HW2_Burcu_Koculu.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "titanic datasını okuyunuz(<b>train.csv</b>). Ödevin geri kalanında dataframe gösterimlerini <b>display()</b> fonksiyonunu kullanarak yapınız.<br> Örn: display(titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "path1 = 'C:\\\\Users\\\\Lenovo\\\\Desktop\\\\kodluyoruz\\\\Odevler\\\\train.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "path2 = 'C:\\\\Users\\\\Lenovo\\\\Desktop\\\\kodluyoruz\\\\Odevler\\\\train_missing.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_titanic_df = pd.read_csv(path2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bu alana python kodunu yazıp çıktısı görülecek şekilde çalıştırınız\n",
    "titanic_df = pd.read_csv(path1)\n",
    "display(titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Soru 1:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kazada ölenlerin yaş ortalamasını bulunuz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.62617924528302"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[titanic_df[\"Survived\"] == 0][\"Age\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kazada ölenlerin bilet fiyatlarının ortalamasını ve medyanını bulunuz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.117886885245877"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[titanic_df[\"Survived\"] == 0][\"Fare\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.5"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[titanic_df[\"Survived\"] == 0][\"Fare\"].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kazada ölen erkeklerin yaş ortalamasını bulunuz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.618055555555557"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[(titanic_df[\"Survived\"] == 0) & (titanic_df[\"Sex\"] == \"male\")].Age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kazada ölen Kadınların yaş ortalamasını bulunuz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.046875"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[(titanic_df[\"Survived\"] == 0) & (titanic_df[\"Sex\"] == \"female\")].Age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Soru 2:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kazadan kurtulanların yaş ortalamasını bulunuz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.343689655172415"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[(titanic_df[\"Survived\"]== 1)].Age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kazadan kurtulanların bilet fiyatlarının ortalamasını bulunuz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.39540760233917"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[(titanic_df[\"Survived\"]== 1)].Fare.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kazadan kurtulan toplam kişi sayısını bulunuz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(titanic_df[\"Survived\"]== 1).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kazadan kurtulan erkeklerin yaş ortalamasını bulunuz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.276021505376345"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[(titanic_df[\"Survived\"]== 1) & (titanic_df[\"Sex\"] == \"male\")].Age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kazadan kurtulan kadınların yaş ortalamasını bulunuz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Soru 3: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10 yaşından küçüklerin bilet fiyatlarının medyan değerini bulunuz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.0"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[(titanic_df[\"Age\"] < 10)].Fare.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10 yaşından büyüklerin bilet fiyatlarının medyan değerini bulunuz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.92915"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df[(titanic_df[\"Age\"] > 10)].Fare.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Soru 5:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.sınıf, 2.sınıf ve 3.sınıf yolcuların ölüm oranlarını karşılaştırınız. Bir veya bir kaç cümle ile sonucu <b>yorumlayınız.</b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37037037037037035"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_1 = titanic_df[(titanic_df[\"Pclass\"] == 1)].index\n",
    "total_pclass1 = len(total_1)\n",
    "dead_1 = titanic_df[(titanic_df[\"Pclass\"] == 1) & (titanic_df[\"Survived\"] == 0)].index\n",
    "dead_pclass1 = len(dead_1)\n",
    "\n",
    "rate_pclass1 = dead_pclass1 / total_pclass1\n",
    "rate_pclass1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5271739130434783"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_2 = titanic_df[(titanic_df[\"Pclass\"] == 2)].index\n",
    "total_pclass2 = len(total_2)\n",
    "dead_2 = titanic_df[(titanic_df[\"Pclass\"] == 2) & (titanic_df[\"Survived\"] == 0)].index\n",
    "dead_pclass2 = len(dead_2)\n",
    "\n",
    "rate_pclass2 = dead_pclass2 / total_pclass2\n",
    "rate_pclass2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7576374745417516"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_3 = titanic_df[(titanic_df[\"Pclass\"] == 3)].index\n",
