ProjectCode.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic=pd.read_csv('titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <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>26.0</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>35.0</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>35.0</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",
       "    <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>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>27.0</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>19.0</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>NaN</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>26.0</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>32.0</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": [
       "     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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_analysis=titanic.groupby('Survived')['Survived'].count()\n",
    "survival_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.61616161616161"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total=survival_analysis[0]+survival_analysis[1]\n",
    "death_percentage=(survival_analysis[0]/total)*100\n",
    "death_percentage\n",
    "#death_percentage is the percent of the people who died."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    25.796178\n",
       "male      81.109185\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_gender=titanic.groupby('Sex')['Survived'].sum()\n",
    "total_gender=titanic.groupby('Sex')['Survived'].count()\n",
    "dead_gender=(total_gender-survived_gender)\n",
    "dead_gender_percentage= (dead_gender/total_gender)*100\n",
    "dead_gender_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(dead_gender_percentage.index,dead_gender_percentage.values)\n",
    "plt.title('Gender wise Death Percentage')\n",
    "for i, value in enumerate(dead_gender_percentage.values):\n",
    "    plt.text(i, value-5, int(value), fontsize=12, color='white', \n",
    "             horizontalalignment='center', verticalalignment='top')\n",
    "plt.show()\n",
    "#Gender wise Death Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "First Class     37.037037\n",
       "Second Class    52.717391\n",
       "Third Class     75.763747\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_class=titanic.groupby('Pclass')['Survived'].sum()\n",
    "total_class=titanic.groupby('Pclass')['Survived'].count()\n",
    "dead_class=total_class-survived_class\n",
    "dead_class_percentage=(dead_class/total_class)*100\n",
    "dead_class_percentage=dead_class_percentage.rename(index={1:'First Class',2:'Second Class',3:'Third Class'})\n",
    "dead_class_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(dead_class_percentage.index,dead_class_percentage.values)\n",
    "plt.title('Class wise Death Percentage')\n",
    "for i, value in enumerate(dead_class_percentage.values):\n",
    "    plt.text(i, value-5, int(value), fontsize=12, color='white', \n",
    "             horizontalalignment='center', verticalalignment='top')\n",
    "plt.show()\n",
    "#Class wise Death Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Total</th>\n",
       "      <th>Survival Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alone</th>\n",
       "      <td>163</td>\n",
       "      <td>537</td>\n",
       "      <td>30.353818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not Alone</th>\n",
       "      <td>179</td>\n",
       "      <td>354</td>\n",
       "      <td>50.564972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Survived  Total  Survival Rate\n",
       "Alone           163    537      30.353818\n",
       "Not Alone       179    354      50.564972"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notalone = np.where((titanic.SibSp + titanic.Parch) >= 1, 'Not Alone', 'Alone')\n",
    "loneliness_survival = titanic.groupby(notalone, as_index=False)['Survived'].agg([np.sum, np.size])\n",
    "loneliness_survival = loneliness_survival.rename(columns={'sum':'Survived', 'size':'Total'})\n",
    "loneliness_survival['Survival Rate'] = (loneliness_survival.Survived/loneliness_survival.Total)*100\n",
    "loneliness_survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(loneliness_survival.index,loneliness_survival['Survival Rate'])\n",
    "plt.title('Death Percentage of travelers with or without companions')\n",
    "for i, value in enumerate(loneliness_survival['Survival Rate']):\n",
    "    plt.text(i, value-5, int(value), fontsize=12, color='white', \n",
    "             horizontalalignment='center', verticalalignment='top')\n",
    "plt.show()\n",
    "#Lonliness Survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Observing the results we can see that people traveling with someone had a much higher(almost 20%) chance of survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived     Fare\n",
       "0           0   7.2500\n",
       "1           1  71.2833\n",
       "2           1   7.9250\n",
       "3           1  53.1000\n",
       "4           0   8.0500\n",
       "..        ...      ...\n",
       "886         0  13.0000\n",
       "887         1  30.0000\n",
       "888         0  23.4500\n",
       "889         1  30.0000\n",
       "890         0   7.7500\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([titanic.Survived,titanic.Fare],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nikhil Manglik\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frange=[0,250,500,750]\n",
    "ax = sns.lmplot('Fare', 'Survived', data = titanic, \n",
    "               palette = 'summer', x_bins = frange)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using this graph we can see that people who bought an expensive ticket had a higher chance of survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d3413713cbe815757a26d37378f5b339a563084230f098ed3b8c6029164fed9d"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 64-bit ('base': conda)",
   "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": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}