{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e98e030e-9467-423f-be5d-deaf3144cfc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0507bb14-a8d9-4c36-b45c-5822d93d8cf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('26__titanic.csv', sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b79d0217-cef7-45e1-a2fc-231dd9676399",
   "metadata": {},
   "outputs": [],
   "source": [
    "# zdefiniujmy sobie kilka funkcji-helperów\n",
    "def format_corr_value(val):\n",
    "    if val == 1.0:\n",
    "        return '1'\n",
    "    v = int(val * 100)\n",
    "    av = (abs(v) // 10) - 4\n",
    "    if (av > 0):\n",
    "        return '%s%s' % (v, '!' * av)\n",
    "    return str(v)\n",
    "\n",
    "def format_corr(dt):\n",
    "    dt2 = dt.copy()\n",
    "    for c in dt2.columns:\n",
    "        dt2[c] = dt2[c].apply(format_corr_value)\n",
    "    return dt2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3557423d-a79e-4f8e-86e1-2c14d6a801c7",
   "metadata": {},
   "source": [
    "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8000c02-db74-44b9-b287-abbf71510773",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>135</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>A5</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Serepeca, Miss. Augusta</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113798</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Marechal, Mr. Pierre</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11774</td>\n",
       "      <td>29.7000</td>\n",
       "      <td>C47</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris, France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jarvis, Mr. John Denzil</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>237565</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North Evington, England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Hickman, Mr. Stanley George</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S.O.C. 14879</td>\n",
       "      <td>73.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>West Hampstead, London / Neepawa, MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Murphy, Miss. Margaret Jane</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>367230</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Lockyer, Mr. Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1222</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Lefebre, Miss. Ida</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4133</td>\n",
       "      <td>25.4667</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>1074</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>O'Connor, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>366713</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Meo, Mr. Alfonzo</td>\n",
       "      <td>male</td>\n",
       "      <td>55.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A.5. 11206</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>201.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Douglas, Mrs. Walter Donald (Mahala Dutton)</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PC 17761</td>\n",
       "      <td>106.4250</td>\n",
       "      <td>C86</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Deephaven, MN / Cedar Rapids, IA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rheims, Mr. George Alexander Lucien</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PC 17607</td>\n",
       "      <td>39.6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris /  New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vander Planke, Miss. Augusta Maria</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>345764</td>\n",
       "      <td>18.0000</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>746</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Daly, Mr. Eugene Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>382651</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>13 15 B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Co Athlone, Ireland New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>O'Leary, Miss. Hanora \"Norah\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330919</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass  survived                                         name     sex  \\\n",