    "total_pclass3 = len(total_3)\n",
    "dead_3 = titanic_df[(titanic_df[\"Pclass\"] == 3) & (titanic_df[\"Survived\"] == 0)].index\n",
    "dead_pclass3 = len(dead_3)\n",
    "\n",
    "rate_pclass3 = dead_pclass3 / total_pclass3\n",
    "rate_pclass3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3. sınıf yolcularının ölüm oranı en yüksek olup, sırayla 2 ve 1. sınıfa doğru ölüm oranı azalmaktadır.\n"
     ]
    }
   ],
   "source": [
    "print(\"3. sınıf yolcularının ölüm oranı en yüksek olup, sırayla 2 ve 1. sınıfa doğru ölüm oranı azalmaktadır.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.sınıf, 2.sınıf ve 3.sınıf bilet fiyatlarını karşılaştırınız"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ortalama Fiyatlar \n",
      "1. sınıf bilet fiyatı: 84.15468749999992 \n",
      "2. sınıf bilet fiyatı: 20.66218315217391 \n",
      "3. sınıf bilet fiyatı: 13.675550101832997\n"
     ]
    }
   ],
   "source": [
    "fare_1 = titanic_df[(titanic_df[\"Pclass\"] == 1)][\"Fare\"].mean()\n",
    "fare_2 = titanic_df[(titanic_df[\"Pclass\"] == 2)][\"Fare\"].mean()\n",
    "fare_3 = titanic_df[(titanic_df[\"Pclass\"] == 3)][\"Fare\"].mean()\n",
    "\n",
    "print(f\"Ortalama Fiyatlar \\n1. sınıf bilet fiyatı: {fare_1} \\n2. sınıf bilet fiyatı: {fare_2} \\n3. sınıf bilet fiyatı: {fare_3}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. sınıf yolcularının bilet fiyatı ortalaması en yüksek olup, sırayla 2 ve 3. sınıfa doğru fiyat ortalaması azalmaktadır.\n"
     ]
    }
   ],
   "source": [
    "print(\"1. sınıf yolcularının bilet fiyatı ortalaması en yüksek olup, sırayla 2 ve 3. sınıfa doğru fiyat ortalaması azalmaktadır.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Soru 6:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kazada ölen kadınların oranı ile erkeklerin oranını karşılaştırınız. Bir veya bir kaç cümle ile sonucu <b>yorumlayınız.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25796178343949044"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_w = titanic_df[(titanic_df[\"Sex\"] == \"female\")].index\n",
    "all_women = len(all_w)\n",
    "dead_w =  titanic_df[(titanic_df[\"Sex\"] == \"female\") & (titanic_df[\"Survived\"]== 0)].index\n",
    "dead_women = len(dead_w)\n",
    "\n",
    "woman_death_rate = dead_women / all_women\n",
    "\n",
    "woman_death_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8110918544194108"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_m = titanic_df[(titanic_df[\"Sex\"] == \"male\")].index\n",
    "all_men = len(all_m)\n",
    "dead_m =  titanic_df[(titanic_df[\"Sex\"] == \"male\") & (titanic_df[\"Survived\"]== 0)].index\n",
    "dead_men = len(dead_m)\n",
    "\n",
    "man_death_rate = dead_men / all_men\n",
    "man_death_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Erkeklerin ölüm oranı, kadınların ölüm oranından % 55.31300709799203 daha fazladır\n"
     ]
    }
   ],
   "source": [
    "print(\"Erkeklerin ölüm oranı, kadınların ölüm oranından %\", (man_death_rate - woman_death_rate)*100 , \"daha fazladır\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Soru 7:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kazada ölen erkeklerin ortalama bilet fiyatı ile kurtulan erkeklerin ortalama bilet fiyatını karşılaştırınız. Bir veya bir kaç cümle ile sonucu <b>yorumlayınız.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ölen erkeklerin ortalama bilet fiyatı: 21.960992948717944 \n",
      "Kurtulan erkeklerin ortalama bilet fiyatı: 40.82148440366974\n"
     ]
    }
   ],
   "source": [
    "dead_man_fare = titanic_df[(titanic_df[\"Survived\"]== 0) & (titanic_df[\"Sex\"] == \"male\")].Fare.mean()\n",
    "alive_man_fare = titanic_df[(titanic_df[\"Survived\"]== 1) & (titanic_df[\"Sex\"] == \"male\")].Fare.mean()\n",
    "\n",
    "print(f\"Ölen erkeklerin ortalama bilet fiyatı: {dead_man_fare} \\nKurtulan erkeklerin ortalama bilet fiyatı: {alive_man_fare}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kurtulan erkeklerin ort bilet fiyatı, ölenlerinkinden daha fazladır. \n"
     ]
    }
   ],
   "source": [
    "print(\"Kurtulan erkeklerin ort bilet fiyatı, ölenlerinkinden daha fazladır. \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Value Doldurma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bundan sonraki soruları <b>train_missing.csv</b> datasını kullanarak cevaplayınız."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Soru 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age, Embarked ve Fare değişkenlerini mean/median/mode değerlerini kullanarak değişken tipine uygun olacak şekilde doldurunuz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp          170\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare            91\n",
       "Cabin          687\n",
       "Embarked       173\n",
       "dtype: int64"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_df = m_titanic_df.copy()\n",
    "missing_df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_mean = SimpleImputer(strategy='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age     0\n",
       "Fare    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_mean.fit(missing_df[[\"Age\",\"Fare\"]])\n",
