, Jonkheer. John George</td>\n",
       "      <td>male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>1</td>\n",
       "      <td>Chisholm, Mr. Roderick Robert Crispin</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>1</td>\n",
       "      <td>Ismay, Mr. Joseph Bruce</td>\n",
       "      <td>male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass                                   Name   Sex   Age  SibSp  Parch  \\\n",
       "179       3                    Leonard, Mr. Lionel  male  36.0      0      0   \n",
       "263       1                  Harrison, Mr. William  male  40.0      0      0   \n",
       "271       3           Tornquist, Mr. William Henry  male  25.0      0      0   \n",
       "277       2            Parkes, Mr. Francis \"Frank\"  male   NaN      0      0   \n",
       "302       3        Johnson, Mr. William Cahoone Jr  male  19.0      0      0   \n",
       "413       2         Cunningham, Mr. Alfred Fleming  male   NaN      0      0   \n",
       "466       2                  Campbell, Mr. William  male   NaN      0      0   \n",
       "481       2       Frost, Mr. Anthony Wood \"Archie\"  male   NaN      0      0   \n",
       "597       3                    Johnson, Mr. Alfred  male  49.0      0      0   \n",
       "633       1          Parr, Mr. William Henry Marsh  male   NaN      0      0   \n",
       "674       2             Watson, Mr. Ennis Hastings  male   NaN      0      0   \n",
       "732       2                   Knight, Mr. Robert J  male   NaN      0      0   \n",
       "806       1                 Andrews, Mr. Thomas Jr  male  39.0      0      0   \n",
       "815       1                       Fry, Mr. Richard  male   NaN      0      0   \n",
       "822       1        Reuchlin, Jonkheer. John George  male  38.0      0      0   \n",
       "266       1  Chisholm, Mr. Roderick Robert Crispin  male   NaN      0      0   \n",
       "372       1                Ismay, Mr. Joseph Bruce  male  49.0      0      0   \n",
       "\n",
       "     Fare Embarked  \n",
       "179   0.0        S  \n",
       "263   0.0        S  \n",
       "271   0.0        S  \n",
       "277   0.0        S  \n",
       "302   0.0        S  \n",
       "413   0.0        S  \n",
       "466   0.0        S  \n",
       "481   0.0        S  \n",
       "597   0.0        S  \n",
       "633   0.0        S  \n",
       "674   0.0        S  \n",
       "732   0.0        S  \n",
       "806   0.0        S  \n",
       "815   0.0        S  \n",
       "822   0.0        S  \n",
       "266   0.0        S  \n",
       "372   0.0        S  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTemp[dfTemp.Fare==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Some common details among \"free ticket\"  cases:\n",
    "* embarked in Southampton\n",
    "* SibSp = 0\n",
    "* Parch = 0\n",
    "* Sex = male\n",
    "* Pclass = 1, 2, 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It makes more sense to me to impute the class-dependent median fee of tickets bought in Southampton:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Fares_S_AllCl_Non0 = dfTemp.loc[(dfTemp.Fare != 0) & (dfTemp.Embarked == 'S'),['Pclass', 'Fare']]\n",
    "for i in range(1,4):\n",
    "    dfTrain.loc[(dfTrain.Fare == 0) & (dfTrain.Pclass == i),'Fare'] = Fares_S_AllCl_Non0[Fares_S_AllCl_Non0.Pclass == i].Fare.median()\n",
    "    dfTest.loc[(dfTest.Fare == 0) & dfTest.Pclass == i, 'Fare'] = Fares_S_AllCl_Non0[Fares_S_AllCl_Non0.Pclass == i].Fare.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imputing missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Embarked  \n",
    "**Embarked** is only missing 2 entries, so I'd impute base on what's more common. But since the goal of this exercise is to be OCD:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Name  Fare  Pclass\n",
       "61                         Icard, Miss. Amelie  80.0       1\n",
       "829  Stone, Mrs. George Nelson (Martha Evelyn)  80.0       1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTrain.loc[dfTrain.Embarked.isnull(),['Name','Fare', 'Pclass']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0e8fafc5f8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f63cF04dMJT4qPmju071LeWTVYxwqznGpOhEREQmjD4BhwCnV/pxdZU3G0RAW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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e8fa45668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.factorplot(x='Pclass',y='Fare',hue='Embarked', data=dfTemp,aspect=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This suggests that 'S' is a relative safe bet. But I still wonder what the more frequent value in 'Embarked' is?