       "135      1.0       0.0                    Goldschmidt, Mr. George B    male   \n",
       "258      1.0       1.0                      Serepeca, Miss. Augusta  female   \n",
       "196      1.0       1.0                         Marechal, Mr. Pierre    male   \n",
       "461      2.0       0.0                      Jarvis, Mr. John Denzil    male   \n",
       "444      2.0       0.0                  Hickman, Mr. Stanley George    male   \n",
       "1043     3.0       1.0                  Murphy, Miss. Margaret Jane  female   \n",
       "976      3.0       0.0                          Lockyer, Mr. Edward    male   \n",
       "955      3.0       0.0                           Lefebre, Miss. Ida  female   \n",
       "1074     3.0       0.0                        O'Connor, Mr. Patrick    male   \n",
       "1015     3.0       0.0                             Meo, Mr. Alfonzo    male   \n",
       "98       1.0       1.0  Douglas, Mrs. Walter Donald (Mahala Dutton)  female   \n",
       "235      1.0       1.0          Rheims, Mr. George Alexander Lucien    male   \n",
       "1273     3.0       0.0           Vander Planke, Miss. Augusta Maria  female   \n",
       "746      3.0       1.0                     Daly, Mr. Eugene Patrick    male   \n",
       "1081     3.0       1.0                O'Leary, Miss. Hanora \"Norah\"  female   \n",
       "\n",
       "       age  sibsp  parch        ticket      fare cabin embarked     boat  \\\n",
       "135   71.0    0.0    0.0      PC 17754   34.6542    A5        C      NaN   \n",
       "258   30.0    0.0    0.0        113798   31.0000   NaN        C        4   \n",
       "196    NaN    0.0    0.0         11774   29.7000   C47        C        7   \n",
       "461   47.0    0.0    0.0        237565   15.0000   NaN        S      NaN   \n",
       "444   21.0    2.0    0.0  S.O.C. 14879   73.5000   NaN        S      NaN   \n",
       "1043   NaN    1.0    0.0        367230   15.5000   NaN        Q       16   \n",
       "976    NaN    0.0    0.0          1222    7.8792   NaN        S      NaN   \n",
       "955    NaN    3.0    1.0          4133   25.4667   NaN        S      NaN   \n",
       "1074   NaN    0.0    0.0        366713    7.7500   NaN        Q      NaN   \n",
       "1015  55.5    0.0    0.0    A.5. 11206    8.0500   NaN        S      NaN   \n",
       "98    48.0    1.0    0.0      PC 17761  106.4250   C86        C        2   \n",
       "235    NaN    0.0    0.0      PC 17607   39.6000   NaN        S        A   \n",
       "1273  18.0    2.0    0.0        345764   18.0000   NaN        S      NaN   \n",
       "746   29.0    0.0    0.0        382651    7.7500   NaN        Q  13 15 B   \n",
       "1081   NaN    0.0    0.0        330919    7.8292   NaN        Q       13   \n",
       "\n",
       "       body                             home.dest  \n",
       "135     NaN                          New York, NY  \n",
       "258     NaN                                   NaN  \n",
       "196     NaN                         Paris, France  \n",
       "461     NaN               North Evington, England  \n",
       "444     NaN  West Hampstead, London / Neepawa, MB  \n",
       "1043    NaN                                   NaN  \n",
       "976   153.0                                   NaN  \n",
       "955     NaN                                   NaN  \n",
       "1074    NaN                                   NaN  \n",
       "1015  201.0                                   NaN  \n",
       "98      NaN      Deephaven, MN / Cedar Rapids, IA  \n",
       "235     NaN                 Paris /  New York, NY  \n",
       "1273    NaN                                   NaN  \n",
       "746     NaN      Co Athlone, Ireland New York, NY  \n",
       "1081    NaN                                   NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# jak zwykle pierwszy rzut oka na dane\n",
    "df.sample(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "338309f0-095f-4222-9096-0bed009835aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
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       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
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       "      <th>count</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.381971</td>\n",
