{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA Danych dotyczących Titanica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## O Danych\n",
    "\n",
    "Dane o pasażerach Titanica\n",
    "\n",
    "Zbiór danych zawiera informacje o pasażerach RMS Titanic, który zatonął 15 kwietnia 1912 roku po zderzeniu z górą\n",
    "lodową. Dane obejmują takie atrybuty jak klasa podróży, wiek, płeć, liczba rodzeństwa/małżonków na pokładzie,\n",
    "liczba rodziców/dzieci na pokładzie, cena biletu oraz miejsce zaokrętowania.\n",
    "\n",
    "Zbiór zawiera także informację o tym, czy pasażer przeżył katastrofę.\n",
    "\n",
    "Titanic przewoził ponad 2,200 osób, z czego ponad 1,500 zginęło, co czyni tę katastrofę jedną z najbardziej\n",
    "tragicznych w historii morskiej.\n",
    "\n",
    "Kolumny:\n",
    "\n",
    "* **pclass** - Klasa biletu\n",
    "* **survived** - Czy pasażer przeżył katastrofę\n",
    "* **name** - Imię i nazwisko pasażera\n",
    "* **sex** - Płeć pasażera\n",
    "* **age** - Wiek pasażera\n",
    "* **sibsp** - Liczba rodzeństwa/małżonków na pokładzie\n",
    "* **parch** - Liczba rodziców/dzieci na pokładzie\n",
    "* **ticket** - Numer biletu\n",
    "* **fare** - Cena biletu\n",
    "* **cabin** - Numer kabiny\n",
    "* **embarked** - Port, w którym pasażer wszedł na pokład (C = Cherbourg, Q = Queenstown, S = Southampton)\n",
    "* **boat** - Numer łodzi ratunkowej\n",
    "* **body** - Numer ciała (jeśli pasażer nie przeżył i ciało zostało odnalezione)\n",
    "* **home.dest** - Miejsce docelowe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Ogólny przegląd danych i analiza brakujących wartości"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>pclass</th>\n",
       "      <th>survived</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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Zabour, Miss. Thamine</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Zakarian, Mr. Mapriededer</td>\n",
       "      <td>male</td>\n",
       "      <td>26.5000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2656</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>304.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Zakarian, Mr. Ortin</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2670</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Zimmerman, Mr. Leo</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>315082</td>\n",
       "      <td>7.8750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1310 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass  survived                                             name  \\\n",
       "0        1.0       1.0                    Allen, Miss. Elisabeth Walton   \n",
       "1        1.0       1.0                   Allison, Master. Hudson Trevor   \n",
       "2        1.0       0.0                     Allison, Miss. Helen Loraine   \n",
       "3        1.0       0.0             Allison, Mr. Hudson Joshua Creighton   \n",
       "4        1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
       "...      ...       ...                                              ...   \n",
       "1305     3.0       0.0                            Zabour, Miss. Thamine   \n",
       "1306     3.0       0.0                        Zakarian, Mr. Mapriededer   \n",
       "1307     3.0       0.0                              Zakarian, Mr. Ortin   \n",
       "1308     3.0       0.0                               Zimmerman, Mr. Leo   \n",
       "1309     NaN       NaN                                              NaN   \n",
       "\n",
       "         sex      age  sibsp  parch  ticket      fare    cabin embarked boat  \\\n",
       "0     female  29.0000    0.0    0.0   24160  211.3375       B5        S    2   \n",
       "1       male   0.9167    1.0    2.0  113781  151.5500  C22 C26        S   11   \n",
       "2     female   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN   \n",
       "3       male  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN   \n",
       "4     female  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN   \n",
       "...      ...      ...    ...    ...     ...       ...      ...      ...  ...   \n",
       "1305  female      NaN    1.0    0.0    2665   14.4542      NaN        C  NaN   \n",
       "1306    male  26.5000    0.0    0.0    2656    7.2250      NaN        C  NaN   \n",
       "1307    male  27.0000    0.0    0.0    2670    7.2250      NaN        C  NaN   \n",
       "1308    male  29.0000    0.0    0.0  315082    7.8750      NaN        S  NaN   \n",
       "1309     NaN      NaN    NaN    NaN     NaN       NaN      NaN      NaN  NaN   \n",
       "\n",
       "       body                        home.dest  \n",
       "0       NaN                     St Louis, MO  \n",
       "1       NaN  Montreal, PQ / Chesterville, ON  \n",
       "2       NaN  Montreal, PQ / Chesterville, ON  \n",
       "3     135.0  Montreal, PQ / Chesterville, ON  \n",
       "4       NaN  Montreal, PQ / Chesterville, ON  \n",
       "...     ...                              ...  \n",
       "1305    NaN                              NaN  \n",
       "1306  304.0                              NaN  \n",
       "1307    NaN                              NaN  \n",
       "1308    NaN                              NaN  \n",
       "1309    NaN                              NaN  \n",
       "\n",
       "[1310 rows x 14 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('26__titanic.csv', sep=\",\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1310 entries, 0 to 1309\n",
      "Data columns (total 14 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   pclass     1309 non-null   float64\n",
      " 1   survived   1309 non-null   float64\n",
      " 2   name       1309 non-null   object \n",
      " 3   sex        1309 non-null   object \n",
      " 4   age        1046 non-null   float64\n",
      " 5   sibsp      1309 non-null   float64\n",
      " 6   parch      1309 non-null   float64\n",
      " 7   ticket     1309 non-null   object \n",
      " 8   fare       1308 non-null   float64\n",
      " 9   cabin      295 non-null    object \n",
      " 10  embarked   1307 non-null   object \n",
      " 11  boat       486 non-null    object \n",
      " 12  body       121 non-null    float64\n",
      " 13  home.dest  745 non-null    object \n",
      "dtypes: float64(7), object(7)\n",
      "memory usage: 143.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Informacje o dataframe\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liczba unikalnych wartości w każdej kolumnie:\n",
      "pclass          3\n",
      "survived        2\n",
      "name         1307\n",
      "sex             2\n",
      "age            98\n",
      "sibsp           7\n",
      "parch           8\n",
      "ticket        929\n",
      "fare          281\n",
      "cabin         186\n",
      "embarked        3\n",
      "boat           27\n",
      "body          121\n",
      "home.dest     369\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Liczba unikalnych wartości w każdej kolumnie\n",
    "unique_values = df.nunique()\n",
    "print(\"Liczba unikalnych wartości w każdej kolumnie:\")\n",
    "print(unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liczba brakujących wartości w każdej kolumnie:\n",
      "pclass          1\n",
      "survived        1\n",
      "name            1\n",
      "sex             1\n",
      "age           264\n",
      "sibsp           1\n",
      "parch           1\n",
      "ticket          1\n",
      "fare            2\n",
      "cabin        1015\n",
      "embarked        3\n",
      "boat          824\n",
      "body         1189\n",
      "home.dest     565\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Liczba brakujących wartości w każdej kolumnie\n",
    "missing_values = df.isnull().sum()\n",
    "print(\"Liczba brakujących wartości w każdej kolumnie:\")\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liczba brakujących wartości w każdej kolumnie po usunięciu niektórych kolumn:\n",
      "pclass        1\n",
      "survived      1\n",
      "name          1\n",
      "sex           1\n",
      "age         264\n",
      "sibsp         1\n",
      "parch         1\n",
      "fare          2\n",
      "embarked      3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Usunięcie kolumn 'ticket', 'cabin', 'boat', 'body' i 'home.dest' z dataframe\n",
    "df.drop(columns=['ticket', 'cabin', 'boat', 'body', 'home.dest'], inplace=True)\n",
    "\n",
    "# Ponowne sprawdzenie brakujących wartości w każdej kolumnie\n",
    "missing_values = df.isnull().sum()\n",
    "print(\"Liczba brakujących wartości w każdej kolumnie po usunięciu niektórych kolumn:\")\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liczba brakujących wartości w każdej kolumnie po uzupełnieniu:\n",
      "pclass      0\n",
      "survived    0\n",
      "name        0\n",
      "sex         0\n",
      "age         0\n",
      "sibsp       0\n",
      "parch       0\n",
      "fare        0\n",
      "embarked    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Uzupełnienie brakujących wartości w kolumnie `age` medianą wieku\n",
    "df['age'].fillna(df['age'].median(), inplace=True)\n",
    "\n",
    "# Uzupełnienie brakujących wartości w kolumnie `fare` medianą ceny biletu\n",
    "df['fare'].fillna(df['fare'].median(), inplace=True)\n",
    "\n",
    "# Uzupełnienie brakujących wartości w kolumnie `embarked` najczęstszą wartością\n",
    "df['embarked'].fillna(df['embarked'].mode()[0], inplace=True)\n",
    "\n",
    "# Uzupełnienie brakujących wartości w kolumnie `pclass` najczęstszą wartością\n",
    "df['pclass'].fillna(df['pclass'].mode()[0], inplace=True)\n",
    "\n",
    "# Uzupełnienie brakujących wartości w kolumnie `survived` najczęstszą wartością\n",
    "df['survived'].fillna(df['survived'].mode()[0], inplace=True)\n",
    "\n",
    "# Uzupełnienie brakujących wartości w kolumnie `sex` najczęstszą wartością\n",
    "df['sex'].fillna(df['sex'].mode()[0], inplace=True)\n",
    "\n",
    "# Uzupełnienie brakujących wartości w kolumnie `sibsp` najczęstszą wartością\n",
    "df['sibsp'].fillna(df['sibsp'].mode()[0], inplace=True)\n",
    "\n",
    "# Uzupełnienie brakujących wartości w kolumnie `parch` najczęstszą wartością\n",
    "df['parch'].fillna(df['parch'].mode()[0], inplace=True)\n",
    "\n",
    "# Zastąpienie brakujących wartości w kolumnie `name` wartością 'Unknown'\n",
    "df['name'].fillna('Unknown', inplace=True)\n",
    "\n",
    "# Ponowne sprawdzenie brakujących wartości w każdej kolumnie\n",
    "missing_values = df.isnull().sum()\n",
    "print(\"Liczba brakujących wartości w każdej kolumnie po uzupełnieniu:\")\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Połączenie kolumn `sibsp` i `parch` w nową kolumnę `family_size`\n",
    "df['family_size'] = df['sibsp'] + df['parch']\n",
    "\n",
    "# Usunięcie kolumn `sibsp` i `parch`\n",
    "df.drop(columns=['sibsp', 'parch'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated 'sex' column values:\n",
      " [1 0]\n",
      "Updated 'embarked' column values:\n",
      " [2 0 1]\n",
      "Sex mapping: {'male': 0, 'female': 1}\n",
      "Embarked mapping: {'C': 0, 'Q': 1, 'S': 2}\n"
     ]
    }
   ],
   "source": [
    "# Mapowanie dla kolumny `sex`\n",
    "sex_mapping = {'male': 0, 'female': 1}\n",
    "df['sex'] = df['sex'].map(sex_mapping)\n",
    "\n",
    "# Sprawdzenie unikalnych wartości w kolumnie `embarked`\n",
    "unique_embarked = df['embarked'].dropna().unique()\n",
    "unique_embarked_sorted = sorted(unique_embarked)  # Sortowanie dla spójności\n",
    "\n",
    "# Mapowanie dla kolumny `embarked`\n",
    "embarked_mapping = {port: idx for idx, port in enumerate(unique_embarked_sorted)}\n",