    "missing_df[[\"Age\",\"Fare\"]] = imp_mean.transform(missing_df[[\"Age\",\"Fare\"]])\n",
    "missing_df[[\"Age\",\"Fare\"]].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_df[\"Embarked\"].fillna(value=missing_df[\"Embarked\"].mode()[0], inplace=True)\n",
    "missing_df[\"Embarked\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Soru 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SibSp değişkeni hangi yöntemler ile doldurulabilir, fikir yürütün. Uygun bir yöntem ile doldurun. (SibSp değişkeni kişilerin gemide bulunan kardeş/eş sayısını belirtiyor.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ortalama: 0.5353675450762829 \n",
      "Mod: 0    0.0\n",
      "dtype: float64 \n"
     ]
    }
   ],
   "source": [
    "# missing_df.iloc[:,0:7].head(50) , data inceleme\n",
    "# missing_df.iloc[:,6:9].head(50)\n",
    "# missing_df.iloc[:,6:9].tail(50)\n",
    "\n",
    "sib_mean = missing_df[\"SibSp\"].mean()\n",
    "sib_mode = missing_df[\"SibSp\"].mode()\n",
    "print(f\"Ortalama: {sib_mean} \\nMod: {sib_mode} \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatrix = missing_df.corr()\n",
    "sn.heatmap(corrMatrix, annot=True)\n",
    "plt.show()\n",
    "#korelasyona bakıldı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#missing value, mod ile dolduruldu\n",
    "missing_df[\"SibSp\"].fillna(value=missing_df[\"SibSp\"].mode()[0], inplace=True)\n",
    "missing_df[\"SibSp\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Soru 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Datayı baştan okuyun. Embarked değişkenini \"NAN\" şeklinde yeni bir kategorisi olacak şekilde doldurun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "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>1.0</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>1.0</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1.0</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>0.0</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>0.0</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>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>NaN</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>NaN</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>0.0</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    1.0   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0    0.0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   \n",
       "4                             Allen, Mr. William Henry    male  35.0    0.0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0    0.0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0    0.0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN    1.0   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0    0.0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0    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      NaN  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN      NaN  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607      NaN   NaN        S  \n",
       "889      0            111369  30.0000  C148      NaN  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_data = pd.read_csv(path2)\n",
    "display(missing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "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>1.0</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>1.0</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NAN</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>1.0</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NAN</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>0.0</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>0.0</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>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>NaN</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>NAN</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>0.0</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    1.0   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0    0.0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   \n",
       "4                             Allen, Mr. William Henry    male  35.0    0.0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0    0.0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0    0.0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN    1.0   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0    0.0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0    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      NAN  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN      NAN  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607      NaN   NaN        S  \n",
       "889      0            111369  30.0000  C148      NAN  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data[\"Embarked\"].fillna(value=\"NAN\",inplace=True)\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Soru 11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Fare\" değişkeni için \"Fare_Missing\" adında yeni bir değişken türetip \"Fare\" değişkenini median ile doldurun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 11,  12,  17,  35,  40,  45,  67,  69,  70,  97, 105, 113, 129,\n",