       "      <td>29.881135</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>160.809917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.837836</td>\n",
       "      <td>0.486055</td>\n",
       "      <td>14.413500</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>97.696922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>256.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>328.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pclass     survived          age        sibsp        parch  \\\n",
       "count  1309.000000  1309.000000  1046.000000  1309.000000  1309.000000   \n",
       "mean      2.294882     0.381971    29.881135     0.498854     0.385027   \n",
       "std       0.837836     0.486055    14.413500     1.041658     0.865560   \n",
       "min       1.000000     0.000000     0.166700     0.000000     0.000000   \n",
       "25%       2.000000     0.000000    21.000000     0.000000     0.000000   \n",
       "50%       3.000000     0.000000    28.000000     0.000000     0.000000   \n",
       "75%       3.000000     1.000000    39.000000     1.000000     0.000000   \n",
       "max       3.000000     1.000000    80.000000     8.000000     9.000000   \n",
       "\n",
       "              fare        body  \n",
       "count  1308.000000  121.000000  \n",
       "mean     33.295479  160.809917  \n",
       "std      51.758668   97.696922  \n",
       "min       0.000000    1.000000  \n",
       "25%       7.895800   72.000000  \n",
       "50%      14.454200  155.000000  \n",
       "75%      31.275000  256.000000  \n",
       "max     512.329200  328.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e331bb52-010c-4e5d-9ab5-9573c38b150e",
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0059ec24-e91f-4599-bf83-9837047fa5c9",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     pclass  survived                  name     sex   age  sibsp  parch  \\\n",
       "726     3.0       0.0  Connolly, Miss. Kate  female  30.0    0.0    0.0   \n",
       "925     3.0       0.0      Kelly, Mr. James    male  44.0    0.0    0.0   \n",
       "\n",
       "     ticket    fare cabin embarked boat  body home.dest  \n",
       "726  330972  7.6292   NaN        Q  NaN   NaN   Ireland  \n",
       "925  363592  8.0500   NaN        S  NaN   NaN       NaN  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sprawdzenie, czy nie ma duplikatów po name:\n",
    "df[df.duplicated(subset=['name'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c1b0ae57-a57a-48c5-99c9-3cd70a2673fb",
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    {
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       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>925</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "     pclass  survived                  name     sex   age  sibsp  parch  \\\n",
       "725     3.0       1.0  Connolly, Miss. Kate  female  22.0    0.0    0.0   \n",
       "726     3.0       0.0  Connolly, Miss. Kate  female  30.0    0.0    0.0   \n",
       "924     3.0       0.0      Kelly, Mr. James    male  34.5    0.0    0.0   \n",
       "925     3.0       0.0      Kelly, Mr. James    male  44.0    0.0    0.0   \n",
       "\n",
       "     ticket    fare cabin embarked boat  body home.dest  \n",
       "725  370373  7.7500   NaN        Q   13   NaN   Ireland  \n",
       "726  330972  7.6292   NaN        Q  NaN   NaN   Ireland  \n",
       "924  330911  7.8292   NaN        Q  NaN  70.0       NaN  \n",
       "925  363592  8.0500   NaN        S  NaN   NaN       NaN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# więc sprawdzmy te nazwiska\n",
    "df[df['name'].isin(['Connolly, Miss. Kate', 'Kelly, Mr. James'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e4d5aa71-db61-4d92-af50-19c9e507fdd1",
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   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      klasa_biletu  ocalal plec  wiek  l_rdz_młż  l_dzieci_rodz   oplata  \\\n",
       "1252           3.0     0.0    M  44.0        0.0            0.0   8.0500   \n",
       "389            2.0     0.0    M  32.0        0.0            0.0  13.0000   \n",
       "638            3.0     0.0    M  35.0        0.0            0.0   7.0500   \n",
       "722            3.0     0.0    M  24.0        0.0            0.0   7.4958   \n",
