{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Titanic Dataset: Basic Data Exploration\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Import Libraries\n",
    "from matplotlib import pyplot as plt\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Import Dataset\n",
    "train_dataset = pd.read_csv(\"dataset/train.csv\")\n",
    "test_datset = pd.read_csv(\"dataset/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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       "<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",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <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>0</td>\n",
       "      <td>26.0</td>\n",
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       "      <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>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Chnage Male/Female to numeric classes\n",
    "train_dataset['Sex'].replace(to_replace=dict(female=0, male=1), inplace=True)\n",
    "train_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">PassengerId</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Survived</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Parch</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>549.0</td>\n",
       "      <td>447.016393</td>\n",
       "      <td>260.640469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>211.00</td>\n",
       "      <td>455.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>22.117887</td>\n",
       "      <td>31.388207</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>10.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>263.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>342.0</td>\n",
       "      <td>444.368421</td>\n",
       "      <td>252.358840</td>\n",
       "      <td>2.0</td>\n",
       "      <td>250.75</td>\n",
       "      <td>439.5</td>\n",
       "      <td>651.5</td>\n",
       "      <td>890.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>48.395408</td>\n",
       "      <td>66.596998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.4750</td>\n",
       "      <td>26.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId                                                            \\\n",
       "        count        mean         std  min     25%    50%    75%    max   \n",
       "0       549.0  447.016393  260.640469  1.0  211.00  455.0  675.0  891.0   \n",
       "1       342.0  444.368421  252.358840  2.0  250.75  439.5  651.5  890.0   \n",
       "\n",
       "  Survived         ...    Parch        Fare                             \\\n",
       "     count mean    ...      75%  max  count       mean        std  min   \n",
       "0    549.0  0.0    ...      0.0  6.0  549.0  22.117887  31.388207  0.0   \n",
       "1    342.0  1.0    ...      1.0  5.0  342.0  48.395408  66.596998  0.0   \n",
       "\n",
       "                                  \n",
       "       25%   50%   75%       max  \n",
       "0   7.8542  10.5  26.0  263.0000  \n",
       "1  12.4750  26.0  57.0  512.3292  \n",
       "\n",
       "[2 rows x 64 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.groupby('Survived',as_index=False).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### How does Gender affect chances of Survival?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation Test = -0.5433513806577553\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef70f11710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male = []\n",
    "female = []\n",
    "for i in [0, 1]:\n",
    "    male.append(len(train_dataset.loc[(train_dataset['Survived'] == i) & (train_dataset['Sex'] == 1)]) )\n",
    "    female.append(len(train_dataset.loc[(train_dataset['Survived'] == i) & (train_dataset['Sex'] == 0)]))\n",
    "\n",
    "ind = np.arange(len(male)) \n",
    "width = 0.3  \n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "ax.bar(ind - width/2, male, width, color='SkyBlue', label='Men')\n",
    "ax.bar(ind + width/2, female, width, color='IndianRed', label='Women')\n",
    "\n",
    "\n",
    "ax.set_ylabel('Number of people')\n",
    "ax.set_xlabel('Survived')\n",
    "ax.set_title('Survivors by gender')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels([\"0\", \"1\"])\n",
    "ax.legend()\n",
    "\n",
    "print(\"Pearson Correlation Test = {}\".format(pearsonr(train_dataset[\"Sex\"], train_dataset[\"Survived\"])[0]) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### How does Class affect chances of Survival?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef70fcc5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation Test = -0.33848103596101536\n"
     ]
    }
   ],
   "source": [
    "uniq_pclass = [1,2,3]\n",
    "Pclass_survivors = []\n",
    "total_survivors = []\n",
    "for i in uniq_pclass:\n",
    "    Pclass_survivors.append(len(train_dataset.loc[(train_dataset['Survived'] == 1) & (train_dataset['Pclass'] == i)]) )\n",
    "    total_survivors.append(len(train_dataset.loc[(train_dataset['Pclass'] == i)]) )\n",
    "ind = np.arange(len(Pclass_survivors))  \n",
    "width = 0.35 \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "ax.bar(ind, total_survivors, width, color='IndianRed', label=\"dead folks\")\n",
    "ax.bar(ind, Pclass_survivors, width, color='SkyBlue', label=\"survivors\")\n",
    "\n",
    "ax.set_ylabel('Survivors')\n",
    "ax.set_xlabel('Pclass')\n",
    "ax.set_title('Amount of Survivors per class')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(uniq_pclass)\n",
    "ax.legend()\n",
    "plt.show()\n",
    "\n",
    "print(\"Pearson Correlation Test = {}\".format(pearsonr(train_dataset[\"Pclass\"], train_dataset[\"Survived\"])[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### The effect of Age on Survival?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation Test = -0.25408475420305304\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef70fb65f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_age = pd.DataFrame(train_dataset.loc[(train_dataset['Survived'] == 1)].Age)\n",
    "dead_age = pd.DataFrame(train_dataset.loc[(train_dataset['Survived'] == 0)].Age)\n",
    "ages=pd.concat([survived_age, dead_age], axis=1)\n",
    "ages.columns = [\"Alive\", \"Dead\"]\n",
    "\n",
    "ax = ages.plot(kind='hist', alpha=0.5, stacked=True, bins=15, figsize=(8,5))\n",
    "ax.set_xlabel(\"Age\")\n",
    "ax.set_ylabel(\"Number of People\")\n",
    "\n",
    "#Drop missing Ages\n",
    "tra = train_dataset.dropna()\n",
    "print(\"Pearson Correlation Test = {}\".format(pearsonr(tra[\"Age\"], tra[\"Survived\"])[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Does Fare have an effect on who survives?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation Test = 0.13424105283521096\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef6c876128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(train_dataset[[\"Survived\", \"Age\", \"Fare\"]]) \n",
    "col = df1.Survived.map({0:'r', 1:'b'})\n",
    "d=df1.plot(x='Age', y='Fare', c=col, kind='scatter', figsize=(8,5))\n",
    "\n",
    "d.legend([\"Dead\"])\n",
    "\n",
    "# Pearsons Correlation Test\n",
    "tra = train_dataset.dropna()\n",
    "print(\"Pearson Correlation Test = {}\".format(pearsonr(tra[\"Fare\"], tra[\"Survived\"])[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Embarked?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ryan\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:4619: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n",
      "C:\\Users\\Ryan\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:2530: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
      "  obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef7142b2e8>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ef77459a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dropped_ = train_dataset.dropna()\n",
    "\n",
    "\n",
    "dropped_['Embarked'].replace(to_replace=dict(S=0, C=1, Q=2), inplace=True)\n",
    "dropped_.head()\n",
    "\n",
    "df_s = pd.DataFrame(dropped_[[\"Survived\", \"Embarked\"]].loc[(train_dataset['Survived'] == 1)]).groupby(['Embarked']).agg(['count']).reset_index()\n",
    "df_d = pd.DataFrame(dropped_[[\"Survived\", \"Embarked\"]].loc[(train_dataset['Survived'] == 0)]).groupby(['Embarked']).agg(['count']).reset_index()\n",
    "\n",
    "\n",
    "df_embarked=pd.merge(df_s,df_d,on='Embarked', how='left')\n",
    "df_embarked.plot(x = \"Embarked\", kind=\"bar\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}