4-pandas-scikit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "pandas introduction\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = pd.Series(np.array([1, 2, 3, 4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([0, 1, 4, 9, 16], name='Squares')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n1     1\n2     4\n3     9\n4    16\nName: Squares, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     1\n16    1\n9     1\n1     1\n0     1\nName: Squares, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    4\n3    9\nName: Squares, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Squares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Squares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a = np.random.standard_normal(10)\n",
    "b = np.random.random(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'normal': a, 'uniform': b})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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       "      <td>-0.615691</td>\n",
       "      <td>0.653197</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.099342</td>\n",
       "      <td>0.897518</td>\n",
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       "      <th>7</th>\n",
       "      <td>-1.132420</td>\n",
       "      <td>0.232371</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.955041</td>\n",
       "      <td>0.337676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.467890</td>\n",
       "      <td>0.902704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.615691</td>\n",
       "      <td>0.653197</td>\n",
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       "      <th>6</th>\n",
       "      <td>-0.099342</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.132420</td>\n",
       "      <td>0.232371</td>\n",
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       "      <th>8</th>\n",
       "      <td>0.955041</td>\n",
       "      <td>0.337676</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normal</th>\n",
       "      <th>uniform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.808904</td>\n",
       "      <td>0.337886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.810783</td>\n",
       "      <td>0.976217</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.411705</td>\n",
       "      <td>0.062335</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.024299</td>\n",
       "      <td>0.782553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.615691</td>\n",
       "      <td>0.653197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.099342</td>\n",
       "      <td>0.897518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.132420</td>\n",
       "      <td>0.232371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.955041</td>\n",
       "      <td>0.337676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.467890</td>\n",
       "      <td>0.902704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(1, axis=0)\n",
    "# explain inplace\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normal</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>0.024299</td>\n",
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       "      <th>6</th>\n",
       "      <td>-0.099342</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.132420</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.955041</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.467890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('uniform', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>8</th>\n",
       "      <td>0.955041</td>\n",
       "      <td>0.337676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.467890</td>\n",
       "      <td>0.902704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normal</th>\n",
       "      <th>uniform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.808904</td>\n",
       "      <td>0.337886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.096323</td>\n",
       "      <td>0.398916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.810783</td>\n",
       "      <td>0.976217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.411705</td>\n",
       "      <td>0.062335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.024299</td>\n",
       "      <td>0.782553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.615691</td>\n",
       "      <td>0.653197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.099342</td>\n",
       "      <td>0.897518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.132420</td>\n",
       "      <td>0.232371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.955041</td>\n",
       "      <td>0.337676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.467890</td>\n",
       "      <td>0.902704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal     0.808904\nuniform    0.337886\nName: 0, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal     0.808904\nuniform    0.337886\nName: 0, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.ix[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n* loc works on labels in the index.\\n* iloc works on the positions in the index (so it only takes integers).\\n* ix usually tries to behave like loc but \\nfalls back to behaving like iloc if the label is not in the index.\\n'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "* loc works on labels in the index.\n",
    "* iloc works on the positions in the index (so it only takes integers).\n",
    "* ix usually tries to behave like loc but \n",
    "falls back to behaving like iloc if the label is not in the index.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>-1</th>\n",
       "      <th>6.09</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.8</td>\n",
       "      <td>2.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>-1</th>\n",
       "      <th>6.09</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.8</td>\n",