    "df['embarked'] = df['embarked'].map(embarked_mapping)\n",
    "\n",
    "# Wyświetlenie zaktualizowanych wartości w kolumnach `sex` i `embarked`\n",
    "print(\"Updated 'sex' column values:\\n\", df['sex'].unique())\n",
    "print(\"Updated 'embarked' column values:\\n\", df['embarked'].unique())\n",
    "\n",
    "# Wyświetlenie mapowania\n",
    "print(\"Sex mapping:\", sex_mapping)\n",
    "print(\"Embarked mapping:\", embarked_mapping)\n",
    "\n",
    "# Stworzenie kopii ramki danych do PCA\n",
    "df_pca = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Podsumowanie kolumn numerycznych:\n",
      "            pclass     survived          sex          age         fare  \\\n",
      "count  1310.000000  1310.000000  1310.000000  1310.000000  1310.000000   \n",
      "mean      2.295420     0.381679     0.355725    29.502036    33.266714   \n",
      "std       0.837742     0.485984     0.478915    12.900383    51.724348   \n",
      "min       1.000000     0.000000     0.000000     0.166700     0.000000   \n",
      "25%       2.000000     0.000000     0.000000    22.000000     7.895800   \n",
      "50%       3.000000     0.000000     0.000000    28.000000    14.454200   \n",
      "75%       3.000000     1.000000     1.000000    35.000000    31.275000   \n",
      "max       3.000000     1.000000     1.000000    80.000000   512.329200   \n",
      "\n",
      "          embarked  family_size  \n",
      "count  1310.000000  1310.000000  \n",
      "mean      1.493893     0.883206  \n",
      "std       0.814053     1.583222  \n",
      "min       0.000000     0.000000  \n",
      "25%       1.000000     0.000000  \n",
      "50%       2.000000     0.000000  \n",
      "75%       2.000000     1.000000  \n",
      "max       2.000000    10.000000  \n"
     ]
    }
   ],
   "source": [
    "# Podsumowanie kolumn numerycznych\n",
    "numerical_summary = df.describe()\n",
    "\n",
    "# Wyświetlenie wyników\n",
    "print(\"Podsumowanie kolumn numerycznych:\")\n",
    "print(numerical_summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Podsumowanie:\n",
    "\n",
    "- Po siedem kolumn zawiera dane numeryczne i tekstowe.\n",
    "- Najwięcej unikatowych wartości ma kolumna 'name' (1307) a najmniej kolumna 'sex' i 'embarked' (odpowiednio 2 i 3).\n",
    "- Unikatowe wartości wskazują, że po katastrofie znaleziono tylko 121 ciał.\n",
    "- Kolumny 'cabin', 'boat', 'body' i 'home.dest' mają najwięcej brakujących wartości i nie wniosą wielu informacji do analizy, dlatego zdecydowano się na ich usunięcie.\n",
    "- Kolumna 'ticket' nie wnosi istotnych informacji do analizy, więc została usunięta.\n",
    "- Brakujące wartości w kolumnie numerycznej 'age' i 'fare' zastąpiono medianą, ponieważ rozkłady wartości są skośne.\n",
    "- Brakujące wartości w kolumnie 'embarked', 'pclass', 'survived', 'sex', 'sibsp', 'parch' zastąpiono najczęściej występują wartością.\n",
    "- Brakującą wartość w kolumnie 'name' zastąpiono wartością 'Unknown'.\n",
    "- W celu łatwiejszej analizy utworzono nową zmienną 'family_size', która powstała poprzez dodanie wartości z kolumny 'sibsp' i 'parch'.\n",
    "- W celu łatwiejszej analizy wartości tekstowe w kolumnie 'sex' zastąpiono liczbowymi: 'male': 0, 'female': 1. To samo wykonano dla kolumny 'embarked': 'C': 0, 'Q': 1, 'S': 2.\n",
    "- Już na początkowym etapie analizy za pomocą podsumowania kolumn numerycznych można stwierdzić, że większość pasażerów była mężczyznami, wsiadła na statek w porcie Southampton, podróżowała 3 klasą, nie przeżyła katastrofy, była w wieku około 22-35 lat, podróżowała samotnie lub z jednym członkiem rodziny, a za bilet zaplaciła około 7-32 jednostek walutowych.\n",
    "\n",
    "**Uwaga:** Należy zwrócić szczególną uwagę na wartości w kolumnie 'age', ponieważ w tej kolumnie było najwięcej brakujących wartości (264), które zostały uzupełnione medianą. Może to wpływać na dokładność analiz związanych z tą zmienną. Jednakże, kolumna ta jest na tyle ważna z punktu widzenia analiz, że zdecydowano się na jej pozostawienie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Analiza pojedynczych zmiennych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Utworzenie wykresu\n",
    "plt.figure(figsize=(20, 6))\n",
    "\n",
    "# Histogram wieku\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.histplot(df['age'], bins=30, kde=True)\n",
    "plt.title('Rozkład wieku pasażerów')\n",
    "plt.xlabel('Wiek')\n",
    "plt.ylabel('Liczba pasażerów')\n",
    "\n",
    "# Histogram ceny biletu\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.histplot(df['fare'], bins=30, kde=True)\n",
    "plt.title('Rozkład ceny biletu')\n",
    "plt.xlabel('Cena biletu')\n",
    "plt.ylabel('Liczba pasażerów')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Podsumowanie:\n",
    "\n",
    "- Histogram wieku pokazuje, że największa liczba pasażerow byla w wieku młodym do średniej dorosłości, z wyraźnym pikiem około 28 lat.\n",
    "- Histogram cen biletów pokazuje, że większość biletów kosztowała poniżej 50 jednostek walutowych. Występuje kilka wartości znacznie przekraczających 200 jednostek walutowych.\n",
    "\n",
    "**Uwaga:** Wyrażny pik na histogramie wieku w okolicach 28 roku życia, spowodowany jest zastąpieniem brakujących 264 wartości wieku w danych medianą o tej wartości. Mimo to nie przeszkadza to w sformułowaniu wniosku, że największa liczba pasażerów była w wieku młodym do średniej dorosłości (około 20-35 lat). Problem ten nie występuje przy analizie cen biletów- zastąpiono tylko dwie brakujące wartości."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ustalenie palety kolorów\n",
    "palette = sns.color_palette(\"Blues\", 6)\n",
    "\n",
    "# Utworzenie wykresów\n",
    "plt.figure(figsize=(20, 12))\n",
    "\n",
    "# Countplot klasy\n",
    "plt.subplot(2, 3, 1)\n",
    "sns.countplot(data=df, x='pclass', palette=palette)\n",
    "plt.title('Liczba pasażerów w każdej klasie')\n",
    "plt.xlabel('Klasa')\n",
    "plt.ylabel('Liczba pasażerów')\n",
    "\n",
    "# Countplot płci\n",
    "plt.subplot(2, 3, 2)\n",
    "sns.countplot(data=df, x='sex', palette=palette)\n",
    "plt.title('Liczba pasażerów według płci')\n",
    "plt.xlabel('Płeć')\n",
    "plt.ylabel('Liczba pasażerów')\n",
    "\n",
    "# Countplot portu zaokrętowania\n",
    "plt.subplot(2, 3, 3)\n",
    "sns.countplot(data=df, x='embarked', palette=palette)\n",
    "plt.title('Liczba pasażerów według portu zaokrętowania')\n",
    "plt.xlabel('Port zaokrętowania')\n",
    "plt.ylabel('Liczba pasażerów')\n",
    "\n",
    "# Countplot liczby członków rodziny\n",
    "plt.subplot(2, 3, 4)\n",
    "sns.countplot(data=df, x='family_size', palette=palette)\n",
    "plt.title('Liczba pasażerów według liczby członków rodziny')\n",
    "plt.xlabel('Liczba członków rodziny')\n",
    "plt.ylabel('Liczba pasażerów')\n",
    "\n",
    "# Countplot przeżywalności\n",
    "plt.subplot(2, 3, 5)\n",
    "sns.countplot(data=df, x='survived', palette=palette)\n",
    "plt.title('Liczba pasażerów, którzy nie przeżyli/przeżyli')\n",
    "plt.xlabel('Przeżycie')\n",
    "plt.ylabel('Liczba pasażerów')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Podsumowanie:\n",
    "\n",
    "- Najwięcej pasażerów podróżowało w trzeciej klasie, następnie w pierwszej, a najmniej w drugiej.\n",
    "- Więcej pasażerów było płci męskiej.\n",
    "- Najwięcej pasażerów zaokrętowało w Southampton, następnie w Cherbourg, a najmniej w Queenstown.\n",
    "- Większość pasażerów podróżowała bez bliskich krewnych.\n",
    "- Większość pasażerów nie przeżyła katastrofy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Grupowanie i agregacja danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agregacja danych ze względu na klasę:\n",
      "   pclass  wiek_mean   wiek_std  plec_mean  plec_std  \\\n",
      "0     1.0  37.812436  14.116481   0.445820  0.497827   \n",
      "1     2.0  29.419675  13.242086   0.382671  0.486919   \n",
      "2     3.0  25.753521  10.146602   0.304225  0.460403   \n",
      "\n",
      "   liczba_czlonkow_rodziny_mean  liczba_czlonkow_rodziny_std  \\\n",
      "0                      0.801858                     1.062224   \n",
      "1                      0.761733                     1.029060   \n",
      "2                      0.967606                     1.920219   \n",
      "\n",
      "   cena_biletu_mean  cena_biletu_std  port_mean  port_std  przezycie_mean  \\\n",
      "0         87.508992        80.447178   1.117647  0.989902        0.619195   \n",
      "1         21.179196        13.607122   1.772563  0.615828        0.429603   \n",
      "2         13.306132        11.478297   1.556338  0.729440        0.254930   \n",
      "\n",
      "   przezycie_std  \n",
      "0       0.486338  \n",
      "1       0.495915  \n",
      "2       0.436129  \n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Funkcja do agregacji\n",
    "def aggregate_by_class(df):\n",
    "    # Grupowanie przez `pclass`\n",
    "    aggregated_data = df.groupby('pclass').agg({\n",
    "        'age': ['mean', 'std'],\n",
    "        'sex': ['mean', 'std'],\n",
    "        'family_size': ['mean', 'std'],\n",
    "        'fare': ['mean', 'std'],\n",
    "        'embarked': ['mean', 'std'],\n",
    "        'survived': ['mean', 'std']\n",
    "    }).reset_index()\n",
    "\n",
    "    # Zmiana nazw kolumn\n",
    "    aggregated_data.columns = ['pclass', 'wiek_mean', 'wiek_std', 'plec_mean', 'plec_std',\n",
    "                               'liczba_czlonkow_rodziny_mean', 'liczba_czlonkow_rodziny_std',\n",
    "                               'cena_biletu_mean', 'cena_biletu_std',\n",
    "                               'port_mean', 'port_std', 'przezycie_mean', 'przezycie_std']\n",
    "    return aggregated_data\n",
    "\n",
    "# Agregacja danych\n",
    "aggregated_data = aggregate_by_class(df)\n",
    "\n",
    "# Wyświetlenie wyników agregacji\n",
    "print(\"Agregacja danych ze względu na klasę:\")\n",
    "print(aggregated_data)\n",
    "\n",
    "# Lista cech do analizy\n",
    "features = ['wiek', 'plec', 'liczba_czlonkow_rodziny', 'cena_biletu', 'port', 'przezycie']\n",
    "\n",
    "# Liczba wierszy i kolumn dla wykresów\n",
    "n_cols = 3\n",
    "n_rows = int(np.ceil(len(features) / n_cols))\n",
    "\n",
    "# Tworzenie wykresów średnich wartości z odchyleniami standardowymi\n",
    "fig, axes = plt.subplots(n_rows, n_cols, figsize=(20, n_rows * 5))\n",
    "\n",
    "for i, feature in enumerate(features):\n",
    "    row = i // n_cols\n",
    "    col = i % n_cols\n",
    "    ax = axes[row, col]\n",
    "    sns.barplot(data=aggregated_data, x='pclass', y=f'{feature}_mean',\n",
    "                yerr=aggregated_data[f'{feature}_std'], ax=ax, palette='viridis')\n",
    "    ax.set_title(f'Średnia wartość {feature} z odchyleniem standardowym według klasy')\n",
    "    ax.set_xlabel('Klasa')\n",
    "    ax.set_ylabel(f'Średnia wartość {feature}')\n",
    "    ax.grid(False)  # Usunięcie siatki\n",
    "\n",
    "# Usuwanie pustych wykresów\n",
    "for j in range(i + 1, n_rows * n_cols):\n",
    "    fig.delaxes(axes.flatten()[j])\n",
    "\n",
    "# Ustawienie odstępów między wykresami\n",
    "plt.tight_layout()\n",
    "\n",
    "# Wyświetlenie wykresów\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Podsumowanie:\n",
    "\n",
    "- **Średnia wartość wieku**- wiek pasażerów klasy 1 jest wyraźnie wyższy w porównaniu do pasażerów klasy 2 i 3. Odchylenie standardowe jest największe w klasie 1, co sugeruje większą różnorodność wiekową pasażerów tej klasy.\n",
    "- **Średnia wartość płci**- proporcje płci (zakodowane jako 0 dla mężczyzn i 1 dla kobiet) są dość podobne we wszystkich klasach, ale w klasie 1 jest nieco wyższy udział kobiet. Odchylenie standardowe jest duże we wszystkich klasach, co sugeruje znaczne zróżnicowanie płciowe w każdej z klas.\n",