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTrain['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'm fairly confident 'S' is the right value to impute to 'Embarked'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfTrain['Embarked'].fillna('S', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Fare (in the test set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the factor plot above plotting fare against class, seems safe to correct bad Fare data based on Passenger Class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pclass4fare = dfTest.loc[dfTest.Fare.isnull(), 'Pclass'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "msgClassMedianFare = dfTest[(dfTest.Pclass==pclass4fare)].Fare.dropna().median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfTest.loc[dfTest.Fare.isnull(),'Fare'] = msgClassMedianFare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Age:  \n",
    "\n",
    "Age is one of those things that to a first approximation can be estimated by how a person is referred to.\n",
    "First I am going to catalogue titles present in names and create another feature, \"Title\". To do this I need to do an inventory of all possible titles present in the dataset. Titles seem to be the seconde word in the name string, ending with a '.'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "nameset = set()\n",
    "cnt = 0\n",
    "for name in dfTemp.Name.values:\n",
    "    for subname in name.split(' '):\n",
    "        # Now I grab the part that has a '.' after but verify it is an abbreviation, not an initial...\n",
    "        if '.' in subname and len(subname) > 2:\n",
    "            cnt += 1\n",
    "            nameset.add(subname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Mr.', 'Mlle.', 'Miss.', 'Countess.', 'Dona.', 'Master.', 'Jonkheer.', 'Dr.', 'Ms.', 'Mrs.', 'Lady.', 'Major.', 'Col.', 'Capt.', 'Mme.', 'Rev.', 'Sir.', 'Don.'}\n"
     ]
    }
   ],
   "source": [
    "print(nameset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createTitle(name):\n",
    "    return list(set(name.split(' ')) & nameset)[0][:-1]\n",
    "\n",
    "dfTrain['Title'] = dfTrain.Name.apply(createTitle)\n",
    "dfTest['Title'] = dfTest.Name.apply(createTitle)\n",
    "dfTemp['Title'] = dfTemp.Name.apply(createTitle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does title allow distinguishing age?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cYEH0ZTKZhrdtU3kxgpX5AQAAeoMwH7J4h3k+xAOt2Dpn3u4wXzl+2y9MAAAARBVhPmTx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\ndE+SdOTI0dDGBaAzbYf54iJ33yfpRyT9GwWh+c8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PO4lqlfnOSw1kq7ebYGkjE8wTPVTm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\nMcaYhvH/JqDriBrSUfoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e8f78d588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = pl.figure(figsize=(12,8))\n",
    "ax = f.add_subplot()\n",
    "violin = sb.violinplot(x='Title',y='Age',data=dfTemp,ax=ax, scale='area')\n",
    "for item in violin.get_xticklabels():\n",
    "    item.set_rotation(45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not awesome. Still, some titles come with a wide age range; others, like Master have a markedly narrower range, and that could still be informative for my Age imputation. Also I probably don't need all titles. What titles do correspond to missing ages?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mr', 'Mrs', 'Miss', 'Master', 'Dr', 'Ms'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTemp.Title[dfTemp.Age.isnull()].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly I don't need to use all of the 'Title' data. Based on the graph above, I will pack Ms and Miss together and impute missing age from the median Age of Miss. I will impute the remaining missing ages directly from the median Age of their corresponding category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# re-titling Ms as Miss\n",
    "dfTemp.loc[dfTemp.Title=='Ms', 'Title'] = 'Miss'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfTrain.loc[(dfTrain.Age.isnull()) &\n",
    "            ((dfTrain.Title=='Miss')|\n",
    "             (dfTrain.Title=='Ms')),'Age'] = dfTemp.loc[dfTemp.Title=='Miss','Age'].median()\n",
    "dfTest.loc[(dfTest.Age.isnull()) &\n",
    "          ((dfTest.Title=='Miss')|\n",
    "          (dfTest.Title=='Ms')),'Age'] = dfTemp.loc[dfTemp.Title=='Miss','Age'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for title in ['Mr', 'Mrs','Master','Dr']:\n",
    "    dfTrain.loc[(dfTrain.Age.isnull()) &\n",
    "               (dfTrain.Title==title),'Age'] = dfTemp.loc[dfTemp.Title==title, 'Age'].median()\n",
    "    dfTest.loc[(dfTest.Age.isnull()) &\n",
    "              (dfTest.Title==title),'Age'] = dfTemp.loc[dfTemp.Title==title,'Age'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 10 columns):\n",