       "      <td>2.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('./data.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./data.txt', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7faf349b8b38>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L7jhEJKPiKmXOowE/Lmmgdy4NcORB5O+U+qYh4OclnZ3JuTQR9S6XJUnAuLHhcke5hF+X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\nIiIFY0kTESkYS5qISMH+P9EK0AkagI8oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faef557a240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data[0], data[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PassengerId  Survived  Pclass  \\\n0              1         0       3   \n1              2         1       1   \n2              3         1       3   \n3              4         1       1   \n4              5         0       3   \n5              6         0       3   \n6              7         0       1   \n7              8         0       3   \n8              9         1       3   \n9             10         1       2   \n10            11         1       3   \n11            12         1       1   \n12            13         0       3   \n13            14         0       3   \n14            15         0       3   \n15            16         1       2   \n16            17         0       3   \n17            18         1       2   \n18            19         0       3   \n19            20         1       3   \n20            21         0       2   \n21            22         1       2   \n22            23         1       3   \n23            24         1       1   \n24            25         0       3   \n25            26         1       3   \n26            27         0       3   \n27            28         0       1   \n28            29         1       3   \n29            30         0       3   \n..           ...       ...     ...   \n861          862         0       2   \n862          863         1       1   \n863          864         0       3   \n864          865         0       2   \n865          866         1       2   \n866          867         1       2   \n867          868         0       1   \n868          869         0       3   \n869          870         1       3   \n870          871         0       3   \n871          872         1       1   \n872          873         0       1   \n873          874         0       3   \n874          875         1       2   \n875          876         1       3   \n876          877         0       3   \n877          878         0       3   \n878          879         0       3   \n879          880         1       1   \n880          881         1       2   \n881          882         0       3   \n882          883         0       3   \n883          884         0       2   \n884          885         0       3   \n885          886         0       3   \n886          887         0       2   \n887          888         1       1   \n888          889         0       3   \n889          890         1       1   \n890          891         0       3   \n\n                                                  Name     Sex   Age  SibSp  \\\n0                              Braund, Mr. Owen Harris    male  22.0      1   \n1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n2                               Heikkinen, Miss. Laina  female  26.0      0   \n3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n4                             Allen, Mr. William Henry    male  35.0      0   \n5                                     Moran, Mr. James    male   NaN      0   \n6                              McCarthy, Mr. Timothy J    male  54.0      0   \n7                       Palsson, Master. Gosta Leonard    male   2.0      3   \n8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n10                     Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n12                      Saundercock, Mr. William Henry    male  20.0      0   \n13                         Andersson, Mr. Anders Johan    male  39.0      1   \n14                Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n16                                Rice, Master. Eugene    male   2.0      4   \n17                        Williams, Mr. Charles Eugene    male   NaN      0   \n18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n19                             Masselmani, Mrs. Fatima  female   NaN      0   \n20                                Fynney, Mr. Joseph J    male  35.0      0   \n21                               Beesley, Mr. Lawrence    male  34.0      0   \n22                         McGowan, Miss. Anna \"Annie\"  female  15.0      0   \n23                        Sloper, Mr. William Thompson    male  28.0      0   \n24                       Palsson, Miss. Torborg Danira  female   8.0      3   \n25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.0      1   \n26                             Emir, Mr. Farred Chehab    male   NaN      0   \n27                      Fortune, Mr. Charles Alexander    male  19.0      3   \n28                       O'Dwyer, Miss. Ellen \"Nellie\"  female   NaN      0   \n29                                 Todoroff, Mr. Lalio    male   NaN      0   \n..                                                 ...     ...   ...    ...   \n861                        Giles, Mr. Frederick Edward    male  21.0      1   \n862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female  48.0      0   \n863                  Sage, Miss. Dorothy Edith \"Dolly\"  female   NaN      8   \n864                             Gill, Mr. John William    male  24.0      0   \n865                           Bystrom, Mrs. (Karolina)  female  42.0      0   \n866                       Duran y More, Miss. Asuncion  female  27.0      1   \n867               Roebling, Mr. Washington Augustus II    male  31.0      0   \n868                        van Melkebeke, Mr. Philemon    male   NaN      0   \n869                    Johnson, Master. Harold Theodor    male   4.0      1   \n870                                  Balkic, Mr. Cerin    male  26.0      0   \n871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.0      1   \n872                           Carlsson, Mr. Frans Olof    male  33.0      0   \n873                        Vander Cruyssen, Mr. Victor    male  47.0      0   \n874              Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.0      1   \n875                   Najib, Miss. Adele Kiamie \"Jane\"  female  15.0      0   \n876                      Gustafsson, Mr. Alfred Ossian    male  20.0      0   \n877                               Petroff, Mr. Nedelio    male  19.0      0   \n878                                 Laleff, Mr. Kristo    male   NaN      0   \n879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n881                                 Markun, Mr. Johann    male  33.0      0   \n882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n883                      Banfield, Mr. Frederick James    male  28.0      0   \n884                             Sutehall, Mr. Henry Jr    male  25.0      0   \n885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n886                              Montvila, Rev. Juozas    male  27.0      0   \n887                       Graham, Miss. Margaret Edith  female  19.0      0   \n888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n889                              Behr, Mr. Karl Howell    male  26.0      0   \n890                                Dooley, Mr. Patrick    male  32.0      0   \n\n     Parch            Ticket      Fare        Cabin Embarked  \n0        0         A/5 21171    7.2500          NaN        S  \n1        0          PC 17599   71.2833          C85        C  \n2        0  STON/O2. 