    "- **Średnia wartość liczby członków rodziny**- liczba członków rodziny jest stosunkowo niska we wszystkich klasach, z niewielką różnicą między klasami. Odchylenie standardowe jest duże, co wskazuje na dużą zmienność w liczbie członków rodziny w każdej z klas.\n",
    "- **Średnia wartość ceny biletu**- ceny biletów są najwyższe w klasie 1 i znacząco niższe w klasach 2 i 3. Odchylenie standardowe ceny biletu jest największe w klasie 1, co sugeruje dużą różnorodność cen biletów w tej klasie.\n",
    "- **Średnia wartość portu** - porty zaokrętowania są różnie reprezentowane w klasach, ale średnie wartości wskazują na pewną różnorodność między klasami. Odchylenie standardowe jest znaczące, co wskazuje na dużą zmienność w portach zaokrętowania w każdej z klas.\n",
    "- **Średnia wartość przeżycia**- pasażerowie klasy 1 mają najwyższą średnią wartość przeżycia, podczas gdy pasażerowie klasy 3 mają najniższą. Odchylenie standardowe jest znaczne, co wskazuje na różnorodność w wynikach przeżycia w każdej z klas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agregacja danych ze względu na płeć:\n",
      "   plec  wiek_mean   wiek_std  liczba_czlonkow_rodziny_mean  \\\n",
      "0     0  30.015502  12.652592                      0.661137   \n",
      "1     1  28.572067  13.300806                      1.285408   \n",
      "\n",
      "   liczba_czlonkow_rodziny_std  cena_biletu_mean  cena_biletu_std  port_mean  \\\n",
      "0                     1.456806         26.126875        42.440265   1.553318   \n",
      "1                     1.719615         46.198097        63.292599   1.386266   \n",
      "\n",
      "   port_std  przezycie_mean  przezycie_std  \n",
      "0  0.787356        0.190758       0.393132  \n",
      "1  0.850644        0.727468       0.445741  \n"
     ]
    },
    {
     "data": {
      "image/png": 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ErveePXsqXbp0+uqrr3T+/HmNGzdOPXr00Pz58+NsIyoqSvXr19e2bdvUpUsX5c2bV4cPH9bYsWN18uRJLV261KL+1q1btXz5cnXv3l2SNHz4cNWtW1effPKJvv/+e3Xr1k23bt3SyJEj1bFjxzj3FdE++OADLVq0SD169FC+fPl048YNbdu2TcePH1exYsX0xRdf6M6dO/r333/N2010fwgNDdX06dPVsmVLde7cWXfv3tWMGTNUs2ZN/fnnnypSpIhFW/PmzdPdu3f1/vvvy2QyaeTIkWrcuLHOnj1rPit07dq1atKkifLly6fhw4frxo0b6tChg8WP4NHGjx+v+vXrq3Xr1oqIiNAvv/yiZs2aacWKFeb9TrTY9sOXL19W5cqV9fjxY/PnO23atFjPihw4cKAGDRqkatWqqWvXrjpx4oQmT56s3bt3a/v27bK3t1dQUJCioqK0bds21a1b1/x5RW/z0fbv36979+6pQoUKL9RHhw8fLmdnZ3322Wc6ffq0vvvuO9nb28vOzk63bt3SwIED9ccff2jWrFkKCAjQgAED4vz8XyTmaO+//75mzZqlDh06qFevXjp37pwmTpyo/fv3m9eJJA0YMEBDhw5V7dq1Vbt2be3bt081atRQRERErDF17NhRQUFBMb7sxycqKkr16tXTn3/+qa5duypPnjxatmyZ2rVrl+BlSFKPHj3k4eGhgQMHmj/jCxcumBPAaEePHlWRIkW0e/du5c2bV506ddKsWbNUq1Ytvffee3r8+LG2bt2qP/74QyVKlEhUDK8z8qP/Q34Ud36UUIcOHVJQUJDs7e3VpUsXZcuWTWfOnNFvv/2mYcOGycvLK8b7e/Tokfr27RvjbONbt26pdu3aat68uVq2bKkFCxaoa9eucnBwUMeOHSUl/pgWn+flMXnz5o0R++3bt9WvX78YP4g/7cGDB6pUqZJOnz6tHj16KCAgQAsXLlT79u11+/ZtixxJSthx91m7d+9WzZo1VaJECS1btkzOzs46evSogoKC5Obmpk8++UT29vaaOnWqKlWqpJCQEJUuXVoeHh4qUKCAtmzZYs5xt23bJpPJpJs3b+rYsWPKnz+/pCd9/kWumOrWrZu8vLw0YMAAhYWFSUrY8VmSZsyYoffff19ly5ZVnz59dPbsWdWvX1+enp7y8/MztzFu3DjzD+7Rxo4dqwMHDih9+vTxxnf69Gk1bdpUnTp1Urt27fTDDz+offv2Kl68uPLnz6/s2bOrXLlymjt3rvr27Wvx2rlz58rV1TXWk+VeNraE9hvDMNSgQQNt27ZNH3zwgfLmzatff/011mPp87bPpzVv3lwBAQEaPny49u3bp+nTp8vb21vffPONpCe3qe/cuXOMY8fu3bt18uRJffnll3G+t4QeM6KNGDFCdnZ2+uijj3Tnzh2NHDlSrVu31q5du+JsQ0p4ziM96QetWrXS+++/r3fffVfffvut6tWrpylTpujzzz9Xt27dJD3J55o3b/7cW8gOHDhQw4cP13vvvadSpUopNDRUe/bs0b59+1S9enW9//77ce7zpcTly9u2bdOSJUvUrVs3ubq6asKECWrSpIn+/vtvcx87fPiwatSoIS8vLw0cOFCPHz/WV199pYwZM8Zoe/LkycqfP7/q16+v1KlT67ffflO3bt0UFRVl/i4T7cSJE2rZsqXef/99de7cWblz507UsT768ylZsqSGDx+uK1euaPz48dq+fbv2798vDw8PlS9f3nwyZKFChST9X967bds287KuXbumv/76Sz169JCU+D46d+5cRUREqGfPnrp586ZGjhyp5s2bq0qVKtq8ebM+/fRTcw7/0UcfxTqQGe1FY5aeXJncv39/NW/eXO+9956uXbum7777ThUqVDCvEynh+8do/fv3l5+fn3bs2BFn3LFp3769FixYoDZt2qhMmTIKCQmJ0QefZ9iwYTKZTPr000919epVjRs3TtWqVdOBAwcsvsNdv35dxYoV0y+//KIaNWpo0KBBGjhwoMqWLavBgwfLwcFBu3bt0saNG1WjRo1ExWAt5M3/h7w5/rx51qxZ6tixo/Lnz6/g4GB5eHho//79Wr16tflK9Y0bN6pWrVoqXry4vvrqK9nZ2ZkHxLZu3apSpUpZLPN5x2YpcfvwhGjevLmyZcum4cOH648//tCECRN069YtixMJEyIh77Vx48Y6efKkfv75Z40dO1YZMmSQJHl5ecW4gCWxyMdiIh8jH4tGPkY+ZpN8zMArtXbtWiNVqlRGqlSpjMDAQOOTTz4x1qxZY0RERMSoK8mws7Mzjh49alHeqVMnw9fX17h+/bpFeYsWLQx3d3fj/v37hmEYxrhx4wxJxoIFC8x1wsLCjJw5cxqSjE2bNpnLK1asaEgy5syZYy4LDw83fHx8jCZNmpjLzp07Z0gyZs6caS6Lbu9pP//8syHJ2LJlS7zrY9SoUUaqVKmM0NBQwzAMY8KECYa/v79RqlQp49NPPzUMwzAiIyMNDw8Po2/fvolu86uvvjIkGS1btoxRv3v37kZsm8DSpUsNScbQoUMtyps2bWqYTCbj9OnThmEYxtixYw1JxrVr1+J8fz/88IMhyRgzZkyM56KiouJ83dWrVw1Jxvfff28YhmHcvn3bsLOzM5o1a2ZkzJjRXK9Xr16Gp6eneVmxfT5Vq1Y1ChYsaDx8+NCi7bJlyxq5cuUyl82cOdOQZOzevdu4dOmSkT9/fiN79uzG+fPn44wzLiNHjozRn2LTp08fQ5KxdetWc9ndu3eNgIAAI1u2bEZkZKRhGAnvy48fPzYCAgIMf39/49atWxZtPb2+27VrZ0gyBg8ebFGnaNGiRvHixc2PT5w4YUgyJk+ebFGvfv36RrZs2ay63qtVq2YRY9++fY1UqVIZt2/fNpdVrFjRqFixovnxjz/+aNjZ2VmsP8MwjClTphiSjO3bt5vLJBmOjo7GuXPnzGVTp041JBk+Pj7mbdAwDCM4ONiQZFE3Nu7u7kb37t3jrVOnTh3D398/Rvnjx4+N8PBwi7Jbt24ZGTNmNDp27Ggui1636dOnN27evGkuX7ZsmSHJ+O2338xlRYoUMXx9fS3W2dq1aw1JMWJ4dh8SERFhFChQwKhSpYpFeVz74ei+u2vXLnPZ1atXDXd3d4t1d/XqVcPBwcGoUaOGuT8bhmFMnDjRkGT88MMPhmE82c+5ubkZn3zyiWEYT/pK+vTpjWbNmhmpUqUy7t69axiGYYwZM8aws7Mz9++E9tFNmzYZkowCBQpYHG9atmxpmEwmo1atWhavDwwMjPVze9qLxrx161ZDkjF37lyL5a1evdqiPHrd1alTx2Lb+Pzzzw1JRrt27cxlP/74oyHJaNGihfmzfXZ7idauXTuL97Z48WJDkjFu3DiL91alSpUY23Vsorfh4sWLW6zb6P3gsmXLDMMwjAcPHhj58+c30qVLZ2zYsMEwDMPYuHGjIcno1atXjOU+/Z79/f0t3m9KRH5kifwobs9u27Gt+woVKhiurq7GhQsXErzsbt26GalSpTI2btxo0ZYkY/To0eay8PBwo0iRIoa3t7e5fyb0mPY8Cc1jni2vW7eu4eLiYrFNPLueovv9Tz/9ZC6LiIgwAgMDDRcXF3NfS8xxt127dkbatGkNwzCMbdu2GW5ubkadOnUscp+GDRsaDg4OxpkzZ8xl//33n+Hq6mpUqFDBXNa9e3eLHLdfv35GhQoVDG9vb/Mx7saNG4bJZDLGjx8f90p8RvQ+unz58sbjx4/N5Qk9PkdERBje3t5GkSJFLD7jadOmGZJiPc5EW7BgQYx8Mzqep3Msf3//GNvo1atXDUdHR+PDDz80l0XnbcePHzeXRUREGBkyZEj0MSK22GKT0H4TvX8YOXKkud7jx4+NoKCgF9o+o/dRz24/jRo1MtKnT29+fPv2bcPJycm8X4zWq1cvI23atMa9e/fMZZKMr776yvw4oceM6Bwqb968Fn1g/PjxhiTj8OHD5rJnc4yE5jyG8X/9YMeOHeayNWvWGJIMZ2dni/UV3ReePl7FpnDhwkadOnXirRPXPt8wEpcvOzg4mI8DhmEYBw8eNCQZ3333nbmsYcOGhpOTk8V7OXbsmJEqVaoYMcR2PKtZs6aRPXt2i7Lo9bZ69WqL8oQe66O38QIFChgPHjww112xYoUhyRgwYIC5LH/+/Ebz5s3Nj4sVK2Y0a9bMYrtcsmSJIck4ePCgYRgJ76PR+14vLy+L7zLR38sKFy5sPHr0yFzesmVLw8HBwWJ/G5sXifn8+fNGqlSpjGHDhlks6/Dhw0bq1KnN5YnZP0ZvC1WqVDFvc89uL9Git/9oe/fuNSQZffr0sajXvn37GNt1bKK34cyZM1t8543eD0YfUyIjI43q1asbDg4Oxs8//2wYhmGcOnXKsLOzMxo1amRxrDAMy31WXN87kgp5syXy5tjdvn3bcHV1NUqXLm2xf3v6dVFRUUauXLmMmjVrWizr/v37RkBAgFG9enVzWUKPzdGvf1Zs+/DniW6zfv36FuXdunWz2G8ZRszvzNHbfnQfTcx7HTVqVKy/icXWd6MlZH/0LPIx8rFo5GPkY+RjtsnHuK36K1a9enXt3LlT9evX18GDBzVy5EjVrFlTmTNnjvU21xUrVlS+fPnMjw3D0OLFi1WvXj0ZhqHr16+b/2rWrKk7d+6Yb2m8cuVK+fr6qmnTpubXp0mTRl26dIk1NhcXF4urDB0cHFSqVCmdPXs23vf09FkdDx8+1PXr11WmTBlJMscSl6CgIEVGRprPVIm+GiQoKMh85eGRI0d0+/Zti6tEEtvmBx98EG8cT1u5cqVSpUplvnIl2ocffijDMLRq1SpJMp+ds2zZsjhvpbx48WJlyJBBPXv2jPFcXGe+Sk/OSMyTJ4/57Nft27crVapU+vjjj3XlyhWdOnVK0pP1FX0GUmxu3rypjRs3qnnz5rp79665r9y4cUM1a9bUqVOnLG5PIj253UnFihX16NEjbdmyRf7+/nHGGZtNmzYpODhYPXv2VJs2beKtu3LlSpUqVUrly5c3l7m4uKhLly46f/68jh07Zq6XkL68f/9+nTt3Tn369Ilxi7HY1tGz/SIoKMiiv7/11lsqXbq05s6day67efOmVq1apdatW1t1vXfp0sViedHbRvQtomOzcOFC5c2bV3ny5LHYF1SpUkWSYtzOtWrVqha3SildurQkqUmTJnJ1dY1R/rxt38PDQ7t27dJ///0Xb73YpEqVynx1XlRUlG7evKnHjx+rRIkSsW7D77zzjtKlS2d+HL0/iI7x0qVLOnDggNq1ayd3d3dzverVq1vsQ6M9vQ+5deuW7ty5o6CgoFjbfnY/LD3pk2XKlLE4i9rLy0utW7e2qLd+/XpFRESoT58+FmeSdu7cWW5ubuYpBOzs7FS2bFnzNn/8+HHduHFDn332mQzD0M6dOyU92eYLFChg7t+J7aNt27a1OCO2dOnSMgzDfPXj0+X//POPHj9+HGN9RHvRmBcuXCh3d3dVr17dot8WL15cLi4u5n4bve6ip5SI9uyZw1OmTFGHDh0kPTkbOLFzGq1evVr29vbq3LmzxXtL7BntXbp0sVi3Xbt2VerUqbVy5UpduXJFQUFBOnr0qAoVKmTeRqNvpfTVV1/FWF58x4iUiPzIEvnRi7t27Zq2bNmijh07KmvWrAla9pw5c/T9999r5MiRqly5ssVzqVOntphGxcHBQe+//76uXr2qvXv3Skr8MS0uic1jJGnIkCFasWKFZs2aFevxLtrKlSvl4+Ojli1bmsvs7e3Vq1cv3bt3TyEhIRb1n3fcfdqmTZtUs2ZNVa1aVUuWLDHP+RgZGam1a9eqYcOGyp49u7m+r6+vWrVqpW3btik0NNS8/CtXrpin8dm6dasqVKhg0ee3bdsmwzBe6Mrxzp07W8z1ndDj8549e3T16lV98MEHFncViL5dZVyOHTumjh07qkGDBvFeLRMtX758Fu/Ly8tLuXPntljfzZs3l5OTk8Uxf82aNbp+/Xq8dzN6mdgS2m9Wrlyp1KlTq2vXruZ6qVKlirGNJ3b7jC1Xv3HjhrnfuLu7q0GDBvr555/Nt6mNjIzU/PnzzbdvjE1ijhnROnToYNEH4tsmoiU054mWL18+BQYGmh9H5+RVqlSxWF+JydWPHj1q/u6YWInJl6tVq2Zx9WKhQoXk5uZmjjEyMlJr1qxRw4YNLd5L3rx5Y73d6tNt37lzR9evX1fFihV19uxZ3blzx6JuQEBAjGUk9FgfvY1369bNYg7kOnXqKE+ePBbTfT29P7p7964OHjyoLl26KEOGDObyrVu3mu+GISW+jzZr1sxi3xL9Wb/77rtKnTq1RXlERESM75TPepGYlyxZoqioKDVv3tyi3/r4+ChXrlzmfpvQ/eOyZcvMVwy3adPmuXfSeFb07WCjr9SLFlsOEZ+2bdtafOdt2rSpfH19tXLlSoWFhalOnTpat26dfH191aJFC0lPpuyKiorSgAEDYlwVaMtcnbzZEnlz7NatW6e7d+/qs88+izHHe/TrDhw4oFOnTqlVq1a6ceOGuR+EhYWpatWq2rJlS4y4nndslhK3D0+IZ7+bR6+LlStXJngZL/JekxL5GPlYQpGPkY+RjyVdPsbguA2ULFlSS5Ys0a1bt/Tnn38qODhYd+/eVdOmTc2DgdECAgIsHl+7dk23b9/WtGnT5OXlZfEXPThw9epVSdKFCxeUM2fOGJ0kd+7cscaVJUuWGHXTpUsX55yp0W7evKnevXsrY8aMcnZ2lpeXlznu5yU9xYoVU5o0aSx2BEFBQapQoYL27Nmjhw8fmp97egA1sW0+ux7jc+HCBWXKlMliQ5WeHCiin5ee/GhYrlw5vffee8qYMaNatGihBQsWWCRTZ86cUe7cuS12nAn19I5z69atKlGihEqUKCFPT09t3bpVoaGhOnjwYLw/EJ4+fVqGYah///4x+kv0YEx0f4nWpk0bXb16VSEhIcqcOXOiYv7333/N62XMmDHPrX/hwoVY++Oz6zqhfTl6voyE3Fosem7fp8XW39u2bavt27ebY1m4cKEePXoU78D/i6z3ZxPS6B+k49v+Tp06paNHj8Zo46233kpQG9EHyWdvsRJd/rxtf+TIkTpy5Ij8/PxUqlQpDRw48LlJ4dNmz56tQoUKmefc8fLy0u+//x7rNvy89RP9+eTKlSvGa2PrYytWrFCZMmXk5OQkT09PeXl5afLkyQnef1y4cCFBbUXH9Wy5g4ODsmfPbnHyQ1BQkPbu3asHDx5o69at8vX1VbFixVS4cGGLgYFnt/nE9NHE9IGoqKjn7sNfJOZTp07pzp078vb2jtF37927Z3EMk2J+pl5eXhYDNs7Ozho8eHC8ccbnwoUL8vX1VZo0aSzKc+bMmajlPBuni4uLfH19df78eaVNm1Y1atSw+IIlPdlnZcqUyeKW+G8y8qP/Q3704qKPQwnJBaQnP5R98MEHatmypcV8ldEyZcoU48ty9HH26XmjE3NMi0ti8hjpyRfUQYMGKTg4WE2aNIm3bvRx69kvk89+ftESmpc8fPhQderUUdGiRbVgwQKLL+TXrl3T/fv348z1oqKizPOLRR8ntm7dqrCwMO3fv9/c55/eDtzc3OKcLy4+z/b1hB6f4zoW2dvbWwz4Py00NFSNGzdW5syZNWfOnAR9YX92fUsx9zMeHh6qV6+exdQhc+fOVebMmc0nXT1PYmNLaL+JPpY+O53Ss+s3sdtnQvph27Zt9ffff5v7yfr163XlypV4c/XEHDMSE8uzEprzxNXGy+bqgwcP1u3bt/XWW2+pYMGC+vjjjy3mO3yexOTLz+vD165d04MHDxKcq2/fvl3VqlVT2rRp5eHhIS8vL/McvLH9GPushB7r49oXSFKePHli5OqXLl3S6dOntWPHDplMJgUGBsb43l6uXDmLbSYxfdTafeBFYj516pQMw1CuXLli9Nvjx48/N1d/dv9ob28f4xa9iXHhwgXZ2dnF+JxfNlc3mUzKmTOnzp8/LycnJxUpUiTG1GdnzpyRnZ1dvCef2Qp58/8hb45dQvLK6MG6du3axegL06dPV3h4eIx1kZDjYWL24Qnx7PabI0cO2dnZWeTiz/Mi7zWpkI89QT6WMORj5GPkY0mXjzHnuA05ODioZMmSKlmypN566y116NBBCxcutLiC7Nkr4KITpHfffTfO+VCj5y9IrKevpnha9Bk1cWnevLl27Nihjz/+WEWKFJGLi4uioqL09ttvP/esO3t7e5UuXVpbtmzR6dOndfnyZQUFBSljxox69OiRdu3apa1btypPnjwWA5mJbTOxVxImhLOzs7Zs2aJNmzbp999/1+rVqzV//nxVqVJFa9eujXN9JlT58uX1v//9T2fPnjUn9yaTSeXLl9fWrVuVKVMmRUVFxTs4Hr0uPvroo1jPAJNi7sQaN26sOXPmaPz48Ro+fHiC442IiFDTpk3l6OioBQsWJMkP3taU0M+nRYsW6tu3r+bOnavPP/9cP/30k0qUKBHnl0Hpxdb7i2x/UVFRKliwYJwnIjx70I6rjZfZ9oOCgvTrr79q7dq1GjVqlL755hstWbIk3vnEJOmnn35S+/bt1bBhQ3388cfy9vZWqlSpNHz4cPOXOGvEGJutW7eqfv36qlChgr7//nv5+vrK3t5eM2fOjHWu6qTYf8SmfPnyevTokXbu3Gkxp2p0svTXX3/p2rVrMbb5xPRRa/eBF4k5KipK3t7eFle+Pe3Zk1aep127dtq8eXOMcpPJFGv8kZGRiVq+Nbi4uGjYsGGqVKnSK2/7dUR+RH70qty6dUtNmjTRW2+9penTp7/wchJ7TLOGc+fOqXXr1qpevbqGDh1q9eUntN87Ojqqdu3aWrZsmVavXq26deu+UHuZMmVSQECAtmzZomzZsskwDAUGBsrLy0u9e/fWhQsXtHXrVpUtWzbeOf3i8qqO5dKTs/T/++8//fnnn3Jzc0vQaxK6vtu2bauFCxdqx44dKliwoJYvX65u3boleJ28SGy2lJD1UrNmTWXMmFE//fSTKlSooJ9++kk+Pj6qVq1anMt9kWPGi+bqicl5rJ2nVahQQWfOnNGyZcu0du1aTZ8+XWPHjtWUKVP03nvvxfvaxObL1szVz5w5o6pVqypPnjwaM2aM/Pz85ODgoJUrV2rs2LExjmevMleXpC1btujs2bMqVqyY0qZNq6CgIE2YMEH37t3T/v37Y/zwmJg+mhS5emJjjoqKkslk0qpVq2Jt99lBl+epXbu2zp8/b54TN1pcg0G2yNWjj9nt27d/5W2/LPJm8uaXEf0+R40apSJFisRa59lt/nmfcWL34S/iRa4UfJH3mtB2E7vfIh97gnyMfOxFkI89QT5mHcl79OoNUqJECUlPbg0cHy8vL7m6uioyMjLeA4wk+fv768iRIzIMw6KjR9+u0Bpu3bqlDRs2aNCgQRowYIC5PDG3CgkKCtI333yj9evXK0OGDMqTJ49MJpPy58+vrVu3auvWrRY/tFmjTSnujd/f31/r16/X3bt3Lc7y/Ouvv8zPR7Ozs1PVqlVVtWpVjRkzRl9//bW++OILbdq0yXwrk127dunRo0cWt9tNiOjBpHXr1mn37t367LPPJD05qE6ePNl8JVPx4sXjXEb0GUL29vbP7S/RevbsqZw5c2rAgAFyd3c3t/s8vXr10oEDB7RlyxZlzJgxQa/x9/ePtT8+u64T2pejbx1z5MiRBL/f5/H09FSdOnU0d+5ctW7dWtu3b9e4cePifc2LrPcXkSNHDh08eFBVq1a12W3dfH191a1bN3Xr1k1Xr15VsWLFNGzYMPPgeFxxLVq0SNmzZ9eSJUss6sR2e+mEiO4rse0Hnu0nixcvlpOTk9asWWO+9askzZw5M1HtJaSt6LhOnDhhccZeRESEzp07Z9E/SpUqJQcHB/N+7+OPP5b0ZJv/3//+pw0bNpgfP+1F+qi1vEjMOXLk0Pr161WuXLl4k+enP9On1921a9eee1am9OTs2NjuZPDs1ZH+/v7atGmT7t+/b3H1+OnTp5/bxtNOnTplcTvme/fu6dKlS6pdu3acr8mRI4fWrFmjmzdvcvV4HMiPyI8SK3p/ceTIkXjrRUVFqXXr1rp9+7bWr18f4+4R0f777z+FhYVZXD1+8uRJSTJPVWKtY1pC85gHDx6ocePG8vDw0M8//5yggVF/f38dOnRIUVFRFvVj+/wSw2Qyae7cuWrQoIGaNWumVatWmU8E8vLyUpo0aeLM9ezs7CxO5AsKCtKWLVsUEBCgIkWKyNXVVYULF5a7u7tWr16tffv2adCgQS8U57MSenx++lj09NXZjx490rlz52JcxT5ixAgtXbpUS5YsUZ48eawS69PefvtteXl5ae7cuSpdurTu37//3GmMXia2hPYbf39/bdiwQffu3bP4oebZzz6h22dipEqVSq1atdKsWbP0zTffaOnSpTFuo/+sxBwzXkZCc56k5OnpqQ4dOqhDhw66d++eKlSooIEDB5p/jI1rn2+NfPlpXl5ecnZ2TlD+/Ntvvyk8PFzLly+3uHLn2duexiehx/qn9wXP3oHhxIkTFvvGrFmzKmvWrNq6davOnj1r/r5eoUIF9evXTwsXLlRkZGSMXP1F+qi1vEjMOXLkkGEYCggIMN8pJTaJ3T8+K126dLp9+3aM8thy9aioKJ07d87iaqMXydWfZhiGTp8+He8gcI4cORQVFaVjx47FOZiWnJA3kzc/7em8Mq4r+6LruLm5We14aI19+LNOnTplcbXi6dOnFRUVZTFt4PMk5r3G9blGX6X87L4rvukQn0U+9n/Ix8jHnq4XXU4+9gT52P9JynyM26q/Yps2bYr1bJLoeVLiuxpVerIhN2nSRIsXL471IHbt2jXz/7Vr19Z///2nRYsWmcvu37+vadOmvWj4scYjxTxDJjEDM0FBQQoPD9e4ceMs5s8OCgrSjz/+qP/++8/iqkNrtCnJ/CPnszuA2rVrKzIyUhMnTrQoHzt2rEwmk3nQ7+bNmzGWGb2BhoeHS3oyl/P169djLCu2+J8VEBCgzJkza+zYsXr06JHKlSsn6cl6OXPmjBYtWqQyZcrEe4W2t7e3KlWqpKlTp8b6Benp/vK0/v3766OPPlJwcLAmT54cb5zSk4Py1KlTNWnSJIs5mJ+ndu3a+vPPP81zE0tSWFiYpk2bpmzZsplvl5HQvlysWDEFBARo3LhxMT7XFzlLLlqbNm107Ngxffzxx0qVKpV5zou4vOh6T6zmzZvr4sWL+t///hfjuQcPHigsLMwq7cQmMjIyxi10vL29lSlTJnP/l55sZ7Hd6ie27XjXrl0WfSExfH19VaRIEc2ePduivXXr1sW4rVyqVKlkMpkszoI7f/68li5dmuD2ateurT/++EN//vmnuezatWsxzkStVq2aHBwcNGHCBIv3OmPGDN25c0d16tQxlzk5OalkyZL6+eef9ffff1tchf3gwQNNmDBBOXLkkK+vb4x4EttHreVFYm7evLkiIyM1ZMiQGMt7/PixedutVq2a7O3t9d1331msu4Tu63PkyGG+cj3awYMHtX37dot6NWvW1KNHjyy2o6ioKE2aNClB7USbNm2aHj16ZH48efJkPX78ON67KDRp0kSGYcQ62PMy+6zXEflRTORHL8bLy0sVKlTQDz/8oL///jvOZQ8aNEhr1qzRzz//HO9tMh8/fqypU6eaH0dERGjq1Kny8vIyn6BorWNaQvOYDz74QCdPntSvv/5qMc1EfGrXrq3Lly9r/vz5Fu/tu+++k4uLiypWrJioWJ/m4OCgJUuWqGTJkqpXr5752JgqVSrVqFFDy5Yts7jt5ZUrVzRv3jyVL1/e4mqZoKAgnT9/XvPnzzf3bTs7O5UtW1ZjxozRo0ePXmi+8dgk9PhcokQJeXl5acqUKYqIiDDXmzVrVozPaP369fryyy/1xRdfqGHDhlaJ81mpU6dWy5YttWDBAs2aNUsFCxZM0NV9LxpbQvtN7dq19fjxY4vvDZGRkfruu+8slpfQ7TOx2rRpo1u3bun999/XvXv3njsHe2KOGS8joTlPUrlx44bFYxcXF+XMmTNGri7F3OdbI19+dnk1a9bU0qVLLT7748ePa82aNTHqSpZ94s6dO4n6ITihx/oSJUrI29tbU6ZMsVgvq1at0vHjxy1ydenJfmrjxo36888/zfuj6JN5RowYIWdn51hPXk9sH7WmxMbcuHFjpUqVSoMGDYqxXRqGYe5Xidk/xiZHjhy6c+eOxa1lL126pF9//dWiXvSd2L7//nuL8mf3L88zZ84c3b171/x40aJFunTpUry5esOGDWVnZ6fBgwfHuELOlrk6eXNM5M0x1ahRQ66urho+fLgePnwY6+uKFy+uHDly6Ntvv9W9e/diLONFjofW2Ic/69nv5tHb//PuWPi0xLzXuD5XNzc3ZciQQVu2bLEof3b/FBfyMUvkY+Rj0cjHyMdslY9x5fgr1rNnT92/f1+NGjVSnjx5FBERoR07dmj+/PnKli2beU6N+IwYMUKbNm1S6dKl1blzZ+XLl083b97Uvn37tH79enNy1blzZ02cOFFt27bV3r175evrqx9//DHOK2NehJubmypUqKCRI0fq0aNHypw5s9auXatz584leBmBgYFKnTq1Tpw4oS5dupjLo6+QlmSRxFqjTUnmHU6vXr1Us2ZN84BSvXr1VLlyZX3xxRc6f/68ChcurLVr12rZsmXq06eP+WzDwYMHa8uWLapTp478/f119epVff/998qSJYv5dhlt27bVnDlz1K9fP/POLywsTOvXr1e3bt3UoEGDeGMMCgrSL7/8ooIFC5p//Iy+9cbJkyfVqlWr577PSZMmqXz58ipYsKA6d+6s7Nmz68qVK9q5c6f+/fdfHTx4MNbXjRo1Snfu3FH37t3l6uoa5wHj+vXr6tatm/LlyydHR0f99NNPFs83atQoxlyd0T777DP9/PPPqlWrlnr16iVPT0/Nnj1b586d0+LFi81nRCa0L9vZ2Wny5MmqV6+eihQpog4dOsjX11d//fWXjh49GuNAn1B16tRR+vTptXDhQtWqVUve3t7Pfc2LrvfEaNOmjRYsWKAPPvhAmzZtUrly5RQZGam//vpLCxYs0Jo1a8xnj1vb3bt3lSVLFjVt2lSFCxeWi4uL1q9fr927d2v06NHmesWLF9f8+fPVr18/lSxZUi4uLqpXr57q1q2rJUuWqFGjRqpTp47OnTunKVOmKF++fLF+UUmI4cOHq06dOipfvrw6duyomzdv6rvvvlP+/PktllmnTh2NGTNGb7/9tlq1aqWrV69q0qRJypkzZ4Ln/fnkk0/0448/6u2331bv3r2VNm1aTZs2zXxGbzQvLy8FBwdr0KBBevvtt1W/fn2dOHFC33//vUqWLBljuwoKCtKIESPk7u6uggULSnpy0kHu3Ll14sSJOG8t8yJ91FoSG3PFihX1/vvva/jw4Tpw4IBq1Kghe3t7nTp1SgsXLtT48ePVtGlTeXl56aOPPtLw4cNVt25d1a5dW/v379eqVauUIUOG58bVsWNHjRkzRjVr1lSnTp109epVTZkyRfnz51doaKi5XsOGDVWqVCl9+OGHOn36tPLkyaPly5ebj6UJvStDRESEqlatqubNm5s/4/Lly6t+/fpxvqZy5cpq06aNJkyYoFOnTplv4bd161ZVrlxZPXr0SFDbKQH5UUzkRy9uwoQJKl++vIoVK6YuXbooICBA58+f1++//64DBw7o8OHDGjJkiCpUqKCrV6/GyF2e3jdnypRJ33zzjc6fP6+33npL8+fP14EDBzRt2jTz1TvWOqYlJI/5/fffNWfOHDVp0kSHDh2yOOa4uLjE+UNbly5dNHXqVLVv31579+5VtmzZtGjRIvPdRp6dEzOxnJ2dtWLFClWpUkW1atVSSEiIChQooKFDh2rdunUqX768unXrptSpU2vq1KkKDw/XyJEjLZYR3Z9PnDihr7/+2lxeoUIFrVq1So6OjipZsuRLxRktocdne3t7DR06VO+//76qVKmid955R+fOndPMmTNjzDnesmVLeXl5KVeuXDH6VPXq1RN8Z6Xnadu2rSZMmKBNmzbpm2++SdBrXjS2hPabevXqqVy5cvrss890/vx55cuXT0uWLIn1BMnnbZ8vomjRoipQoIAWLlyovHnzqlixYs99TUKPGS8joTlPUsmXL58qVaqk4sWLy9PTU3v27NGiRYss8ou49vnWyJefNWjQIK1evVpBQUHq1q2b+Yf9/PnzWyyzRo0acnBwUL169cw/Xv7vf/+Tt7f3c6+IjZbQY729vb2++eYbdejQQRUrVlTLli115coVjR8/XtmyZVPfvn0t6gcFBWnu3Lnm6c6kJz8ely1bVmvWrFGlSpXk4OAQI54X6aPWktiYc+TIoaFDhyo4OFjnz59Xw4YN5erqqnPnzunXX39Vly5d9NFHHyVq/xibFi1a6NNPP1WjRo3Uq1cv3b9/X5MnT9Zbb72lffv2mesVL15cTZo00bhx43Tjxg2VKVNGISEh5ru4JDRX9/T0VPny5dWhQwdduXJF48aNU86cOdW5c+c4X5MzZ0598cUXGjJkiIKCgtS4cWM5Ojpq9+7dypQpU6KmobMm8uaYyJtjX69jx47Ve++9p5IlS6pVq1ZKly6dDh48qPv372v27Nmys7PT9OnTVatWLeXPn18dOnRQ5syZdfHiRW3atElubm767bffErVOrLEPf9a5c+dUv359vf3229q5c6d++ukntWrV6rlXRD4tMe81+nP94osv1KJFC9nb26tevXpKmzat3nvvPY0YMULvvfeeSpQooS1btpj3R89DPhYT+dgT5GPkY+RjNsrHDLxSq1atMjp27GjkyZPHcHFxMRwcHIycOXMaPXv2NK5cuWJRV5LRvXv3WJdz5coVo3v37oafn59hb29v+Pj4GFWrVjWmTZtmUe/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      "text/plain": [
       "<Figure size 2000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Funkcja do agregacji\n",
    "def aggregate_by_sex(df):\n",
    "    # Grupowanie przez `sex`\n",
    "    aggregated_data = df.groupby('sex').agg({\n",
    "        'age': ['mean', 'std'],\n",
    "        'family_size': ['mean', 'std'],\n",
    "        'fare': ['mean', 'std'],\n",
    "        'embarked': ['mean', 'std'],\n",
    "        'survived': ['mean', 'std']\n",
    "    }).reset_index()\n",
    "\n",
    "    # Zmiana nazw kolumn\n",
    "    aggregated_data.columns = ['plec', 'wiek_mean', 'wiek_std', 'liczba_czlonkow_rodziny_mean', 'liczba_czlonkow_rodziny_std',\n",
    "                               'cena_biletu_mean', 'cena_biletu_std', 'port_mean', 'port_std',\n",
    "                               'przezycie_mean', 'przezycie_std']\n",
    "    return aggregated_data\n",
    "\n",
    "# Agregacja danych\n",
    "aggregated_data_sex = aggregate_by_sex(df)\n",
    "\n",
    "# Wyświetlenie wyników agregacji\n",
    "print(\"Agregacja danych ze względu na płeć:\")\n",
    "print(aggregated_data_sex)\n",
    "\n",
    "# Lista cech do analizy\n",
    "features_sex = ['wiek', 'liczba_czlonkow_rodziny', 'cena_biletu', 'port', 'przezycie']\n",
    "\n",
    "# Liczba wierszy i kolumn dla wykresów\n",
    "n_cols = 3\n",
    "n_rows = int(np.ceil(len(features_sex) / n_cols))\n",
    "\n",
    "# Tworzenie wykresów średnich wartości z odchyleniami standardowymi\n",
    "fig, axes = plt.subplots(n_rows, n_cols, figsize=(20, n_rows * 5))\n",
    "\n",
    "for i, feature in enumerate(features_sex):\n",
    "    row = i // n_cols\n",
    "    col = i % n_cols\n",
    "    ax = axes[row, col]\n",
    "    sns.barplot(data=aggregated_data_sex, x='plec', y=f'{feature}_mean',\n",
    "                yerr=aggregated_data_sex[f'{feature}_std'], ax=ax, palette='viridis')\n",
    "    ax.set_title(f'Średnia wartość {feature} z odchyleniem standardowym według płci')\n",
    "    ax.set_xlabel('Płeć')\n",
    "    ax.set_ylabel(f'Średnia wartość {feature}')\n",
    "    ax.grid(False)  # Usunięcie siatki\n",
    "\n",
    "# Usuwanie pustych wykresów\n",
    "for j in range(i + 1, n_rows * n_cols):\n",
    "    fig.delaxes(axes.flatten()[j])\n",
    "\n",
    "# Ustawienie odstępów między wykresami\n",
    "plt.tight_layout()\n",
    "\n",
    "# Wyświetlenie wykresów\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Podsumowanie:\n",
    "\n",
    "- **Średnia wartość wieku**- średnia wieku mężczyzn (0) i kobiet (1) jest bardzo podobna. Odchylenie standardowe jest duże, co sugeruje znaczną różnorodność wiekową zarówno wśród mężczyzn, jak i kobiet.\n",
    "- **Średnia wartość liczby członków rodziny**- średnia liczba członków rodziny jest wyższa wśród kobiet niż mężczyzn. Odchylenie standardowe jest również większe wśród kobiet, co wskazuje na większą zmienność w liczbie członków rodziny wśród kobiet.\n",
    "- **Średnia wartość ceny biletu**- kobiety miały średnio wyższą cenę biletu niż mężczyźni. Odchylenie standardowe ceny biletu jest większe wśród kobiet, co sugeruje większą różnorodność cen biletów w tej grupie.\n",
    "- **Średnia wartość portu**- port zaokrętowania jest podobny dla obu płci, co wskazuje na brak znaczącej różnicy w średnim porcie zaokrętowania między mężczyznami a kobietami. Odchylenie standardowe jest duże, co wskazuje na znaczną zmienność w porcie zaokrętowania w obu grupach.\n",
    "- **Średnia wartość przeżycia**- średnia wartość przeżycia jest znacznie wyższa wśród kobiet niż mężczyzn. Odchylenie standardowe jest większe wśród kobiet, co wskazuje na różnorodność wyników przeżycia w tej grupie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agregacja danych ze względu na przeżycie:\n",
      "   przezycie  wiek_mean   wiek_std  liczba_czlonkow_rodziny_mean  \\\n",
      "0        0.0  29.945165  12.216475                      0.849383   \n",
      "1        1.0  28.784167  13.920034                      0.938000   \n",
      "\n",
      "   liczba_czlonkow_rodziny_std  cena_biletu_mean  cena_biletu_std  port_mean  \\\n",
      "0                     1.791901         23.331856        34.105727   1.606173   \n",
      "1                     1.168283         49.361184        68.648795   1.312000   \n",
      "\n",
      "   port_std  \n",
      "0  0.731905  \n",
      "1  0.903487  \n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Funkcja do agregacji\n",
    "def aggregate_by_survived(df):\n",
    "    # Grupowanie przez `survived`\n",
    "    aggregated_data = df.groupby('survived').agg({\n",
    "        'age': ['mean', 'std'],\n",
    "        'family_size': ['mean', 'std'],\n",
    "        'fare': ['mean', 'std'],\n",
    "        'embarked': ['mean', 'std']\n",
    "    }).reset_index()\n",
    "\n",
    "    # Zmiana nazw kolumn\n",
    "    aggregated_data.columns = ['przezycie', 'wiek_mean', 'wiek_std',\n",
    "                               'liczba_czlonkow_rodziny_mean', 'liczba_czlonkow_rodziny_std',\n",
    "                               'cena_biletu_mean', 'cena_biletu_std',\n",
    "                               'port_mean', 'port_std']\n",
    "    return aggregated_data\n",
    "\n",
    "# Agregacja danych\n",
    "aggregated_data_survived = aggregate_by_survived(df)\n",
    "\n",
    "# Wyświetlenie wyników agregacji\n",
    "print(\"Agregacja danych ze względu na przeżycie:\")\n",
    "print(aggregated_data_survived)\n",
    "\n",
    "# Lista cech do analizy\n",
    "features_survived = ['wiek', 'liczba_czlonkow_rodziny', 'cena_biletu', 'port']\n",
    "\n",
    "# Liczba wierszy i kolumn dla wykresów\n",
    "n_cols = 3\n",
    "n_rows = int(np.ceil(len(features_survived) / n_cols))\n",
    "\n",
    "# Tworzenie wykresów średnich wartości z odchyleniami standardowymi\n",
    "fig, axes = plt.subplots(n_rows, n_cols, figsize=(20, n_rows * 5))\n",
    "\n",
    "for i, feature in enumerate(features_survived):\n",
    "    row = i // n_cols\n",
    "    col = i % n_cols\n",
    "    ax = axes[row, col]\n",
    "    sns.barplot(data=aggregated_data_survived, x='przezycie', y=f'{feature}_mean',\n",
    "                yerr=aggregated_data_survived[f'{feature}_std'], ax=ax, palette='viridis')\n",
    "    ax.set_title(f'Średnia wartość {feature} z odchyleniem standardowym według przeżycia')\n",
    "    ax.set_xlabel('Przeżycie')\n",
    "    ax.set_ylabel(f'Średnia wartość {feature}')\n",
    "    ax.grid(False)  # Usunięcie siatki\n",
    "\n",
    "# Usuwanie pustych wykresów\n",
    "for j in range(i + 1, n_rows * n_cols):\n",
    "    fig.delaxes(axes.flatten()[j])\n",
    "\n",
