DataScience365/Day 2/Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "  </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  \\\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",
       "   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  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PassengerId = train[\"PassengerId\"]\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fulldata = [test,train]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "FamilySize     891 non-null int64\n",
      "dtypes: float64(2), int64(6), object(5)\n",
      "memory usage: 90.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0.62963</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass          1         2         3\n",
       "Survived  0.62963  0.472826  0.242363"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JbgGuB6iqtyS5Dng1cLR3Z+tdSe7r7Tuc5M1V9ekRfx/S0Dxz1+XmU8DrkzwPIMlzB6x5FvBfSZ7Kwpk7vbUvrKr7qmofC3c/bkzy48CJqvpjYAq4bN+FUBcXz9x1WamqY0neBfx9ku8BXwZuWbTst4D7gK8AD7IQe4D39H5gGhb+krgf2Au8Icl3WXjL2f0r/k1IHfgDVUlqkJdlJKlBxl2SGmTcJalBxl2SGmTcJalBxl2SGmTcJalB/weESrs8Sx/JzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = train.groupby(\"Pclass\")[\"Survived\"].mean()\n",
    "a.plot(kind = \"bar\");\n",
    "a.to_frame().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0.742038</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex         female      male\n",
       "Survived  0.742038  0.188908"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = train.groupby(\"Sex\")[\"Survived\"].mean()\n",
    "a.plot(kind = \"bar\");\n",
    "a.to_frame().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in fulldata:\n",
    "    i[\"FamilySize\"] = i['SibSp'] + i['Parch'] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>FamilySize</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0.303538</td>\n",
       "      <td>0.552795</td>\n",
       "      <td>0.578431</td>\n",
       "      <td>0.724138</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "FamilySize        1         2         3         4    5         6         7   \\\n",
       "Survived    0.303538  0.552795  0.578431  0.724138  0.2  0.136364  0.333333   \n",
       "\n",
       "FamilySize   8    11  \n",
       "Survived    0.0  0.0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = train.groupby(\"FamilySize\")[\"Survived\"].mean()\n",
    "a.plot(kind = \"bar\");\n",
    "a.to_frame().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in fulldata:\n",
    "    i[\"IsAlone\"] = 0\n",
    "    i.loc[i['FamilySize'] == 1, 'IsAlone'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>IsAlone</th>\n",
       "      <th>Not alone</th>\n",
       "      <th>Alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0.50565</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "IsAlone   Not alone     Alone\n",
       "Survived    0.50565  0.303538"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = train.groupby(\"IsAlone\")[\"Survived\"].mean()\n",
    "a.plot(kind = \"bar\");\n",
    "a.to_frame().T.rename(columns = {0 : \"Not alone\",1 : \"Alone\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in fulldata:\n",
    "    i['Embarked'] = i['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>Embarked</th>\n",
       "      <th>Cherbourg</th>\n",
       "      <th>Queenstown</th>\n",
       "      <th>Southampton</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0.553571</td>\n",
       "      <td>0.38961</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Embarked  Cherbourg  Queenstown  Southampton\n",
       "Survived   0.553571     0.38961     0.339009"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Txswjd61HFw7dq/uvqqrzr0PSO13yBnrn3P8B+FBV3Z/kc8AJ4BTwqaVepKrenOQxwHuAXwauAm4c+CSna6rqS/1TPR9N8hV6/yF5ysgXlMbI2w9IUoM8LSNJDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktSg/we9gFkb5zeZhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = train.groupby(\"Embarked\")[\"Survived\"].mean()\n",
    "a.plot(kind = \"bar\");\n",
    "a.to_frame().T.rename(columns = {\"C\" : \"Cherbourg\",\"Q\" : \"Queenstown\",\"S\" : \"Southampton\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}