3101282    7.9250          NaN        S  \n3        0            113803   53.1000         C123        S  \n4        0            373450    8.0500          NaN        S  \n5        0            330877    8.4583          NaN        Q  \n6        0             17463   51.8625          E46        S  \n7        1            349909   21.0750          NaN        S  \n8        2            347742   11.1333          NaN        S  \n9        0            237736   30.0708          NaN        C  \n10       1           PP 9549   16.7000           G6        S  \n11       0            113783   26.5500         C103        S  \n12       0         A/5. 2151    8.0500          NaN        S  \n13       5            347082   31.2750          NaN        S  \n14       0            350406    7.8542          NaN        S  \n15       0            248706   16.0000          NaN        S  \n16       1            382652   29.1250          NaN        Q  \n17       0            244373   13.0000          NaN        S  \n18       0            345763   18.0000          NaN        S  \n19       0              2649    7.2250          NaN        C  \n20       0            239865   26.0000          NaN        S  \n21       0            248698   13.0000          D56        S  \n22       0            330923    8.0292          NaN        Q  \n23       0            113788   35.5000           A6        S  \n24       1            349909   21.0750          NaN        S  \n25       5            347077   31.3875          NaN        S  \n26       0              2631    7.2250          NaN        C  \n27       2             19950  263.0000  C23 C25 C27        S  \n28       0            330959    7.8792          NaN        Q  \n29       0            349216    7.8958          NaN        S  \n..     ...               ...       ...          ...      ...  \n861      0             28134   11.5000          NaN        S  \n862      0             17466   25.9292          D17        S  \n863      2          CA. 2343   69.5500          NaN        S  \n864      0            233866   13.0000          NaN        S  \n865      0            236852   13.0000          NaN        S  \n866      0     SC/PARIS 2149   13.8583          NaN        C  \n867      0          PC 17590   50.4958          A24        S  \n868      0            345777    9.5000          NaN        S  \n869      1            347742   11.1333          NaN        S  \n870      0            349248    7.8958          NaN        S  \n871      1             11751   52.5542          D35        S  \n872      0               695    5.0000  B51 B53 B55        S  \n873      0            345765    9.0000          NaN        S  \n874      0         P/PP 3381   24.0000          NaN        C  \n875      0              2667    7.2250          NaN        C  \n876      0              7534    9.8458          NaN        S  \n877      0            349212    7.8958          NaN        S  \n878      0            349217    7.8958          NaN        S  \n879      1             11767   83.1583          C50        C  \n880      1            230433   26.0000          NaN        S  \n881      0            349257    7.8958          NaN        S  \n882      0              7552   10.5167          NaN        S  \n883      0  C.A./SOTON 34068   10.5000          NaN        S  \n884      0   SOTON/OQ 392076    7.0500          NaN        S  \n885      5            382652   29.1250          NaN        Q  \n886      0            211536   13.0000          NaN        S  \n887      0            112053   30.0000          B42        S  \n888      2        W./C. 6607   23.4500          NaN        S  \n889      0            111369   30.0000         C148        C  \n890      0            370376    7.7500          NaN        Q  \n\n[891 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('./data/train.csv')\n",
    "print(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "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>22.000000</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>38.000000</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>female</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>male</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>A24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>male</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<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>22.000000</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>38.000000</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>female</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>male</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>A24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>male</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.fillna(train.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7faeef171dd8>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U5Ofn48yZM9Dr9SgoKEBSUpJr+969e7F9+3bodDqkpqYiPz8fWi3/fSAiulE8Jm5paSkc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faef0441358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train['Fare'], train['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unnecessary columns, these columns won't be useful in analysis and prediction\n",
    "train = train.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.get_dummies(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.fillna(train.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_Y = train['Survived'].ix[0:690]\n",
    "train_X = train.drop('Survived', axis=1).ix[0:690]\n",
    "\n",
    "test_Y = train['Survived'].ix[691:]\n",
    "test_X = train.drop('Survived', axis=1).ix[691:]\n",
    "\n",
    "# Random Forest model\n",
    "forest = RandomForestClassifier(max_features='sqrt')\n",
    "parameter_grid = {\n",
    "    'max_depth': [4, 5],\n",
    "    'n_estimators': [200, 210],\n",
    "    'criterion': ['entropy']\n",
    "}\n",
    "\n",
    "cross_validation = StratifiedKFold(train_Y, n_folds=5)\n",
    "grid_search = GridSearchCV(forest,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "grid_search.fit(train_X, train_Y)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V9Anp5GQl4rC5kTqsTDRF3kuSZcoanQRlGYFQieSiZD2ZI1w9dSS5JgNdPDHL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faef038ee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import linear_model, datasets\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, :2]  # we only take the first two features.\n",
    "Y = iris.target\n",
    "\n",
    "h = .02  # step size in the mesh\n",
    "\n",
    "logreg = linear_model.LogisticRegression(C=1e5)\n",
    "\n",
    "# we create an instance of Neighbours Classifier and fit the data.\n",
    "logreg.fit(X, Y)\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1, figsize=(4, 3))\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "\n",
    "# Plot also the training points\n",
    "plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}