    "# Ustawienie odstępów między wykresami\n",
    "plt.tight_layout()\n",
    "\n",
    "# Wyświetlenie wykresów\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Podsumowanie:\n",
    "\n",
    "- **Średnia wartość wieku**- średnia wieku osób, które przeżyły (1), jest zbliżona do średniej wieku osób, które nie przeżyły (0). Odchylenie standardowe jest duże, co wskazuje na znaczną różnorodność wiekową w obu grupach.\n",
    "- **Średnia wartość liczby członków rodziny**- średnia liczba członków rodziny jest nieznacznie wyższa wśród osób, które przeżyły, niż wśród tych, które nie przeżyły. Odchylenie standardowe jest znaczne w obu grupach, co wskazuje na dużą zmienność liczby członków rodziny.\n",
    "- **Średnia wartość ceny biletu**- osoby, które przeżyły, miały średnio wyższą cenę biletu niż osoby, które nie przeżyły. Odchylenie standardowe ceny biletu jest większe wśród osób, które przeżyły, co sugeruje większą różnorodność cen biletów w tej grupie.\n",
    "- **Średnia wartość portu**- port zaokrętowania jest podobny dla obu grup, co wskazuje na brak znaczącej różnicy w średnim porcie zaokrętowania między osobami, które przeżyły, a tymi, które nie przeżyły. Odchylenie standardowe jest duże w obu grupach, co wskazuje na znaczną zmienność w porcie zaokrętowania."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agregacja danych ze względu na port zaokrętowania:\n",
      "   port  wiek_mean   wiek_std  liczba_czlonkow_rodziny_mean  \\\n",
      "0     0  31.401543  13.630504                      0.770370   \n",
      "1     1  28.256098   9.540330                      0.455285   \n",
      "2     2  29.109869  13.027331                      0.973828   \n",
      "\n",
      "   liczba_czlonkow_rodziny_std  cena_biletu_mean  cena_biletu_std  \n",
      "0                     0.944093         62.336267        84.185996  \n",
      "1                     1.161204         12.409012        13.616133  \n",
      "2                     1.761851         27.505229        37.101615  \n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Funkcja do agregacji\n",
    "def aggregate_by_embarked(df):\n",
    "    # Grupowanie przez `embarked`\n",
    "    aggregated_data = df.groupby('embarked').agg({\n",
    "        'age': ['mean', 'std'],\n",
    "        'family_size': ['mean', 'std'],\n",
    "        'fare': ['mean', 'std']\n",
    "    }).reset_index()\n",
    "\n",
    "    # Zmiana nazw kolumn\n",
    "    aggregated_data.columns = ['port', 'wiek_mean', 'wiek_std',\n",
    "                               'liczba_czlonkow_rodziny_mean', 'liczba_czlonkow_rodziny_std',\n",
    "                               'cena_biletu_mean', 'cena_biletu_std']\n",
    "    return aggregated_data\n",
    "\n",
    "# Agregacja danych\n",
    "aggregated_data_embarked = aggregate_by_embarked(df)\n",
    "\n",
    "# Wyświetlenie wyników agregacji\n",
    "print(\"Agregacja danych ze względu na port zaokrętowania:\")\n",
    "print(aggregated_data_embarked)\n",
    "\n",
    "# Lista cech do analizy\n",
    "features_embarked = ['wiek', 'liczba_czlonkow_rodziny', 'cena_biletu']\n",
    "\n",
    "# Liczba wierszy i kolumn dla wykresów\n",
    "n_cols = 3\n",
    "n_rows = int(np.ceil(len(features_embarked) / n_cols))\n",
    "\n",
    "# Tworzenie wykresów średnich wartości z odchyleniami standardowymi\n",
    "fig, axes = plt.subplots(n_rows, n_cols, figsize=(20, n_rows * 5))\n",
    "\n",
    "# Jeśli mamy tylko jeden wiersz, `axes` będzie jednowymiarowe\n",
    "if n_rows == 1:\n",
    "    axes = np.expand_dims(axes, axis=0)\n",
    "\n",
    "for i, feature in enumerate(features_embarked):\n",
    "    row = i // n_cols\n",
    "    col = i % n_cols\n",
    "    ax = axes[row, col]\n",
    "    sns.barplot(data=aggregated_data_embarked, x='port', y=f'{feature}_mean',\n",
    "                yerr=aggregated_data_embarked[f'{feature}_std'].values, ax=ax, palette='viridis')\n",
    "    ax.set_title(f'Średnia wartość {feature} z odchyleniem standardowym według portu')\n",
    "    ax.set_xlabel('Port')\n",
    "    ax.set_ylabel(f'Średnia wartość {feature}')\n",
    "    ax.grid(False)  # Usunięcie siatki\n",
    "\n",
    "# Usuwanie pustych wykresów\n",
    "for j in range(i + 1, n_rows * n_cols):\n",
    "    fig.delaxes(axes.flatten()[j])\n",
    "\n",
    "# Ustawienie odstępów między wykresami\n",
    "plt.tight_layout()\n",
    "\n",
    "# Wyświetlenie wykresów\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Podsumowanie:\n",
    "\n",
    "- **Średnia wartość wieku**- osoby zaokrętowane w porcie Cherbourg mają nieco wyższą średnią wieku niż osoby zaokrętowane w portach Queenstown i Southampton. Odchylenie standardowe jest największe w porcie Cherbourg, co wskazuje na większą różnorodność wiekową pasażerów zaokrętowanych w tym porcie.\n",
    "- **Średnia wartość liczby członków rodziny**- średnia liczba członków rodziny jest najwyższa dla osób zaokrętowanych w porcie Southampton, natomiast najniższa dla portu Queenstown. Odchylenie standardowe jest znaczące we wszystkich portach, co sugeruje dużą zmienność liczby członków rodziny niezależnie od portu zaokrętowania.\n",
    "- **Średnia wartość ceny biletu**- osoby zaokrętowane w porcie Cherbourg miały średnio najwyższą cenę biletu, podczas gdy osoby zaokrętowane w porcie Queenstown miały najniższą średnią cenę biletu. Odchylenie standardowe ceny biletu jest największe w porcie Cherbourg, co sugeruje dużą różnorodność cen biletów wśród pasażerów zaokrętowanych w tym porcie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agregacja danych ze względu na liczbę członków rodziny:\n",
      "   family_size  wiek_mean   wiek_std  cena_biletu_mean  cena_biletu_std\n",
      "0          0.0  30.619469  10.687977         21.741707        41.405247\n",
      "1          1.0  32.143617  13.437696         51.228106        63.497471\n",
      "2          2.0  26.672431  16.708730         41.566062        44.996721\n",
      "3          3.0  20.220933  14.132646         51.186047        43.718696\n",
      "4          4.0  24.727273  16.248610         80.067432       101.313185\n",
      "5          5.0  20.120000  18.448848         87.081000       101.036027\n",
      "6          6.0  17.375000  15.396428         28.396094         8.020605\n",
      "7          7.0  18.000000  15.175168         46.900000         0.000000\n",
      "8         10.0  26.772727   4.070403         69.550000         0.000000\n"
     ]
    },
    {
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t2rTRq6++qhs3bmjVqlXav3+/8uXLF+e6CQkJ0cSJE3XgwAFrPdm0aZNcXFy0adMm9e/f31omPXhEanwOHDigypUrK3v27Hrrrbfk5eWl77//Xs2aNdOiRYvUvHnzOD93+fJl1a5dW2FhYdqwYUO8sUqKtQ6lB48QunjxovU3i8uFCxdUqVIl3bp1S/3791fGjBn1xRdfqEmTJlq4cKE1Nkfq8qpVq9SoUSNlzZpVr776qgIDA3Xo0CH9/PPPNif2o6KiVLduXZUvX14ffPCBVq9erQ8//FD58uVTr169ZLFY9NJLL2nChAkKCwuTv7+/9bM//fSTrl+/rpdeeine7+boeu/Xr58yZMig4cOH6+TJk5o0aZL69u2r+fPnx7uM6OhoNWnSRJs3b1bPnj1VsGBB7du3TxMnTtRff/2lJUuW2Ey/adMmLV26VH369JEkjR07Vo0aNdKbb76p6dOnq3fv3rp69aomTJigrl27xttWxHjllVe0cOFC9e3bV4UKFdKVK1e0efNmHTp0SKVKldKQIUN07do1/fvvv9btJqY+XL9+XbNnz1bbtm3Vo0cP3bhxQ3PmzFHdunX122+/qUSJEjbL+uabb3Tjxg29/PLLslgsmjBhglq0aKHjx49br6BfuXKlWrZsqUKFCmns2LG6cuWKunTpohw5csSKffLkyWrSpInat2+vu3fv6rvvvtMLL7ygn3/+2druxIirHT5//ryqV6+u+/fvW3/fWbNmxXkF+YgRIzRy5EjVqlVLvXr10pEjRzRjxgzt3LlTW7ZskZubm0JCQhQdHa3NmzerUaNG1t8rZpuPsWfPHt28eVNVq1ZNVB0dO3as0qZNq7feektHjx7Vxx9/LDc3N7m4uOjq1asaMWKEtm/frs8//1x58uTRsGHD4v39ExNzjJdfflmff/65unTpov79++vEiROaOnWq9uzZY10nkjRs2DC9++67atCggRo0aKDdu3erTp061k7Zo7p27aqQkJBYJ0cTEh0drcaNG+u3335Tr169FBwcrB9//FGdOnWyex726Natmz7//HPVr19f3bt31/3797Vp0yZt375dZcqU0csvvxzrKubly5dr3rx5sZICY8aMkcVi0f/+9z9dvHhRkyZNUq1atfTHH38obdq06tChg0aNGqX58+erb9++1s/dvXtXCxcuVMuWLRO8avjPP/9USEiI3Nzc1LNnT+XOnVvHjh3TTz/9pDFjxiggICBWu3/v3j299tprsa6Cvnz5skqVKqXvvvtOderUUffu3fXFF1+oVatWev3117Vjxw6NHTtWhw4dipX0PXr0qFq1aqVu3bqpU6dO+uyzz9S5c2eVLl1ahQsXjjP2uPZdt2/fVrVq1XT06FH17dtXefLk0YIFC9S5c2eFh4db9w0hISH69ttvrfMKCwvTgQMHrHW6WLFikh7U84CAABUsWDDedRhj9OjRcnd316BBgxQZGSl3d3dr3S9btqzGjh2rCxcuaPLkydqyZYv27NljTQzZ26YNGTJE3bt3tyn7+uuvtWLFilh151GPO/bz9vZW8+bNNX/+fH300UdydXW1fvbbb7+VMUbt27d/7HoAgITQR/4/9JHj7yNLDy4q7tq1qwoXLqzBgwcrffr02rNnj5YvX269Q33t2rWqX7++SpcureHDh8vFxcWaDNu0aZPKlStnM8/WrVsrT548Gjt2rHbv3q3Zs2crc+bMGj9+vHWaGTNmqHDhwmrSpInSpEmjn376Sb1791Z0dLS1b+WI1q1bK3fu3Bo7dqy2b9+uKVOm6OrVq3ZdAPgwe75rixYt9Ndff+nbb7/VxIkTlSlTJklSQEBAnBdROmLSpEnWCzNiTJw4UX/88YcyZswY7+fsPTYzxqhp06bavHmzXnnlFRUsWFA//PBDnMfpjzt+fdjjfvMOHTqoR48esdqknTt36q+//tI777wT73ezty2KMW7cOLm4uGjQoEG6du2aJkyYoPbt22vHjh3xLkOyvz8lPTimbteunV5++WW99NJL+uCDD9S4cWPNnDlTb7/9tnr37i3pQV+xdevWj32M/4gRIzR27Fh1795d5cqV0/Xr1/X7779r9+7dql27tl5++eV42xLJsb745s2btXjxYvXu3Vs+Pj6aMmWKWrZsqdOnT1vr2L59+1SnTh0FBARoxIgRun//voYPH64sWbLEWrYj2/KRI0fUtm1bvfzyy+rRo4eef/55h/Yh9hzzV6lSRRaLRRs3brTpa7i4uGjz5s3WeV26dEmHDx+29u0craPz5s3T3bt31a9fP4WFhWnChAlq3bq1atSoofXr1+t///uf9fzAoEGD4ryoLEZiY5Ye9GOHDh2q1q1bq3v37rp06ZI+/vhjVa1a1aYfNGfOHL388suqVKmSBgwYoOPHj6tJkyby9/dXzpw5Y8U0dOhQ5cyZ05oMtVfnzp31/fffq0OHDqpQoYI2bNgQqw4+qZEjR2rEiBGqVKmSRo0aJXd3d+3YsUNr165VnTp11KJFC+XPn9/mM7t27dKkSZNi9eW+/PJL3bhxQ3369NGdO3c0efJk1ahRQ/v27VOWLFnUqlUr9enTR/PmzVPJkiVtPjtv3jxVq1ZN2bNnjzfWs2fPqly5cgoPD1fPnj0VHBysM2fOaOHChbp165bc3d0dOv9bunRpDR06VD179rTrvFiMxBx73b59W02bNtXvv/+u1atXq2zZsnYfu4SEhOjHH3/U9evX5evrK2OMtmzZYj0P0KRJE0n/V88rV64c7zqMMXfuXN25c0c9e/aUh4eH/P39tXr1atWvX1958+bViBEjdPv2bX388ceqXLmydu/ebb2gw942zZFzSI9K7nPi+I8YIAVZuXKlcXV1Na6urqZixYrmzTffNCtWrDB3796NNa0k4+LiYg4cOGBT3q1bN5M1a1Zz+fJlm/I2bdoYPz8/c+vWLWOMMZMmTTKSzPfff2+dJiIiwuTPn99IMuvWrbOWh4aGGknmyy+/tJZFRkaawMBA07JlS2vZiRMnjCQzd+5ca1nM8h727bffGklm48aNCa6P999/37i6uprr168bY4yZMmWKCQoKMuXKlTP/+9//jDHGREVFmfTp05vXXnvN4WUOHz7cSDJt27aNNX2fPn1MXE3IkiVLjCTz7rvv2pS3atXKWCwWc/ToUWOMMRMnTjSSzKVLl+L9fp999pmRZD766KNY70VHR8f7uYsXLxpJZvr06cYYY8LDw42Li4t54YUXTJYsWazT9e/f3/j7+1vnFdfvU7NmTVO0aFFz584dm2VXqlTJFChQwFo2d+5cI8ns3LnTnDt3zhQuXNjkzZvXnDx5Mt444zNhwoRY9SkuAwYMMJLMpk2brGU3btwwefLkMblz5zZRUVHGGPvr8v37902ePHlMUFCQuXr1qs2yHl7fnTp1MpLMqFGjbKYpWbKkKV26tPX1kSNHjCQzY8YMm+maNGlicufOnaTrvVatWjYxvvbaa8bV1dWEh4dby0JDQ01oaKj19VdffWVcXFxs1p8xxsycOdNIMlu2bLGWSTIeHh7mxIkT1rJPPvnESDKBgYHWbdAYYwYPHmwk2UwbFz8/P9OnT58Ep2nYsKEJCgqKVX7//n0TGRlpU3b16lWTJUsW07VrV2tZzLrNmDGjCQsLs5b/+OOPRpL56aefrGUlSpQwWbNmtVlnK1euNJJixfBoG3L37l1TpEgRU6NGDZvy+NrhmLq7Y8cOa9nFixeNn5+fzbq7ePGicXd3N3Xq1LHWZ2OMmTp1qpFkPvvsM2PMg3bO19fXvPnmm8aYB3UlY8aM5oUXXjCurq7mxo0bxhhjPvroI+Pi4mKt3/bW0XXr1hlJpkiRIjb7m7Zt2xqLxWLq169v8/mKFSvG+bs9LLExb9q0yUgy8+bNs5nf8uXLbcpj1l3Dhg1tto23337bSDKdOnWyln311VdGkmnTpo31t310e4nRqVMnm++2aNEiI8lMmjTJ5rvVqFEj1nYdl5h1+/A+9dFlrF271kgy/fv3j/X5+PYFf//9t/Hz8zO1a9c29+/ft1lW9uzZbbbZ77//3kgykydPtpZVrFjRlC9f3maeixcvjhVrXKpWrWp8fHzMqVOn7IrVGGN69+5tXF1dzdq1a40xD9Zh7dq1jbu7u/n222+NMcb88ccfRpLp3r27zWcHDRpkJFk/a4wxQUFBsfbpFy9eNB4eHub111+3ltmz74rZh3z99dfWsrt375qKFSsab29v67pcsGCBkWQOHjxojDFm6dKlxsPDwzRp0sS8+OKL1s8WK1bMNG/ePMF1GPNb5c2b16a9uXv3rsmcObMpUqSIuX37trX8559/NpLMsGHDrGWOtGkP27Jli3Fzc7NpS+Oqp/Ye+61YscJIMsuWLbNZTrFixeLcxgDAUfSRbdFHjlt4eLjx8fEx5cuXt9mHPvy56OhoU6BAAVO3bl2bed26dcvkyZPH1K5d21oWsx4e3l8aY0zz5s1NxowZbcriWrd169Y1efPmjTfeuMQss0mTJjblvXv3NpLM3r17rWVBQUE2x7uP7ssd+a7vv/9+nP27uOpuDElm+PDhDn2/mGPSR/vZj7L32Cym3k2YMME63f37901ISEisuO05frX3Nw8PDzeenp7W7S1G//79jZeXl7l586a17NH1ZG9bFPN7FixY0KZfPHnyZCPJ7Nu3z1r2aN/C3v6UMf93TL1161ZrWcyxXdq0aW3WV8z5gcf1FYoXL24aNmyY4DTxtSXGONYXd3d3t7Yvxhizd+9eI8l8/PHH1rJmzZoZT09Pm+9y8OBB4+rqGisGe7flmPW2fPlym3J79yGOHPMXLlzYtG7d2vq6VKlS5oUXXjCSzKFDh4wx/9ePi2kj7K2jMdt4QECATZ8i5pxP8eLFzb1796zlbdu2Ne7u7jbnseKSmJhPnjxpXF1dzZgxY2zmtW/fPpMmTRprecy6K1GihM22MWvWLCPJpv8Rsy3UqFHDus09ur3EiNn+Y+zatctIMgMGDLCZrnPnzna1f3G1n48u4++//zYuLi6mefPmNueDjIl/f3fp0iWTK1cuU7Ro0Vi/Y9q0ac2///5rnXbHjh1Gks2xQNu2bU22bNlslrd79267zm107NjRuLi4mJ07d8Z6L7544zr/261bNyPJTJw40Rhj/3kxY+w/9oppQxcsWGBu3LhhQkNDTaZMmcyePXus09h77LJz504jyfz666/GGGP+/PNPI8m88MILNudUmjRpYkqWLBnv+jPm/34rX19fc/HiRZv3SpQoYTJnzmyuXLliLdu7d69xcXExHTt2tJY50qY9LK5zSHHV06Q+Jw7n4