       "649            3.0     0.0    M  23.0        0.0            0.0   7.0500   \n",
       "804            3.0     0.0    M   NaN        0.0            0.0   7.7500   \n",
       "301            1.0     0.0    M  47.0        0.0            0.0  34.0208   \n",
       "95             1.0     1.0    K  54.0        1.0            1.0  81.8583   \n",
       "206            1.0     0.0    M  44.0        2.0            0.0  90.0000   \n",
       "433            2.0     0.0    M  30.0        0.0            0.0  10.5000   \n",
       "402            2.0     1.0    K  30.0        1.0            0.0  13.8583   \n",
       "849            3.0     0.0    M  26.0        1.0            0.0   7.8542   \n",
       "1030           3.0     0.0    M   NaN        0.0            0.0   8.4583   \n",
       "1155           3.0     0.0    M   NaN        0.0            0.0   7.7750   \n",
       "697            3.0     0.0    K  30.0        0.0            0.0   8.6625   \n",
       "\n",
       "     kabina lodz  mial_lodke  mial_kabine  \n",
       "1252    NaN  NaN           0            0  \n",
       "389     NaN  NaN           0            0  \n",
       "638     NaN  NaN           0            0  \n",
       "722     NaN  NaN           0            0  \n",
       "649     NaN  NaN           0            0  \n",
       "804     NaN  NaN           0            0  \n",
       "301     D46  NaN           0            1  \n",
       "95      A34    5           1            1  \n",
       "206     C78  NaN           0            1  \n",
       "433     NaN  NaN           0            0  \n",
       "402     NaN   12           1            0  \n",
       "849     NaN  NaN           0            0  \n",
       "1030    NaN  NaN           0            0  \n",
       "1155    NaN  NaN           0            0  \n",
       "697     NaN  NaN           0            0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# czyli widzimy, że to raczej zbieżność nazwisk\n",
    "\n",
    "# Teraz tworzymy nową strukturę danych bardziej przejrzystą do obróbki. Nie mam ochoty analizować takich\n",
    "# danych jak: home.dest, body, embarked, ticket, name - a w zasadzie to nawet boat i cabin zamienię na \n",
    "# wartość bool mial_lodke i mial_kabine. Łódką będę się zajmował później, bo jest coś ciekawego z łódką 'A'.\n",
    "df2 = df.copy()\n",
    "df2 = df2[[c for c in df.columns if c not in ['name', 'ticket', 'home.dest', 'body', 'embarked']]]\n",
    "df2.columns = ['klasa_biletu', 'ocalal', 'plec', 'wiek', 'l_rdz_młż', 'l_dzieci_rodz', 'oplata', 'kabina', 'lodz']\n",
    "df2['plec'] = df2['plec'].apply(lambda x: ({'female': 'K', 'male': 'M'}.get(x, x)))\n",
    "df2['mial_lodke'] = df2['lodz'].notnull().astype(int)\n",
    "df2['mial_kabine'] = df2['kabina'].notnull().astype(int)\n",
    "df2.drop(\n",
    "df2.sample(15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6b8f3e69-68e4-42c6-8edf-63a93811c425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>klasa_biletu</th>\n",
       "      <th>ocalal</th>\n",
       "      <th>plec</th>\n",
       "      <th>wiek</th>\n",
       "      <th>l_rdz_młż</th>\n",
       "      <th>l_dzieci_rodz</th>\n",
       "      <th>oplata</th>\n",
       "      <th>kabina</th>\n",
       "      <th>port</th>\n",
       "      <th>lodz</th>\n",
       "      <th>cialo</th>\n",
       "      <th>dest</th>\n",
       "      <th>mial_lodke</th>\n",
       "      <th>mial_kabine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A36</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast, NI</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Liverpool, England / Belfast</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B102</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B94</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B52 B54 B56</td>\n",
       "      <td>S</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rotterdam, Netherlands</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      klasa_biletu  ocalal plec  wiek  l_rdz_młż  l_dzieci_rodz  oplata  \\\n",
       "7              1.0     0.0    M  39.0        0.0            0.0     0.0   \n",
       "70             1.0     0.0    M   NaN        0.0            0.0     0.0   \n",