      "Survived    891 non-null int64\n",
      "Pclass      891 non-null int64\n",
      "Name        891 non-null object\n",
      "Sex         891 non-null object\n",
      "Age         891 non-null float64\n",
      "SibSp       891 non-null int64\n",
      "Parch       891 non-null int64\n",
      "Fare        891 non-null float64\n",
      "Embarked    891 non-null object\n",
      "Title       891 non-null object\n",
      "dtypes: float64(2), int64(4), object(4)\n",
      "memory usage: 69.7+ KB\n",
      "None\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 9 columns):\n",
      "Pclass      418 non-null int64\n",
      "Name        418 non-null object\n",
      "Sex         418 non-null object\n",
      "Age         418 non-null float64\n",
      "SibSp       418 non-null int64\n",
      "Parch       418 non-null int64\n",
      "Fare        418 non-null float64\n",
      "Embarked    418 non-null object\n",
      "Title       418 non-null object\n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 29.5+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(dfTrain.info())\n",
    "print('-' * 50)\n",
    "print(dfTest.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I can simplify this data set by dropping 'Title' and 'Name' data from both sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfTrain.drop(['Name','Title'],inplace=True, axis=1)\n",
    "dfTest.drop(['Name','Title'], inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labeling categorical data\n",
    "\n",
    "Sex, and Embarked are categorical variables that need to be relabeled numerically. Because neither feature is ordinal, I'm going to one-hot encode both, using pandas' get_dummies method. This will avoid introducing a \"ranking\" in these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one-hot encoding non-hierarchical categorical labels\n",
    "dfTrain = pd.concat([dfTrain,pd.get_dummies(dfTrain[['Sex', 'Embarked']])],axis=1)\n",
    "dfTest = pd.concat([dfTest,pd.get_dummies(dfTest[['Sex', 'Embarked',]])],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we don't need 'Sex' or 'Embarked any more so we drop them from both sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfTrain.drop(['Sex', 'Embarked'], axis=1, inplace=True)\n",
    "dfTest.drop(['Sex', 'Embarked'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This concludes this part of the pre-processing; the data cleanup.\n",
    "Here's what the data sets look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      "Survived      891 non-null int64\n",
      "Pclass        891 non-null int64\n",
      "Age           891 non-null float64\n",
      "SibSp         891 non-null int64\n",
      "Parch         891 non-null int64\n",
      "Fare          891 non-null float64\n",
      "Sex_female    891 non-null float64\n",
      "Sex_male      891 non-null float64\n",
      "Embarked_C    891 non-null float64\n",
      "Embarked_Q    891 non-null float64\n",
      "Embarked_S    891 non-null float64\n",
      "dtypes: float64(7), int64(4)\n",
      "memory usage: 76.6 KB\n",
      "None\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 10 columns):\n",
      "Pclass        418 non-null int64\n",
      "Age           418 non-null float64\n",
      "SibSp         418 non-null int64\n",
      "Parch         418 non-null int64\n",
      "Fare          418 non-null float64\n",
      "Sex_female    418 non-null float64\n",
      "Sex_male      418 non-null float64\n",
      "Embarked_C    418 non-null float64\n",
      "Embarked_Q    418 non-null float64\n",
      "Embarked_S    418 non-null float64\n",
      "dtypes: float64(7), int64(3)\n",
      "memory usage: 32.7 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(dfTrain.info())\n",
    "print('-' * 50)\n",
    "print(dfTest.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm going to pickle both Dataframes for safekeeping until the next blog..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfTrain.to_pickle('/home/madhatter106/DATA/Titanic/dfTrainCln_I.pkl')\n",
    "dfTest.to_pickle('/home/madhatter106/DATA/Titanic/dfTestCln_I.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next notebook, I'll be looking to do some additional post-cleanup pre-processing. Until next time!"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
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  "nikola": {
   "category": "",
   "date": "2017-01-09 16:46:09 UTC-05:00",
   "description": "",
   "link": "",
   "slug": "titanic-i-cleanup",
   "tags": "Ipython, Jupyter notebook, kaggle, titanic",
   "title": "Titanic Diaries - Part I: Data Dusting",
   "type": "text"
  }
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