VHqSFFq166tbdu2qUmTJtq7d68mTJigunXrKnv27HE+2jo0NFSFChWyvjbGaNGiRWrcuLGMMbp8+bL1r27durp27Zr1Mca//vqrsmbNajOeZrp06dSzZ884Y/P29ra52sfd3V3lypXT8ePHE/xOD98heefOHV2+fFkVKlSQJGss8QkJCVFUVJT1qr5NmzZZxxmLueNw//79Cg8Ptxl7zNFlvvLKKwnG8bBff/1Vrq6u1ruyY7z++usyxlgfmRJzJeOPP/4Y7+OTFy1apEyZMqlfv36x3ovvLgHpwdXbwcHB1jsFtmzZIldXV73xxhu6cOGC/v77b0kP1lfM1ZpxCQsL09q1a9W6dWvduHHDWleuXLmiunXr6u+//471GJ1///1XoaGhunfvnjZu3KigoKB444zLunXrNHjwYPXr108dOnRIcNpff/1V5cqVU5UqVaxl3t7e6tmzp06ePGl91Iy9dXnPnj06ceKEBgwYEOtRjHGto0frRUhIiE19f+6551S+fHnNmzfPWhYWFqZly5apffv2Sbree/bsaTO/mG0j5rHQcVmwYIEKFiyo4OBgm7agRo0akhTrseQ1a9a0eaRU+fLlJUktW7aUj49PrPLHbfvp06fXjh07dPbs2QSni4urq6v1ztLo6GiFhYXp/v37KlOmTJzb8IsvvqgMGTJYX8e0BzExnjt3Tn/88Yc6deokPz8/63S1a9e2aUNjPNyGXL16VdeuXVNISEicy360HZYe1MkKFSrY3HESEBAQ687J1atX6+7duxowYIDNVfc9evSQr6+vddgAFxcXVapUybrNHzp0SFeuXNFbb70lY4y2bdsm6cE2X6RIEWv9drSOduzY0eZK3PLly8sYo65du9pMV758ef3zzz+6f/9+rPURI7ExL1iwQH5+fqpdu7ZNvS1durS8vb2t9TZm3cUMIxHj0bssZs6cqS5dukh6cMWso+O+LV++XG5uburRo4fNd0vM3T/xiXm03PDhw2O9F1c7EhERoebNmytDhgz69ttvbe7SlR78jg9vs61atVLWrFn166+/2kyzY8cOm0eSzps3Tzlz5lRoaGi8sV66dEkbN25U165dlStXrsfGKj24cn369OmaMGGCqlevroiICDVs2FCrVq1S1qxZ1aZNG0myxvfw42GlB/tXSbGG0ShUqJDNvj8gIEDPP/98nG1TQvuuX3/9VYGBgWrbtq21zM3NTf3799fNmze1YcMGSf/XrsTU6U2bNqls2bKqXbu29ZgkPDxc+/fvt3s81E6dOtnUyd9//10XL15U7969be7ab9iwoYKDg63rwNE2Lcb58+fVqlUrlShRQtOnT39sfPYc+9WqVUvZsmWzaWf279+vP//8k6vEASQJ+si26CPHbdWqVbpx44beeuutWE++ifncH3/8ob///lvt2rXTlStXrPUgIiJCNWvW1MaNG2PFFVef8MqVK9bhTyTbdXvt2jVdvnxZoaGhOn78uK5duxZvzPF59DgzZl08fCz3OIn5rsnp4MGD6tq1q5o2bZrgHdWS/cdmv/76q9KkSaNevXpZp3N1dY1Vdxw9fn3cb+7n56emTZtan44jPbi7bv78+dZHaMfFkbYoRpcuXWyeuPRoPzcu9vanYhQqVEgVK1a0vo7p79eoUcNmfTlyHuDAgQPW81KOcqQvXqtWLZunSBQrVky+vr7WGKOiorRixQo1a9bM5rsULFgwzkfeO7It58mTJ9Y87N2H2HvML8mmbb9x44b27t2rnj17KlOmTNbyTZs2KX369Na7wx2toy+88IJNnyLmt37ppZdsxsQuX7687t69+9jHbScm5sWLFys6OlqtW7e2qbeBgYEqUKCAtd7GrLtXXnnFZtuIGVIhxo8//mi9e7hDhw4JPqUiLjGP5I95YkKMuPZNibVkyRJFR0dr2LBhsZ7CEFfbFBUVpbZt2+rGjRv64YcfYv2OzZo1s7nju1y5cipfvnys8wBnz561aQfmzZuntGnTqmXLlvHGGh0drSVLlqhx48YqU6ZMrPfjivfR87/R0dHq3Lmz5syZI+n/zt3Ye14shiPHXteuXVOdOnV0+PBhrV+/3ubpk/Yeu8Q8fefh8wA5cuRQx44dtXv3bt26dUvGGG3evNnu8wAtW7ZUQECA9XVM/75z5842d18XK1ZMtWvXtv6GjrZpMR53DikuyXVOHP8dEuNIccqWLavFixfr6tWr+u233zR48GDduHFDrVq1siYCY+TJk8fm9aVLlxQeHq5Zs2YpICDA5i8mMXDx4kVJ0qlTp5Q/f/5YDdXzzz8fZ1w5cuSINW2GDBlijVH0qLCwML366qvKkiWL0qZNq4CAAGvcj+sklipVSunSpbM5cAoJCVHVqlX1+++/686dO9b3Hk6eOrrMR9djQk6dOqVs2bLZJB0kWR+ZGpOofPHFF1W5cmV1795dWbJkUZs2bfT999/bdD6PHTum559/3uZA014PH2hu2rRJZcqUUZkyZeTv769Nmzbp+vXr2rt3b4I75aNHj8oYo6FDh8aqLzEJmpj6EqNDhw66ePGiNmzYkOAjduLy77//WtfLRx999NjpT506FWd9fHRd21uXYxJA9jyCMWaM34fFVd87duyoLVu2WGNZsGCB7t27l2DSPzHr/dEOfEwSOKHt7++//9aBAwdiLeO5556zaxkxnYpHH0UVU/64bX/ChAnav3+/cubMqXLlymnEiBGP7UQ/7IsvvlCxYsWs45IFBATol19+iXMbftz6ifl9ChQoEOuzcdWxn3/+WRUqVJCnp6f8/f0VEBCgGTNm2N1+nDp1yq5lxcT1aLm7u7vy5s1rc+FDSEiIdu3apdu3b2vTpk3KmjWrSpUqpeLFi1vbgrgOxB2po47Ugejo6Me24YmJ+e+//9a1a9eUOXPmWHX35s2bNvswKfZvGhAQYHORRNq0aTVq1KgE40zIqVOnlDVrVqVLl86m/NHHmT2JY8eOKVu2bDYdoIT06NFDx44d0w8//BBnB//RdWKxWJQ/f36bMb1efPFFeXh4WDsx165d088///zYDkzMNmxPOyo9OCn7yiuvqG3bttaEt6enp0qUKBFryIhTp07JxcUl1roNDAxU+vTpY10I9Gh9leI/Lklo3xWzvT56MuLRfU2WLFlUoECBOI9Jzp49q+PHj2vLli2Kjo62u0P8aPsRX5sgScHBwTb7Pcn+Nk16MH5769atFRUVpcWLF8vDw+Ox8dlz7Ofi4qL27dtryZIl1vE3582bJ09PT73wwguPXQYA2IM+8v+hjxw3e/p6MYm6Tp06xaoLs2fPVmRkZKx1YU8/bMuWLapVq5Z1TNCAgADruOmJSYw/un/Ply+fXFxcYo3PmpDEfNfkcv36dbVo0ULZs2fXl19++diT5fYem8Ucpz/6eN5Ht1dHj1/t+c07duyo06dPW7e11atX68KFCwmeB3CkLXIklkfZ25+KbxlPeh5g1KhRCg8P13PPPaeiRYvqjTfesBlv+nEc6Ys/rj9w6dIl3b592+5jZke25fjOA9izD7H3mF960Kc+d+6cjh49qq1bt8pisahixYqxzglWrlzZZptxpI4mdR1ITMx///23jDEqUKBArHp76NChx54HcHNzU968eW1ePzpMgiNi+qaP/s5JfR7AxcUlwQubH/bOO+9o7dq1+uabb+IcViSuev7cc8/Z7Dtq166trFmzWs8DREdH69tvv1XTpk1j7cMfdunSJV2/ft3udjSu878x6/PRC0UcOS8mOXbsNWDAAO3cuVOrV6+ONdyavccurq6uqlixYqzjripVqigqKkrbt2/XwYMHFRYWliznAQoWLGi9sM3RNi3G484hPSo5z4njv8MY40ix3N3dVbZsWZUtW1bPPfecunTpogULFtjcUfbonW8xHcqXXnop3vFPY8Z4cVR8VxPFXH0Yn9atW2vr1q164403VKJECXl7eys6Olr16tV77BXKbm5uKl++vDZu3KijR4/q/PnzCgkJUZYsWXTv3j3t2LFDmzZtUnBwsE2D7egyHb2D0B5p06bVxo0btW7dOv3yyy9avny55s+frxo1amjlypV2XZ2VkCpVqujTTz/V8ePHrTtli8WiKlWqaNOmTcqWLdtjT87HrItBgwbFe2XZowd9LVq00JdffqnJkydr7Nixdsd79+5dtWrVSh4eHvr+++8TdTHAf8ne36dNmzZ67bXXNG/ePL399tv6+uuvVaZMmQQPSBKz3hOz/UVHR6to0aLxXoTwaCcnvmU8ybYfEhKiH374QStXrtT777+v8ePHa/HixQmOuSg9GP+2c+fOatasmd544w1lzpxZrq6uGjt2rM0drk8aY1xixgiqWrWqpk+frqxZs8rNzU1z586Nc2zq5Gg/4lKlShXdu3dP27Zts27z0v9dJHP48GFdunQp1jbvSB1N6jqQmJijo6OVOXNmm6tOH/bowfnjdOrUSevXr49VbrFY4ow/KirKofn/1yZPnqxvv/1WX3/9tc3Vzo7KkCGDGjVqpHnz5mnYsGFauHChIiMjk/QO36tXr6ply5Z67rnnNHv2bGt5zLbcuXPnOD9n75XFjtTLxO67HlWlShWtWbNGt2/f1q5duzRs2DDrEw82bdqkQ4cOydvbO9aYbfH5r9oPSXrjjTe0bds2rV69OtY45PGxdx137NhR77//vpYsWaK2bdvqm2++UaNGjWzu2gCApEAfmT7yk4j5nu+//368x1GPJlkf9xsfO3ZMNWvWVHBwsD766CPlzJlT7u7u+vXXXzVx4sQkuSs7MXddJea72rtcR4+XO3furLNnz+q3336Tr6+vQ591Bnu267p16ypLliz6+uuvVbVqVX399dcKDAyMNZ7rwxLTFiX2PIAj/amk7gNWrVpVx44d048//qiVK1dq9uzZmjhxombOnKnu3bsn+FlH++JJeR7A0W35vzwPID14atXx48dVqlQpeXl5KSQkRFOmTNHNmze1Z8+eWElgR+pocpwHcDTm6OhoWSwWLVu2LM7lPq69elSDBg108uRJvfrqqzblSdWu/deWLFmi8ePHa/To0apXr16i5+Pq6qp27drp008/1fTp07VlyxadPXs2Sc8DJHT+d/jw4RoxYsQTzd+Retm0aVN99913GjdunL788stYF1zZq0qVKhozZoz14sMhQ4ZYn3iwadMm6/je9ibG/8vzAIk5h5Sc58Tx33m6My+AnWIeU3Lu3LkEpwsICJCPj4+ioqISPCCXpKCgIO3fv1/GGJsDgyNHjjx5wP/f1atXtWbNGo0cOVLDhg2zljvySKWQkBCNHz9eq1evVqZMmRQcHCyLxaLChQtr06ZN2rRpkxo1apSky5TiP1gKCgrS6tWrdePGDZuryg4fPmx9P4aLi4tq1qypmjVr6qOPPtJ7772nIUOGaN26ddZHPu3YsUP37t2zeXyxPWJ2tqtWrdLOnTv11ltvSXrQCZkxY4ayZcsmLy8vlS5dOt55xFxN6ebm9tj6EqNfv37Knz+/hg0bJj8/P+tyH6d///76448/tHHjRusBw+MEBQXFWR8fXdf21uWYKyr3799v9/d9HH9/fzVs2FDz5s1T+/bttWXLFk2aNCnBzyRmvSdGvnz5tHfvXtWsWdNpj7DJmjWrevfurd69e+vixYsqVaqUxowZY02MxxfXwoULlTdvXi1evNhmmrgeNW2PmLoSVzvwaD1ZtGiRPD09tWLFCps7KufOnevQ8uxZVkxcR44csbm6+e7duzpx4oRN/ShXrpzc3d2t7d4bb7wh6cE2/+mnn2rNmjXW1w9LTB1NKomJOV++fFq9erUqV66cYGfh4d/04XV36dKlx17BLj1IDMf1BINHr0YOCgrSunXrdOvWLZu7xo8ePfrYZdgrX758WrFihcLCwhK8a3zTpk0aNGiQBgwYEOux/A97tO4ZY3T06NFYJ9o6duyopk2baufOnZo3b55KliwZ6yrqR8Ws6/379yc4XXR0tNq3b6/w8HCtXr061h33cQkKClJ0dLT+/vtv61XaknThwgWFh4c7PHTHwxLadwUFBenPP/9UdHS0TUc5rv16SEiI5s6dq++++05RUVGqVKmSXFxcrBelHTp0SJUqVUr0if2H24SYYS9iHDlyxGa/J9nXpknSd999p0mTJmnSpEkJPio/sYoUKaKSJUtq3rx5ypEjh06fPq2PP/44yZcDAA+jj0wf+WEP9/Xiu6MvZhpfX98k64f99NNPioyM1NKlS23uunz0cdWO+Pvvv23uJjt69Kiio6Nthr16HEe+a3y/a8zdyeHh4TblCQ3n9ahx48ZpyZIlWrx4sYKDg+36jL3HZkFBQVqzZo1u3rxpkzR7dHu19/jVETHJpc8//1zjx4/XkiVL1KNHjwSPAR1pi56Evf2p5OTv768uXbqoS5cuunnzpqpWraoRI0ZYE+Px1bmk6Is/LCAgQGnTprXrmDkptmV79yH2HvNLD+7mzpUrlzZt2qTjx49bzwVWrVpVAwcO1IIFCxQVFRXrPEBi6mhSSUzM+fLlkzFGefLksT7lMC4P94MeXnf37t3TiRMnVLx48QRjy5AhQ6w2TYr7PEB0dLROnDhhc3duUp8HiI6O1sGDBxNMWP7111/q1KmTmjVrZn2CQVziqud//fVXrH1Hx44d9eGHH+qnn37SsmXLFBAQkOBjuKUH25Kvr69d7aij538dOS/mqGbNmqlOnTrq3LmzfHx8NGPGDJvl2nvsEhISort37+rbb7/VmTNnbOp0TGL8ueees/t896MeXgePOnz4sDJlyiQvLy95enra3aZJ9p9DSixnnm/E4/EodaQo69ati/MKp5ixJB53xY2rq6tatmypRYsWxbmzunTpkvX/Bg0a6OzZs1q4cKG17NatW5o1a1Ziw48zHin2VVuONJIhISGKjIzUpEmTbMbLDgkJ0VdffaWzZ8/aXJGVFMuUZB2r5dEDpgYNGigqKkpTp061KZ84caIsFos14RcWFhZrnjEHOpGRkZIejCly+fLlWPOKK/5H5cmTR9mzZ9fEiRN17949Va5cWdKD9XLs2DEtXLhQFSpUSPDO7MyZM6tatWr65JNP4jyh9HB9edjQoUM1aNAgDR482OagIj5z587VJ598omnTptmMufw4DRo00G+//WYdi1h6MC7KrFmzlDt3buvjhuyty6VKlVKePHk0adKkWL9rYq4ojtGhQwcdPHhQb7zxhlxdXa3j5cYnsevdUa1bt9aZM2f06aefxnrv9u3bioiISJLlxCUqKirWo8YyZ86sbNmyWeu/9GA7i+uRaHFtxzt27LCpC47ImjWrSpQooS+++MJmeatWrYr1+E1XV1dZLBabK4ZPnjypJUuW2L28Bg0aaPv27frtt9+sZZcuXYp11X6tWrXk7u6uKVOm2HzXOXPm6Nq1a2rYsKG1zNPTU2XLltW3336r06dP29x9ffv2bU2ZMkX58uVT1qxZY8XjaB1NKomJOeZRz6NHj441v/v371u33Vq1asnNzU0ff/yxzbqzt63Ply+f9Y71GHv37tWWLVtspqtbt67u3btnsx1FR0dr2rRpdi3HHi1btpQxRiNHjoz1Xsx3O3funFq3bq0qVaro/fffT3B+X375pW7cuGF9vXDhQp07dy7Wkxrq16+vTJkyafz48dqwYYNdV4kHBASoatWq+uyzz3T69Ok4Y5WkkSNHasWKFfr222/tfhRrgwYNJMX+DWOeevHw9pAY8e27GjRooPPnz2v+/PnWsvv37+vjjz+Wt7e3TSI5pg6PHz9exYoVs94RHRISojVr1uj333+3+yrxuJQpU0aZM2fWzJkzbdrKZcuW6dChQ9Z14Eibtn//fnXv3l0vvfRSrDsmklKHDh20cuVKTZo0SRkzZnzsk0EAwF70kWOjjxxbnTp15OPjo7Fjx+rOnTtxfq506dLKly+fPvjgA928eTPWPBLTD4tr3V67di3RiTxJsY4zYy42c2Tf6sh3je939fX1VaZMmazjqsaYPn26XTGsXr1a77zzjoYMGaJmzZrZHbu9x2YNGjTQ/fv3bY7roqKiYl2cZ+/xq6M6dOigq1ev6uWXX9bNmzcfeyztSFv0JOztTyWXK1eu2Lz29vZW/vz5Y50HkGLXuaToiz86v7p162rJkiU2v/2hQ4e0YsWKWNNKT7Yt27sPsfeYP0ZISIjWrl2r3377zdq2lyhRQj4+Pho3bpzSpk0b500xjtbRpORozC1atJCrq6tGjhwZa7s0xljrVZkyZRQQEKCZM2fq7t271mk+//xzu+p2vnz5dO3aNZvH+587d04//PCDzXQxieJH27ukvPi3WbNmcnFx0ahRo2I9kSBmHdy8eVPNmzdX9uzZ9cUXXyR408uSJUtsxn//7bfftGPHjlj7jmLFiqlYsWKaPXu2Fi1apDZt2jz2qZ4uLi5q1qyZfvrpJ/3++++x3o+JNzHnfx05L5YYHTt21JQpUzRz5kz973//s5bbe+wiSeXLl5ebm5vGjx8vf39/6w0FISEh2r59uzZs2PBE5wEe7t8/XI/379+vlStXWs+VONKmOXIO6Uk463wjHo87xpGi9OvXT7du3VLz5s0VHBysu3fvauvWrZo/f75y585tHXcoIePGjdO6detUvnx59ejRQ4UKFVJYWJh2796t1atXWzujPXr00NSpU9WxY0ft2rVLWbNm1VdffWXXXV328vX1VdWqVTVhwgTdu3dP2bNn18qVK3XixAm751GxYkWlSZNGR44csRmHJObOaMn2USVJsUxJ1gO0/v37q27dutbGvXHjxqpevbqGDBmikydPqnjx4lq5cqV+/PFHDRgwwHpl9qhRo7Rx40Y1bNhQQUFBunjxoqZPn64cOXJYHyvUsWNHffnllxo4cKD1YDEiIkKrV69W79691bRp0wRjDAkJ0XfffaeiRYtar+aOeUTRX3/9pXbt2j32e06bNk1VqlRR0aJF1aNHD+XNm1cXLlzQtm3b9O+//2rv3r1xfu7999/XtWvX1KdPH/n4+MR7gH358mX17t1bhQoVkoeHh77++mub95s3b27tFD3qrbfe0rfffqv69eurf//+8vf31xdffKETJ05o0aJF1qvH7a3LLi4umjFjhho3bqwSJUqoS5cuypo1qw4fPqwDBw7EOoiwV8OGDZUxY0YtWLBA9evXV+bMmR/7mcSud0d06NBB33//vV555RWtW7dOlStXVlRUlA4fPqzvv/9eK1assN5pk9Ru3LihHDlyqFWrVipevLi8vb21evVq7dy5Ux9++KF1utKlS2v+/PkaOHCgypYtK29vbzVu3FiNGjXS4sWL1bx5czVs2FAnTpzQzJkzVahQoThP7Nhj7NixatiwoapUqaKuXbsqLCxMH3/8sQoXLmwzz4YNG+qjjz5SvXr11K5dO128eFHTpk1T/vz57R4b7c0339RXX32levXq6dVXX5WXl5dmzZplvfshRkBAgAYPHqyRI0eqXr16atKkiY4cOaLp06erbNmysbarkJAQjRs3Tn5+fipatKikBxccPP/88zpy5Ei8j6ZOTB1NKo7GHBoaqpdfflljx47VH3/8oTp16sjNzU1///23FixYoMmTJ6tVq1YKCAjQoEGDNHbsWDVq1EgNGjTQnj17tGzZMmXKlOmxcXXt2lUfffSR6tatq27duunixYuaOXOmChc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      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Funkcja do agregacji\n",
    "def aggregate_by_family_size(df):\n",
    "    # Grupowanie przez `family_size`\n",
    "    aggregated_data = df.groupby('family_size').agg({\n",
    "        'age': ['mean', 'std'],\n",
    "        'fare': ['mean', 'std']\n",
    "    }).reset_index()\n",
    "\n",
    "    # Zmiana nazw kolumn\n",
    "    aggregated_data.columns = ['family_size', 'wiek_mean', 'wiek_std',\n",
    "                               'cena_biletu_mean', 'cena_biletu_std']\n",
    "    return aggregated_data\n",
    "\n",
    "# Agregacja danych\n",
    "aggregated_data_family_size = aggregate_by_family_size(df)\n",
    "\n",
    "# Wyświetlenie wyników agregacji\n",
    "print(\"Agregacja danych ze względu na liczbę członków rodziny:\")\n",
    "print(aggregated_data_family_size)\n",
    "\n",
    "# Lista cech do analizy\n",
    "features_family_size = ['wiek', 'cena_biletu']\n",
    "\n",
    "# Liczba wierszy i kolumn dla wykresów\n",
    "n_cols = 2\n",
    "n_rows = int(np.ceil(len(features_family_size) / n_cols))\n",
    "\n",
    "# Tworzenie wykresów średnich wartości z odchyleniami standardowymi\n",
    "fig, axes = plt.subplots(n_rows, n_cols, figsize=(20, n_rows * 5))\n",
    "\n",
    "# Jeśli mamy tylko jeden wiersz, `axes` będzie jednowymiarowe\n",
    "if n_rows == 1:\n",
    "    axes = np.expand_dims(axes, axis=0)\n",
    "\n",
    "for i, feature in enumerate(features_family_size):\n",
    "    row = i // n_cols\n",
    "    col = i % n_cols\n",
    "    ax = axes[row, col]\n",
    "    sns.barplot(data=aggregated_data_family_size, x='family_size', y=f'{feature}_mean',\n",
    "                yerr=aggregated_data_family_size[f'{feature}_std'].values, ax=ax, palette='viridis')\n",
    "    ax.set_title(f'Średnia wartość {feature} z odchyleniem standardowym według liczby członków rodziny')\n",
    "    ax.set_xlabel('Liczba członków rodziny')\n",
    "    ax.set_ylabel(f'Średnia wartość {feature}')\n",
    "    ax.grid(False)  # Usunięcie siatki\n",
    "\n",
    "# Usuwanie pustych wykresów\n",