       "            162, 166, 175, 181, 182, 193, 206, 210, 215, 229, 255, 264, 267,\n",
       "            283, 296, 301, 308, 324, 332, 354, 390, 396, 404, 413, 417, 427,\n",
       "            430, 436, 437, 438, 440, 443, 456, 463, 490, 494, 504, 533, 562,\n",
       "            589, 596, 599, 608, 614, 617, 618, 627, 654, 659, 676, 679, 681,\n",
       "            684, 690, 695, 721, 722, 729, 748, 752, 757, 763, 773, 781, 783,\n",
       "            790, 794, 798, 806, 808, 822, 823, 825, 841, 863, 872, 885, 888],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na_indices = missing_data[missing_data[\"Fare\"].isnull()].index\n",
    "na_indices #kayıp olan fare değerleri indisleri tutuldu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "fare_median = missing_data.Fare.median()\n",
    "missing_data.Fare.fillna(value=fare_median,inplace=True) #nan değerler yerine median dolduruldu\n",
    "missing_data.insert(10,\"Fare_Missing\",value=0) #Fare_missing değişkeni türetildi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.45625"
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      ],
      "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    1.0   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0    0.0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   \n",
       "4                             Allen, Mr. William Henry    male  35.0    0.0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0    0.0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0    0.0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN    1.0   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0    0.0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0    0.0   \n",
       "\n",
       "     Parch            Ticket      Fare  Fare_Missing Cabin Embarked  \n",
       "0        0         A/5 21171   7.25000             0   NaN        S  \n",
       "1        0          PC 17599  71.28330             0   C85        C  \n",
       "2        0  STON/O2. 3101282   7.92500             0   NaN      NAN  \n",
       "3        0            113803  53.10000             0  C123        S  \n",
       "4        0            373450   8.05000             0   NaN      NAN  \n",
       "..     ...               ...       ...           ...   ...      ...  \n",
       "886      0            211536  13.00000             0   NaN        S  \n",
       "887      0            112053  30.00000             0   B42        S  \n",
       "888      2        W./C. 6607  14.45625             0   NaN        S  \n",
       "889      0            111369  30.00000             0  C148      NAN  \n",
       "890      0            370376   7.75000             0   NaN        Q  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(fare_median)\n",
    "display(missing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_data.loc[na_indices, \"Fare_Missing\"] = 1 #NaN olan indislerdeki Fare_Missing değeri 1 yapıldı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Behr, Mr. Karl Howell</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
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       "      <td>370376</td>\n",
       "      <td>7.75000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 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    1.0   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0    1.0   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0    0.0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0    1.0   \n",
       "4                             Allen, Mr. William Henry    male  35.0    0.0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0    0.0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0    0.0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN    1.0   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0    0.0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0    0.0   \n",
       "\n",
       "     Parch            Ticket      Fare  Fare_Missing Cabin Embarked  \n",
       "0        0         A/5 21171   7.25000             0   NaN        S  \n",
       "1        0          PC 17599  71.28330             0   C85        C  \n",
       "2        0  STON/O2. 3101282   7.92500             0   NaN      NAN  \n",
       "3        0            113803  53.10000             0  C123        S  \n",
       "4        0            373450   8.05000             0   NaN      NAN  \n",
       "..     ...               ...       ...           ...   ...      ...  \n",
       "886      0            211536  13.00000             0   NaN        S  \n",
       "887      0            112053  30.00000             0   B42        S  \n",
       "888      2        W./C. 6607  14.45625             1   NaN        S  \n",
       "889      0            111369  30.00000             0  C148      NAN  \n",
       "890      0            370376   7.75000             0   NaN        Q  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(missing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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