       "125            1.0     0.0    M   NaN        0.0            0.0     0.0   \n",
       "150            1.0     0.0    M  40.0        0.0            0.0     0.0   \n",
       "170            1.0     1.0    M  49.0        0.0            0.0     0.0   \n",
       "223            1.0     0.0    M   NaN        0.0            0.0     0.0   \n",
       "234            1.0     0.0    M  38.0        0.0            0.0     0.0   \n",
       "363            2.0     0.0    M   NaN        0.0            0.0     0.0   \n",
       "384            2.0     0.0    M   NaN        0.0            0.0     0.0   \n",
       "410            2.0     0.0    M   NaN        0.0            0.0     0.0   \n",
       "473            2.0     0.0    M   NaN        0.0            0.0     0.0   \n",
       "528            2.0     0.0    M   NaN        0.0            0.0     0.0   \n",
       "581            2.0     0.0    M   NaN        0.0            0.0     0.0   \n",
       "896            3.0     0.0    M  49.0        0.0            0.0     0.0   \n",
       "898            3.0     0.0    M  19.0        0.0            0.0     0.0   \n",
       "963            3.0     0.0    M  36.0        0.0            0.0     0.0   \n",
       "1254           3.0     1.0    M  25.0        0.0            0.0     0.0   \n",
       "\n",
       "           kabina port lodz  cialo                          dest  mial_lodke  \\\n",
       "7             A36    S  NaN    NaN                   Belfast, NI           0   \n",
       "70            NaN    S  NaN    NaN  Liverpool, England / Belfast           0   \n",
       "125          B102    S  NaN    NaN                           NaN           0   \n",
       "150           B94    S  NaN  110.0                           NaN           0   \n",
       "170   B52 B54 B56    S    C    NaN                     Liverpool           1   \n",
       "223           NaN    S  NaN    NaN                       Belfast           0   \n",
       "234           NaN    S  NaN    NaN        Rotterdam, Netherlands           0   \n",
       "363           NaN    S  NaN    NaN                       Belfast           0   \n",
       "384           NaN    S  NaN    NaN                       Belfast           0   \n",
       "410           NaN    S  NaN    NaN                       Belfast           0   \n",
       "473           NaN    S  NaN    NaN                       Belfast           0   \n",
       "528           NaN    S  NaN    NaN                       Belfast           0   \n",
       "581           NaN    S  NaN    NaN                       Belfast           0   \n",
       "896           NaN    S  NaN    NaN                           NaN           0   \n",
       "898           NaN    S  NaN    NaN                           NaN           0   \n",
       "963           NaN    S  NaN    NaN                           NaN           0   \n",
       "1254          NaN    S   15    NaN                           NaN           1   \n",
       "\n",
       "      mial_kabine  \n",
       "7               1  \n",
       "70              0  \n",
       "125             1  \n",
       "150             1  \n",
       "170             1  \n",
       "223             0  \n",
       "234             0  \n",
       "363             0  \n",
       "384             0  \n",
       "410             0  \n",
       "473             0  \n",
       "528             0  \n",
       "581             0  \n",
       "896             0  \n",
       "898             0  \n",
       "963             0  \n",
       "1254            0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# analiza podejrzanych danych\n",
    "# 1. Dlaczego mamy dane z zerową opłatą (fare[min] == 0)?\n",
    "df2[df2['oplata'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9dbb68c5-a311-4762-aaa3-ca54f438ec9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>klasa_biletu</th>\n",
       "      <th>ocalal</th>\n",
       "      <th>plec</th>\n",
       "      <th>wiek</th>\n",
       "      <th>l_rdz_młż</th>\n",
       "      <th>l_dzieci_rodz</th>\n",
       "      <th>oplata</th>\n",
       "      <th>kabina</th>\n",