    "for j in range(i + 1, n_rows * n_cols):\n",
    "    fig.delaxes(axes.flatten()[j])\n",
    "\n",
    "# Ustawienie odstępów między wykresami\n",
    "plt.tight_layout()\n",
    "\n",
    "# Wyświetlenie wykresów\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Podsumowanie:\n",
    "\n",
    "- **Średnia wartość wieku**- pasażerowie podróżujący samotnie (liczba członków rodziny = 0) mają średnią wartość wieku około 30 lat. Wiek pasażerów z 1-2 członkami rodziny jest nieco wyższy, z najwyższą średnią dla pasażerów z 1 członkiem rodziny. Pasażerowie z większą liczbą członków rodziny (od 3 wzwyż) mają tendencję do niższej średniej wartości wieku, co może sugerować obecność większej liczby dzieci w tych grupach. Największe odchylenie standardowe występuje dla pasażerów z 1 członkiem rodziny, co oznacza dużą różnorodność wiekową w tej grupie.\n",
    "- **Średnia wartość ceny biletu**- pasażerowie podróżujący samotnie zapłacili średnio najniższą cenę za bilet. Cena biletu rośnie wraz ze wzrostem liczby członków rodziny, osiągając najwyższą średnią wartość dla pasażerów z 4 i 5 członkami rodziny. Pasażerowie z 6-7 członkami rodziny płacili średnio mniej, co może wynikać z zakupu tańszych biletów grupowych. Odchylenie standardowe ceny biletu jest największe dla pasażerów z 4 i 5 członkami rodziny, co wskazuje na dużą różnorodność w cenach biletów w tych grupach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agregacja danych ze względu na przedziały wiekowe:\n",
      "   wiek_bin  cena_biletu_mean  cena_biletu_std\n",
      "0   (0, 10]         29.436044        25.088494\n",
      "1  (10, 20]         28.807050        45.942597\n",
      "2  (20, 30]         24.629425        38.029729\n",
      "3  (30, 40]         42.701507        72.449334\n",
      "4  (40, 50]         47.254231        56.579850\n",
      "5  (50, 60]         61.943347        75.476675\n",
      "6  (60, 70]         64.217285        79.973713\n",
      "7  (70, 80]         34.755567        26.987844\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Funkcja do agregacji z binowaniem wieku\n",
    "def aggregate_by_age_bins(df, bins):\n",
    "    # Binowanie wieku\n",
    "    df['age_bin'] = pd.cut(df['age'], bins=bins)\n",
    "\n",
    "    # Grupowanie przez biny wiekowe\n",
    "    aggregated_data = df.groupby('age_bin').agg({\n",
    "        'fare': ['mean', 'std']\n",
    "    }).reset_index()\n",
    "\n",
    "    # Zmiana nazw kolumn\n",
    "    aggregated_data.columns = ['wiek_bin', 'cena_biletu_mean', 'cena_biletu_std']\n",
    "    return aggregated_data\n",
    "\n",
    "# Określenie przedziałów wiekowych\n",
    "age_bins = [0, 10, 20, 30, 40, 50, 60, 70, 80]\n",
    "\n",
    "# Agregacja danych\n",
    "aggregated_data_age_bins = aggregate_by_age_bins(df, age_bins)\n",
    "\n",
    "# Wyświetlenie wyników agregacji\n",
    "print(\"Agregacja danych ze względu na przedziały wiekowe:\")\n",
    "print(aggregated_data_age_bins)\n",
    "\n",
    "# Tworzenie wykresów średnich wartości z odchyleniami standardowymi dla przedziałów wiekowych\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "sns.barplot(data=aggregated_data_age_bins, x='wiek_bin', y='cena_biletu_mean',\n",
    "            yerr=aggregated_data_age_bins['cena_biletu_std'].values, ax=ax, palette='viridis')\n",
    "ax.set_title('Średnia wartość cena biletu z odchyleniem standardowym według przedziałów wiekowych')\n",
    "ax.set_xlabel('Przedziały wiekowe')\n",
    "ax.set_ylabel('Średnia wartość cena biletu')\n",
    "ax.grid(False)  # Usunięcie siatki\n",
    "\n",
    "# Zapisanie wykresów do pliku\n",
    "plt.tight_layout()\n",
    "\n",
    "# Wyświetlenie wykresów\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Podsumowanie:\n",
    "\n",
    "- Średnia wartość ceny biletu jest stosunkowo stała dla młodszych grup wiekowych (0-20 lat) i wynosi około 25-30 jednostek walutowych. Dla osób w wieku 20-30 lat średnia cena biletu jest niższa, co może sugerować, że osoby w tym wieku wybierały tańsze opcje podróży. Od przedziału wiekowego 30-40 lat, cena biletu zaczyna rosnąć, osiągając najwyższe średnie wartości dla osób w wieku 50-70 lat.\n",
    "- Najwyższe średnie wartości ceny biletu obserwuje się w przedziałach wiekowych 50-70 lat, co może sugerować, że starsze osoby miały tendencję do wybierania droższych opcji podróży. W przedziale wiekowym 70-80 lat średnia wartość ceny biletu spada, co może być wynikiem mniejszej liczby pasażerów w tym przedziale wiekowym.\n",
    "- Największe odchylenie standardowe ceny biletu występuje w przedziałach wiekowych 40-50, 50-60 i 60-70 lat. Sugeruje to dużą różnorodność w cenach biletów kupowanych przez osoby w tych grupach wiekowych. Dla najmłodszych grup wiekowych (0-20 lat) oraz najstarszych (70-80 lat), odchylenie standardowe jest mniejsze, co oznacza mniejszą różnorodność cen biletów w tych grupach."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Podsumowanie analizy zależności między zmiennymi- macierz korelacji"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Wybór kolumn numerycznych\n",
    "numerical_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
    "\n",
    "# Tworzenie macierzy korelacji dla kolumn numerycznych\n",
    "correlation_matrix = df[numerical_columns].corr()\n",
    "\n",
    "# Wizualizacja macierzy korelacji za pomocą heatmap\n",
    "plt.figure(figsize=(12, 10))\n",
    "heatmap = sns.heatmap(correlation_matrix, cmap='coolwarm', cbar=True, annot=False, linewidths=0.5, linecolor='gray')\n",
    "\n",
    "# Dodanie wartości korelacji ręcznie do każdego prostokąta\n",
    "for i in range(len(correlation_matrix.columns)):\n",
    "    for j in range(len(correlation_matrix.columns)):\n",
    "        plt.text(j + 0.5, i + 0.5,\n",
    "                 '{:.2f}'.format(correlation_matrix.iloc[i, j]),\n",
    "                 ha='center', va='center', color='black', fontsize=10)\n",
    "\n",
    "# Ustawienie tytułu\n",
    "heatmap.set_title('Macierz korelacji zmiennych numerycznych', fontdict={'fontsize':16}, pad=12)\n",
    "plt.savefig('macierz_korelacji.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Podsumowanie:\n",
    "\n",
    "**Najsilniejsze korelacje**:\n",
    "\n",
    "- Klasa biletu (pclass) i cena biletu (fare)- wartość korelacji: -0.56, silna negatywna korelacja, co oznacza, że pasażerowie z wyższych klas (niższa liczba klasy) płacili wyższe ceny za bilety.\n",
    "- Płeć (sex) i przeżywalność (survived)- wartość korelacji: 0.53, silna pozytywna korelacja, co sugeruje, że kobiety miały większe szanse na przeżycie.\n",
    "\n",
    "**Najsłabsze korelacje**:\n",
    "\n",
    "- Liczba członków rodziny (family_size) i przeżywalność (survived)- wartość korelacji: 0.03, bardzo słaba pozytywna korelacja, co oznacza, że liczba członków rodziny miała minimalny wpływ na przeżywalność.\n",
    "- Wiek (age) i przeżywalność (survived)- wartość korelacji: -0.04, bardzo słaba negatywna korelacja, co oznacza, że wiek miał minimalny wpływ na przeżywalność."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Badanie wartości odstających- nowa zmienna współczynnik przeżywalności"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wybór cech do stworzenia współczynnika przeżywalności\n",
    "features = ['pclass', 'age', 'fare', 'sex', 'embarked', 'family_size']\n",
    "\n",
    "# Tworzenie kopii ramki danych tylko z wybranymi cechami\n",
    "df_survival = df[features].copy()\n",
    "\n",
    "# Skalowanie cech\n",
    "scaler = StandardScaler()\n",
    "df_survival_scaled = scaler.fit_transform(df_survival)\n",
    "\n",
    "# Współczynniki korelacji jako wagi (na podstawie wcześniej obliczonej macierzy korelacji):\n",
    "weights = {\n",
    "    'pclass': -0.31,\n",
    "    'age': -0.04,\n",
    "    'fare': 0.24,\n",
    "    'sex': 0.53,\n",
    "    'embarked': -0.18,\n",
    "    'family_size': 0.03\n",
    "}\n",
    "\n",
    "# Tworzenie współczynnika przeżywalności jako kombinacji liniowej skalowanych cech\n",
    "df['survival_score'] = (weights['pclass'] * df_survival_scaled[:, 0] +\n",
    "                        weights['age'] * df_survival_scaled[:, 1] +\n",
    "                        weights['fare'] * df_survival_scaled[:, 2] +\n",
    "                        weights['sex'] * df_survival_scaled[:, 3] +\n",
    "                        weights['embarked'] * df_survival_scaled[:, 4] +\n",
    "                        weights['family_size'] * df_survival_scaled[:, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lista cech do wykresów\n",
    "features = ['survival_score', 'pclass', 'age', 'fare', 'embarked', 'family_size']\n",
    "\n",
    "# Tworzenie wykresów pudełkowych\n",
    "plt.figure(figsize=(20, 15))\n",
    "for i, feature in enumerate(features):\n",
    "    plt.subplot(3, 3, i + 1)\n",
    "    sns.boxplot(x=df[feature])\n",
    "    plt.title(f'Boxplot dla {feature}')\n",
    "    plt.xlabel(feature)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Podsumowanie:\n",
    "\n",
    "- Wartości odstające są szczególnie widoczne w przypadku zmiennych ciągłych, takich jak age i fare.\n",
    "- Klasy biletów (pclass) i porty zaokrętowania (embarked) mają wartości kategoryczne, które nie wykazują wartości odstających.\n",
    "- Zmienna family_size pokazuje wartości odstające dla dużych rodzin, co może sugerować rzadkie przypadki dużych rodzin podróżujących razem.\n",
    "\n",
    "- W przypadku zmiennych takich jak age i fare, warto przeanalizować wpływ wartości odstających na modele predykcyjne i rozważyć ich ewentualne przekształcenie lub usunięcie.\n",
    "- Wartości odstające mogą dostarczyć cennych informacji, ale również mogą zniekształcać wyniki analizy. Dlatego należy podjąć decyzje na podstawie kontekstu analizy i celów badania."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sWKNpX3zNn3baacXt++yzzyb9+vVLhg8f3ugxl2TBsrvttluj6UceeWSjfbjgcSsqKpKnn376S9d5//33J507d04OO+yw4rSf/vSnyfLLL5/85z//aTTvySefnFRWViavvfbaYtf173//O8nn88khhxxSnPab3/ym0estSea/Lnr16pWMHTu2OG3nnXdONt100+LtPffcM9lzzz2TysrK5LbbbkuSJEkeeeSRJCKSSZMmFedryns0Sebvs4U/j5qzv4cOHZr06dMnmTlzZnHa448/nlRUVCQHH3xwcVqPHj2So446arHbJkmS5PPPP0/WWGONZODAgckHH3zQ6L6FX9ML9jMAQEek3m4srXp7ccvPnTs3WX/99ZNtt922OO3hhx9OIiI57rjjGs17yCGHLJLn8MMPT1ZZZZVkxowZjebdf//9kx49eiw275IMHDgwiYjkhhtuKE6bNWtWssoqqyQbb7xxcdqC2nXh7T169OhFtkF9fX2y8sorJ3vttddSH1uNrkb/4rJqdDU60PEYSh3okH75y1/G5MmTY/LkyXHVVVfFNttsE9/85jcbDd31t7/9LSorK+PYY49ttOyJJ54YSZLEbbfdVpx2+umnx5AhQ2Ls2LFx5JFHxujRoxdZboEFRw5H/G84prlz58add9652PkbGhrijjvuiN133z2+8pWvFKevssoqccABB8R9990Xs2fPbvY2ePvtt+Oxxx6LsWPHRo8ePYrTd9hhhxg8ePBSl19wxHJExGeffRYzZ86MQYMGRc+ePRcZ2mhJdt9991h11VWLtzfddNPYbLPN4m9/+1tEzD/aesqUKXHYYYfFaqut1mjZpQ2FtHC+OXPmxIwZM2LkyJHxySefxHPPPRcRUXzedXV18cknnyx2PQuOuJ40aVKjYa6aYuEMC86aGD16dLz88suNhmpfnJEjR8a9995bzP/444/HEUccEb169SpOv/fee6Nnz57FI7Wbk/Xggw+Obt26FW/vvffescoqqxS3/b///e94991348gjj2x0faudd9451l133bj11ltj3rx5ccghh8Qf/vCHiIjiWQKTJ0+OOXPmxMknn7zItbGWtN+OPPLIePLJJ+OGG26IlVdeOSLm75/a2tq45pprisMfNjQ0xLXXXhu77757LL/88o3Wsd9++8UKK6zQaBtGRLz88suLPN53vvOdRrdHjhy52PkW56mnnorRo0fH6quvHnfeeWejx1yaL56FcMwxx0REFLf7AqNHj/7S9+E777wTe++9dwwdOjR+9atfFadPnDgxRo4cGSussELMmDGj+G/77bePhoaGmDJlyiLrmjFjRuy5554xZMiQRsMI7rvvvpHP52PChAnFaXV1dTFjxoxGZ/uMHDkyHnnkkfj4448jIuK+++6LnXbaKYYOHdrotZrL5WKrrbYqLteU9+iXWdr+XvAZd8ghh8SKK65YnG/DDTeMHXbYodE279mzZ/zzn/+Mt956a7GP9eijj8Yrr7wSxx133CJnYRiWDQCgMfV2+vX2wst/8MEHMWvWrOL39gVuv/32iJhfiy1sQY2yQJIkccMNN8Suu+4aSZI0qjNqampi1qxZTf4NYIF+/frFHnvsUbzdvXv3OPjgg+PRRx+Nd95550uX7dq1a6N6pEuXLrHppps2qZ5To6vRv0iNrkYHOh6NcaBD2nTTTWP77beP7bffPsaMGRO33nprDB48uFg0R8wfMqpfv36NCpOI/w2VNm3atOK0Ll26xOWXXx6vvPJKzJkzJ6644orFfhGrqKhoVGxHRKy99toREUu8Vth7770Xn3zySayzzjqL3LfeeuvFvHnzFnvdqaVZkH+ttdZa5L7FPdYXffrpp3HqqacWr5HUq1ev6N27d3z44YdLLSgXWNxjr7322sVtseDL88JDdDXV008/HXvssUf06NEjunfvHr179y4WCgvyrbHGGnHCCSfE73//++jVq1fU1NTEL3/5y0b599tvvxgxYkR885vfjL59+8b+++8f1113XZOa5Pfff39sv/32xesm9e7du3gtsaYU3W+//Xa8+OKLxetFbbHFFo2K8XvvvTdGjBgRFRUVzc76xW2fy+Vi0KBBxW2/4PWxuNfCuuuuG9OmTYuKiopYY401Gg29FTF/+LKIpu+33/zmN3HFFVfEpZdeGptvvnmj+w4++OB47bXXis/5zjvvjOnTp8dBBx20yHq+ePDEgoLsgw8+aDQ9n89H7969F5n3i/Mtya677hrdunWLurq66N69e5OWWeCL233NNdeMioqKRd7/a6yxxhLX8fnnn8e+++4bDQ0N8Ze//CWqqqqK973wwgtx++23R+/evRv923777SNi/jXmFtbQ0BD7779/fPLJJ3HDDTc0+pGkZ8+eseuuuxavORcxf4i2VVddNbbddtvitJEjR8bnn38eDz74YDz//PPx7rvvxsiRI2PUqFGNXquDBw9uVPw25T36ZZa2v7/sNbzeeuvFjBkzij8UnHfeefHUU0/FgAEDYtNNN43TTz+90Y8wzX1NAwB0ZOrt9OvtW265JTbffPPI5/Ox4oorRu/eveOyyy5rtOzCNd3CBg0a1Oj2e++9Fx9++GH89re/XaTOOPTQQyNi0TpjaQYNGrTIPlzavlqgf//+iyzb1HpOja5G/yI1uhod6Hg0xgFifgG9zTbbxNtvvx0vvPDCMq2jrq4uIiIKhcIyr6OcHHPMMXH22WfHvvvuG9ddd13ccccdMXny5FhppZWafWZ1a/vwww9j9OjR8fjjj8eZZ54ZN998c0yePDnOPffciIhG+S688MJ44okn4oc//GF8+umnceyxx8aQIUPijTfeiIj5R8xOmTIl7rzzzjjooIPiiSeeiP322y922GGHaGhoWGKGl156KbbbbruYMWNG/PznP49bb701Jk+eXLwW1NK20YKjdqdMmRL33ntvbLLJJrH88ssXi+6PPvooHn300eIRuC3J2hKnnXZarLLKKsu8/L/+9a/47ne/G9/85jcXKd4jImpqaqJv375x1VVXRcT863WtvPLKxSJyYQtfX3BhC45kX9p8TbXXXnvFSy+91Ogo7WW1pCOZFz5S+4tOOumkePDBB+O6665b5PqE8+bNix122KF4hs4X/+21116N5j/llFPi7rvvjmuuuSYGDhy4yGMdfPDB8fLLL8cDDzwQc+bMiZtuuim+8Y1vFH/oiYj46le/Gvl8vvha7dOnT6y99toxcuTI+Ne//hX19fVx7733NnqtNuc9uiRN3d9Nse+++8bLL78cl156afTr1y/OP//8GDJkSKMzlQAAWDbq7eZrSb197733xm677Rb5fD5+9atfxd/+9reYPHlyHHDAAcv0XXnB4x144IFLrDNGjBixTM9zWSxrHaBGbxo1uhpdjQ5kXae0AwC0F59//nlERHz00UcRETFw4MC48847Y86cOY2OYl8wfNDCX1CfeOKJOPPMM+PQQw+Nxx57LL75zW/Gk08+2WjItIj5XyRffvnl4pHQERH/+c9/IiJi9dVXX2yu3r17x3LLLRfPP//8Ivc999xzUVFREQMGDIiI5g0XtCD/4n5UWNxjfdH1118fY8eOjQsvvLA4rVAoxIcfftjkDIt77P/85z/FbbHgaP+nnnqqyeuMiLj77rtj5syZ8Ze//CVGjRpVnP7KK68sdv4NNtggNthgg/jxj38cDzzwQIwYMSJ+/etfx1lnnRUR83/I2W677WK77baLn//85/Gzn/0sfvSjH8Vdd9212OIvIuLmm2+O+vr6uOmmmxodNXvXXXc16Tmsttpqsdpqq8W9994bL7/8crFgGTVqVJxwwgkxceLEaGhoaPT8mpP1i9s+SZJ48cUXY8MNN4yI/70+nn/++UZHHi+YtrgCbYE111wzIubvty+ebbCw9957rzjU2C9/+cvFzlNZWRkHHHBAjB8/Ps4999y48cYb41vf+laLC+eWOP/886NTp05x5JFHRrdu3eKAAw5o8rIvvPBCoyPNX3zxxZg3b94S3/9f9Oc//zkuvvjiuPjii2P06NGL3L/mmmvGRx99tMTX5cImTpwY559/fowbN26J83/961+P3r17x4QJE2KzzTaLTz75ZJEzARYMXXjvvffGaqutVnytjhw5Murr62PChAkxffr0Rq/V5r5Hl8XCr+Eveu6556JXr16NhvpbZZVV4sgjj4wjjzwy3n333dhkk03i7LPPjh133LHRa7op2xYAgMbU2/9T6np7wVmmdXV1jc5cveKKKxbJOG/evHjllVcanTX74osvNpqvd+/e0a1bt2hoaGi178IvvvhiJEnSaJsubV+1lBpdjb44avT51OhAR+KMcYCYf82uO+64I7p06VIcum2nnXaKhoaG+L//+79G81500UWRy+Vixx13LC57yCGHRL9+/eKSSy6J8ePHx/Tp04tHHX/RwutLkiT+7//+Lzp37hzbbbfdYuevrKyMr33tazFp0qRGQzlNnz49rr766thqq62KQ0Ut+ALZlGJ5lVVWiaFDh8Yf//jHRkMiTZ48OZ555pmlLl9ZWbnIUZ+XXnpps456vvHGG+PNN98s3v7Xv/4V//znP4vbtnfv3jFq1Ki4/PLL47XXXmu07JcdcbqgIFt4nrlz5za6zlNExOzZs4s/0CywwQYbREVFRdTX10dExPvvv7/I+ocOHRoRUZynqRlmzZq1yI8RX2bkyJHxj3/8I/71r38VC5mhQ4dGt27d4pxzzonq6uoYNmxYcf7mZL3yyitjzpw5xdvXX399vP3228Vt/9WvfjX69OkTv/71rxste9ttt8Wzzz4bO++88xJzf+1rX4tu3brFuHHjolAoNLpv4euQ7b///jF37ty44YYbokuXLktc30EHHRQffPBBfPvb346PPvqo0bWz0pDL5eK3v/1t7L333jF27Ni46aabmrzsF39cuPTSSyMiitv9yzz11FPxzW9+Mw488MD47ne/u9h59t1333jwwQeLZ9Qs7MMPPyy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      "text/plain": [
       "<Figure size 2000x1800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Podział wieku na przedziały\n",
    "df['age_bin'] = pd.cut(df['age'], bins=[0, 10, 20, 30, 40, 50, 60, 70, 80], labels=['(0, 10]', '(10, 20]', '(20, 30]', '(30, 40]', '(40, 50]', '(50, 60]', '(60, 70]', '(70, 80]'])\n",
    "\n",
    "# Podział ceny biletu na przedziały\n",
    "df['fare_bin'] = pd.cut(df['fare'], bins=[0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550], labels=['(0, 50]', '(50, 100]', '(100, 150]', '(150, 200]', '(200, 250]', '(250, 300]', '(300, 350]', '(350, 400]', '(400, 450]', '(450, 500]', '(500, 550]'])\n",
    "\n",
    "# Lista cech do wykresów\n",
    "features = ['pclass', 'age_bin', 'fare_bin', 'sex', 'embarked', 'family_size']\n",
    "\n",
    "# Tworzenie wykresów pudełkowych\n",
    "plt.figure(figsize=(20, 18))\n",
    "for i, feature in enumerate(features):\n",
    "    plt.subplot(3, 2, i + 1)\n",
    "    sns.boxplot(x=df[feature], y=df['survival_score'])\n",
    "    plt.title(f'Boxplot dla {feature} a współczynnik przeżywalności')\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('Survival Score')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Podsumowanie:\n",
    "\n",
    "- Klasa biletu, cena biletu i płeć są kluczowymi czynnikami wpływającymi na przeżywalność pasażerów Titanica.\n",
    "- Wiek, port zaokrętowania i liczba członków rodziny również mają wpływ, ale są mniej jednoznaczne w swoich trendach.\n",
    "- Analiza wartości odstających wskazuje na obecność ekstremalnych przypadków w każdej z grup, co może sugerować różne strategie przetrwania lub inne czynniki wpływające na przeżywalność."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 osób z największą szansą na przeżycie:\n",
      "     pclass   age      fare  sex  embarked  family_size  survival_score\n",
      "302     1.0  35.0  512.3292    1         0          0.0        3.713157\n",
      "50      1.0  58.0  512.3292    1         0          1.0        3.660770\n",
      "250     1.0  18.0  262.3750    1         0          4.0        2.681486\n",
      "251     1.0  21.0  262.3750    1         0          4.0        2.672181\n",
      "49      1.0  36.0  512.3292    0         0          1.0        2.621921\n",
      "183     1.0  35.0  512.3292    0         0          0.0        2.606067\n",
      "253     1.0  48.0  262.3750    1         0          4.0        2.588430\n",
      "66      1.0  36.0  262.3750    1         0          0.0        2.549829\n",
      "97      1.0  27.0  247.5208    1         0          2.0        2.546708\n",
      "35      1.0  45.0  262.3750    1         0          0.0        2.521912\n",
      "\n",
      "10 osób z najmniejszą szansą na przeżycie:\n",
      "      pclass   age     fare  sex  embarked  family_size  survival_score\n",
      "1235     3.0  74.0   7.7750    0         2          0.0       -1.039690\n",
      "1068     3.0  61.0   6.2375    0         2          0.0       -1.006502\n",
      "896      3.0  49.0   0.0000    0         2          0.0       -0.998233\n",
      "736      3.0  59.0   7.2500    0         2          0.0       -0.995599\n",
      "1015     3.0  55.5   8.0500    0         2          0.0       -0.981029\n",
      "979      3.0  51.0   7.0542    0         2          0.0       -0.971693\n",
      "1287     3.0  51.0   7.7500    0         2          0.0       -0.968463\n",
      "833      3.0  51.0   8.0500    0         2          0.0       -0.967070\n",
      "1225     3.0  60.5  14.4542    0         2          0.0       -0.966811\n",
      "1160     3.0  50.0   8.0500    0         2          0.0       -0.963968\n"
     ]
    }
   ],
   "source": [
    "# Wyświetlanie 10 osób z największą szansą na przeżycie\n",
    "top_10_survival = df.nlargest(10, 'survival_score')\n",
    "print(\"10 osób z największą szansą na przeżycie:\")\n",
    "print(top_10_survival[['pclass', 'age', 'fare', 'sex', 'embarked', 'family_size', 'survival_score']])\n",
    "\n",
    "# Wyświetlanie 10 osób z najmniejszą szansą na przeżycie\n",
    "bottom_10_survival = df.nsmallest(10, 'survival_score')\n",
    "print(\"\\n10 osób z najmniejszą szansą na przeżycie:\")\n",
    "print(bottom_10_survival[['pclass', 'age', 'fare', 'sex', 'embarked', 'family_size', 'survival_score']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Podsumowanie:\n",
    "\n",
    "**10 osób z największą szansą na przeżycie**:\n",
    "\n",
    "- **Klasa biletu**- wszyscy pasażerowie z największą szansą na przeżycie byli pasażerami 1. klasy.\n",
    "- **Płeć**- większość osób z wysokim współczynnikiem przeżywalności to kobiety, co sugeruje, że płeć miała znaczący wpływ na przeżycie.\n",
    "- **Wiek**- wiek tych pasażerów mieści się w przedziale od 18 do 58 lat, co sugeruje, że osoby w tym przedziale wiekowym miały większe szanse na przeżycie.\n",
    "- **Cena biletu**- wysokie ceny biletów (powyżej 247 jednostek) są zauważalne wśród osób z największą szansą na przeżycie.\n",
    "- **Port zaokrętowania**- większość z tych pasażerów zaokrętowała w porcie Cherbourg (oznaczonym jako 0).\n",
    "- **Liczba członków rodziny**- pasażerowie ci mają zmienną liczbę członków rodziny na pokładzie, od 0 do 4.\n",
    "\n",
    "**10 osób z najmniejszą szansą na przeżycie**:\n",
    "\n",
    "- **Klasa biletu**- wszyscy pasażerowie z najmniejszą szansą na przeżycie byli pasażerami 3. klasy.\n",
    "- **Płeć**- wszyscy ci pasażerowie to mężczyźni, co sugeruje, że płeć miała znaczący wpływ na przeżycie.\n",
    "- **Wiek**- wynosi od 49 do 74 lat, co sugeruje, że starsze osoby miały mniejsze szanse na przeżycie.\n",
    "- **Cena biletu**- niskie ceny biletów (poniżej 15 jednostek) są zauważalne wśród osób z najmniejszą szansą na przeżycie.\n",
    "- **Port zaokrętowania**- większość z tych pasażerów zaokrętowała w porcie Southampton (oznaczonym jako 2).\n",
    "- **Liczba członków rodziny**- pasażerowie ci podróżowali bez rodziny na pokładzie (liczba członków rodziny = 0)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Wnioski z analizy danych i PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dodanie kolumny survival_score\n",
    "df_pca['survival_score'] = df['survival_score']\n",
    "\n",
    "# Standaryzacja danych\n",
    "scaler = StandardScaler()\n",
    "df_pca_scaled = scaler.fit_transform(df_pca)\n",
    "\n",
    "# Przeprowadzenie PCA\n",
    "pca = PCA(n_components=2)\n",
    "pca_components = pca.fit_transform(df_pca_scaled)\n",
    "\n",
    "# Tworzenie ramki danych z wynikami PCA\n",
    "df_pca_results = pd.DataFrame(data=pca_components, columns=['PC1', 'PC2'])\n",
    "\n",
    "# Dodanie kolumny 'survival_score' do wyników PCA, aby móc kolorować punkty na wykresie\n",
    "df_pca_results['survival_score'] = df['survival_score'].values\n",
    "\n",
    "# Wizualizacja wyników PCA\n",
    "plt.figure(figsize=(10, 8))\n",
    "scatter = plt.scatter(x=df_pca_results['PC1'], y=df_pca_results['PC2'], c=df_pca_results['survival_score'], cmap='coolwarm', alpha=0.6)\n",
    "plt.colorbar(scatter, label='Survival Score')\n",
    "plt.title('Wyniki PCA z survival_score')\n",
    "plt.xlabel('Główna składowa 1 (PC1)')\n",
    "plt.ylabel('Główna składowa 2 (PC2)')\n",
    "plt.grid(True)\n",
    "plt.savefig('pcatitanic.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Kluczowe Wnioski z Analiz**\n",
    "\n",
    "- Z wykresu PCA wynika, że pasażerowie o niższym współczynniku przeżywalności (w odcieniach niebieskiego) są bardziej skoncentrowani w dolnej lewej części wykresu.\n",
    "- Pasażerowie o wyższym współczynniku przeżywalności (w odcieniach czerwieni) są bardziej rozproszeni, ale zauważalnie skupiają się w górnej prawej części wykresu.\n",
    "- Te różnice sugerują, że główne składowe skutecznie oddzielają grupy pasażerów o różnych szansach na przeżycie.\n",
    "- Pierwsza główna składowa (PC1) wydaje się mieć większy wpływ na rozróżnienie pasażerów o różnym współczynniku przeżywalności, co sugeruje, że zmienne mające największy wpływ na PC1 są kluczowe dla przeżycia.\n",
    "- Druga główna składowa (PC2) również przyczynia się do rozróżnienia, ale w mniejszym stopniu.\n",
    "\n",
    "Analiza danych pasażerów Titanica ujawniła, że płeć, klasa biletu i cena biletu były najważniejszymi czynnikami wpływającymi na przeżywalność. Wiek, liczba członków rodziny i port zaokrętowania miały mniejszy wpływ, ale nadal były istotne w kontekście specyficznych sytuacji pasażerów. Wykorzystanie PCA pozwoliło na wizualizację tych różnic i potwierdzenie, że główne składowe skutecznie oddzielają grupy pasażerów o różnych szansach na przeżycie, co jest zgodne z wcześniejszymi analizami."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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