       "      <th>lodz</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>K</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.4750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.7875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>K</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>K</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>K</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>K</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>K</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>K</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>K</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1047</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>K</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>K</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      klasa_biletu  ocalal plec  wiek  l_rdz_młż  l_dzieci_rodz   oplata  \\\n",
       "794            3.0     1.0    K   5.0        0.0            0.0  12.4750   \n",
       "855            3.0     0.0    M  11.0        0.0            0.0  18.7875   \n",
       "582            2.0     1.0    K  12.0        0.0            0.0  15.7500   \n",
       "1056           3.0     1.0    M  12.0        1.0            0.0  11.2417   \n",
       "653            3.0     1.0    K  13.0        0.0            0.0   7.2292   \n",
       "513            2.0     1.0    K  14.0        1.0            0.0  30.0708   \n",
       "569            2.0     0.0    M  14.0        0.0            0.0  65.0000   \n",
       "1057           3.0     1.0    K  14.0        1.0            0.0  11.2417   \n",
       "1236           3.0     1.0    M  14.0        0.0            0.0   9.2250   \n",
       "1279           3.0     0.0    K  14.0        0.0            0.0   7.8542   \n",
       "1304           3.0     0.0    K  14.5        1.0            0.0  14.4542   \n",
       "1007           3.0     1.0    K  15.0        0.0            0.0   8.0292   \n",
       "1047           3.0     1.0    K  15.0        0.0            0.0   7.2250   \n",
       "1300           3.0     1.0    K  15.0        1.0            0.0  14.4542   \n",
       "\n",
       "     kabina lodz  mial_lodke  mial_kabine  \n",
       "794     NaN   13           1            0  \n",
       "855     NaN  NaN           0            0  \n",
       "582     NaN    9           1            0  \n",
       "1056    NaN    C           1            0  \n",
       "653     NaN    C           1            0  \n",
       "513     NaN  NaN           0            0  \n",
       "569     NaN  NaN           0            0  \n",
       "1057    NaN    C           1            0  \n",
       "1236    NaN   13           1            0  \n",
       "1279    NaN  NaN           0            0  \n",
       "1304    NaN  NaN           0            0  \n",
       "1007    NaN  NaN           0            0  \n",
       "1047    NaN    C           1            0  \n",
       "1300    NaN  NaN           0            0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OK, widzimy, że to raczej wygląda na załogę, co jest dziwne - nie było tam kobiet[???] Żadne kelnerki,\n",
    "# sprzątaczki, muzycy? \n",
    "# One płaciły za bilety, albo nie były uwzględnione tutaj? DZIWNE!!!!\n",
    "# dalej - co z wiekiem - min jest ok. 0... Ale OK, mogą być przecież z rodzicami - więc sprawdźmy tych,\n",
    "# co rodziców nie mieli...\n",
    "df2[(df2['l_dzieci_rodz'] == 0) & (df2['wiek'] < 16)].sort_values(by=\"wiek\", ascending=True).head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee4e6284-1bc9-420e-9ed9-373e4f1c8509",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Skandal na pokładzie były dzieci bez rodziców - a nawet jedno miało 5 lat!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "092aaf20-1735-4317-8e68-119c521a4b60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "liczba wszystkich uczestników: 1309\n",
      "W tym wyprawę przeżyło: 500\n",
      "\n",
      "\n",
      "           liczba wszystkich  liczba ocalałych\n",
      "plec                                          \n",
      "Kobiety                  466             339.0\n",
      "Mężczyźni                843             161.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# na początek kilka podstawowych danych szczególnie nas interesujących\n",
    "print('liczba wszystkich uczestników: %s\\nW tym wyprawę przeżyło: %s\\n\\n' % (\n",
    "    df2.count()['plec'], df2[df2['ocalal'] == 1].count()['plec']))\n",
    "by_sex = df2.groupby('plec').agg({'ocalal': ['count', 'sum']})\n",
    "by_sex.rename(index={'M': 'Mężczyźni', 'K': 'Kobiety'}, inplace=True)\n",
    "by_sex.columns = ['liczba wszystkich', 'liczba ocalałych']\n",
    "print(by_sex)\n",
    "by_sex.plot(kind='pie', subplots=True)\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "facf0de3-3226-4fdf-9b11-b57debb90f95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Będąc kobietą miałeś 72.7% szans na przeżycie\n",
      "Będąc mężczyzną miałeś 19.1% szans na przeżycie\n"
     ]
    }
   ],
   "source": [
    "# no wnioski tutaj się same nasuwają. Mężczyźni zachowali się bardzo dzielnie w większości! \n",
    "wmn = by_sex.loc['Kobiety']\n",
    "mn = by_sex.loc['Mężczyźni']\n",
    "print('Będąc kobietą miałeś %0.1f%% szans na przeżycie' % (100 * wmn['liczba ocalałych'] / wmn['liczba wszystkich']))\n",
    "print('Będąc mężczyzną miałeś %0.1f%% szans na przeżycie' % (100 * mn['liczba ocalałych'] / mn['liczba wszystkich']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "bf308eb5-d0f9-4df9-ba32-e5617612ad65",
   "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>klasa_biletu</th>\n",
       "      <th>ocalal</th>\n",
       "      <th>wiek</th>\n",
       "      <th>l_rdz_młż</th>\n",
       "      <th>l_dzieci_rodz</th>\n",
       "      <th>oplata</th>\n",
       "      <th>mial_lodke</th>\n",
       "      <th>mial_kabine</th>\n",
       "      <th>cena_1os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>klasa_biletu</th>\n",
       "      <td>1</td>\n",
       "      <td>-31</td>\n",
       "      <td>-40</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>-55!</td>\n",
       "      <td>-32</td>\n",
       "      <td>-71!!!</td>\n",
       "      <td>-50!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ocalal</th>\n",
       "      <td>-31</td>\n",
       "      <td>1</td>\n",
       "      <td>-5</td>\n",
       "      <td>-2</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>94!!!!!</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wiek</th>\n",
       "      <td>-40</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "      <td>-24</td>\n",
       "      <td>-15</td>\n",
       "      <td>17</td>\n",
       "      <td>-5</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l_rdz_młż</th>\n",
       "      <td>6</td>\n",
       "      <td>-2</td>\n",
       "      <td>-24</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>16</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l_dzieci_rodz</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-15</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oplata</th>\n",
       "      <td>-55!</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>50!</td>\n",
       "      <td>83!!!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mial_lodke</th>\n",
       "      <td>-32</td>\n",
       "      <td>94!!!!!</td>\n",
       "      <td>-5</td>\n",
       "      <td>-2</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mial_kabine</th>\n",
       "      <td>-71!!!</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>50!</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cena_1os</th>\n",
       "      <td>-50!</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>-8</td>\n",
       "      <td>-6</td>\n",
       "      <td>83!!!!</td>\n",
       "      <td>21</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              klasa_biletu   ocalal wiek l_rdz_młż l_dzieci_rodz  oplata  \\\n",
       "klasa_biletu             1      -31  -40         6             1    -55!   \n",
       "ocalal                 -31        1   -5        -2             8      24   \n",
       "wiek                   -40       -5    1       -24           -15      17   \n",
       "l_rdz_młż                6       -2  -24         1            37      16   \n",
       "l_dzieci_rodz            1        8  -15        37             1      22   \n",
       "oplata                -55!       24   17        16            22       1   \n",
       "mial_lodke             -32  94!!!!!   -5        -2             8      25   \n",
       "mial_kabine         -71!!!       30   28         0             3     50!   \n",
       "cena_1os              -50!       21   19        -8            -6  83!!!!   \n",
       "\n",
       "              mial_lodke mial_kabine cena_1os  \n",
       "klasa_biletu         -32      -71!!!     -50!  \n",
       "ocalal           94!!!!!          30       21  \n",
       "wiek                  -5          28       19  \n",
       "l_rdz_młż             -2           0       -8  \n",
       "l_dzieci_rodz          8           3       -6  \n",
       "oplata                25         50!   83!!!!  \n",
       "mial_lodke             1          31       21  \n",
       "mial_kabine           31           1       39  \n",
       "cena_1os              21          39        1  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dobra, rzut oka na korelację... Ale zanim, to dodajmy kolumnę z opłatą podzieloną przez \n",
    "# liczbę dzieci/braci/współmałżonków\n",
    "df3 = df2.copy()\n",
    "df3['cena_1os'] = df3['oplata'] / (df3['l_rdz_młż'] + df3['l_dzieci_rodz'] + 1)\n",
    "\n",
    "format_corr(df3.corr(numeric_only=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "de386d6e-bf5f-43ee-a3f1-50e9a68e8a74",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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n+vXrdccdd+jhhx9Wp06dNHnyZOMLsnv37lq7dq0SEhIUFhamV199Va+//rpGjRplbG+xWIxZkn79+km6MEvh5+enbt26ydfXV9KFcxm++uorRUdHO+x/xYoVCg4OVnh4uIYNG6Ynn3zS4QTU5ORklZaWaujQoWrVqpXxujSYlXvjjTf06quvatq0aerYsaOio6O1ZcsW49wJf39/7dixQ2fOnFF4eLh69OihZcuWOXVOypNPPlnhcmhnBQUF6bPPPlNpaamio6MVFham5557Tn5+flU+j6o647F48WL9/ve/17hx49ShQwc98cQTOnv2rKQLV9oMGzZMDz74oHr16qWff/7ZOKm3Mg0aNFBCQoLS09MVFhamCRMmaObMmVfs26ZNm3TTTTcZPw+1xWKvyVv0XScFBQXy8/PTqVOnjF+OmmKz2bR161atOt6Uq3hMoHw87r777utykytc3rWMR1FRkY4ePaqQkJDLTuOj+srKylRQUCBfX1+XP4Rx9uzZ2r59u7Zu3erSdtSEoqIitW/fXgkJCVWepFwVM41Jbfn1r3+tmJgYjRgxotL1l/tdd+b7+8b89AAA11WbNm0cbthWl3l6euqdd9654k3V6qO8vDz9/ve/18MPP1zr++IqHgDANbvcDcjqostdyluftWzZUpMnT74u+2IGBQAAmA4BBQAAmA4BBQAAmA4BBQAAmA4BBQAAmA4BBQAAmA4BBQAgSVq3bp3Wr1/v6mYAkggoAHBFERERiomJccm+v/nmG1ksFmVkZFSr/C233KK5c+c6vZ+UlBQ9//zzuvPOO53e1lnOtHHUqFG67777amzf1a1v5MiRio+Pr7H9Xomz4yxJcXFxxsMNJWnSpEl69tlna75xLsKN2gC43t+u802xxlb+NNgbQVpaWpVPy63KTz/9pLFjx2rTpk1q3br1Nbdh5cqVWrJkSYUnMV+NefPmqSafyFKd+vbt26cPP/xQixYtqrH9Xg+TJ0/Wr371K02YMMF4NlFdxgwKAFwHNpvtuuynRYsW8vb2dmqb5s2bKzMzU127dq2RNlz6VONr4efnpyZNmtRIXdWtb8GCBXrggQfk4+NTY/u9Hlq2bKmoqCgtWbLE1U2pEQQUAKhh5dP1a9euVUREhDw9PbV69WqVlZXp9ddfV5s2bWS1WnX77bdr27ZtDtvu3r1b3bp1k6enp3r27Km9e/c6rB81apQsFossFovc3NzUtGlTubm5aefOnZIqHj45efKknnzySQUEBMjT01NhYWH65z//aaxfv369OnfuLKvVqltuuUWzZs0y1s2fP19dunQx3m/cuFEWi0ULFy40lkVHRzs8g6eoqEiJiYkaOnSopAvPbhkyZIi8vLwUEhJS4SnBK1euNPpz8SsuLs7o78WHZOx2u2bMmKG2bdvKy8tLt912m9atW+dQZ2Zmpu655x75+vrKx8dH/fr105EjRyqt71JlZWX6xz/+YbS/XH5+vh577DE1bdpU3t7eGjRokA4fPuxQ5rPPPtNvfvMbBQUFyd/fX9HR0crPz5ckbdu2TXfddZeaNGkif39/DR482GhTZUpLSzVmzBiFhITIy8tL7du317x58yote3E7hg4dqvfff7/KeusSAgoA1JIXXnhBzz77rLKzsxUdHa158+Zp1qxZevvtt7Vv3z5FR0dr6NChxhfM2bNnNXjwYLVv317p6emKi4vTpEmTHOqcN2+ecnJylJOTo++++05PPfWUWrZsqQ4dOlTYf1lZmQYNGqTU1FStXr1aWVlZ+stf/iI3NzdJUnp6uoYPH66HHnpI+/fvV1xcnF555RWtXLlS0oVzbzIzM42H5iUnJ6t58+ZKTr5wiOz8+fNKTU11eG7NJ598osDAQHXu3FnShUDwzTffaMeOHVq3bp0WLVqkvLw8o/yDDz5o9CcnJ0fvv/++3N3d1bdv30o/05dfflkrVqzQ4sWLlZmZqQkTJujRRx812vTdd9+pf//+8vT01I4dO5Senq7Ro0fr/Pnz1Rqzffv26eTJk+rZs6fD8lGjRmnPnj3avHmz/ud//kd2u1133323MTOWkZGhAQMGqFOnTvr444/16aefasiQISotLZV0YWxjY2OVlpamTz75RA0aNNDvfvc7lZWVVdqOsrIytWnTRmvXrlVWVpZeffVVvfTSS1q7dq1DuYMHD+r3v/+9CgsLJV140vDx48f17bffVqu/ZsY5KABQS2JiYjRs2DDj/dtvv60XXnhBDz30kCRp+vTp+te//qW5c+dq4cKFeu+991RaWqq///3v8vb2VufOnXXixAn96U9/Murw8/OTn5+fpAtX3axYsUKJiYkKDAyssP/t27dr9+7dys7OVrt27SRJbdu2NdbPnj1bAwYM0CuvvCJJateunbKysjRz5kyNGjVKYWFh8vf3V3Jysu6//37t3LlTEydO1Jw5cyRdON+lqKhId911l1Hnpk2bjMM7hw4d0kcffaTPP/9cvXr1kiQtX75cHTt2NMp7eXnJy8tLknTkyBE988wzio+PV2RkZIX+nD17VrNnz9aOHTvUu3dvoz8pKSn629/+pvDwcC1cuFB+fn5KSEiQh4eH0a/q+uabb+Tm5qaWLVsayw4fPqzNmzfrs88+U58+fSRJ7733noKDg7Vx40Y98MADmjFjhnr27KmFCxeqoKBAvr6+DrNP999/v8N+li9frpYtWyorK0thYWEV2uHh4aHXXnvNeB8SEqLU1FStXbtWw4cP19dff60NGzaoVatW+vzzz43PsPwcom+++UY333xztfttRsygAEAtufh/4QUFBfr+++8rzAz07dtX2dnZkqTs7GzddtttDueQlH8RX2rv3r0aNWqUZs6c6RAQLpaRkaE2bdpU+QWdnZ1daXsOHz6s0tJSWSwW9e/fXzt37tTJkyeVmZmpp556SqWlpcrOztbOnTvVvXt3NW7cWNKFwy9btmwxDo9kZ2fL3d3d4XPo0KFDpeeAnDp1SoMHD9agQYP0/PPPV9rerKwsFRUVKTIyUo0bNzZe77zzjnG4JCMjQ/369TPCibMKCwtltVplsVgcPid3d3cjZEmSv7+/2rdvb4xd+QxKVY4cOaIRI0aobdu28vX1NU5iPXbsWJXbLFmyRD179lSLFi3UuHFjLVu2zCi/cuVKNW/eXL6+vkY4kWT8+9y5c1fRe3NhBgUAakllV9Nc/MUnXfhSL19W3atVcnNzNXToUI0ePVojR46sstzFX1yVuXjfFy+7WEREhJYuXapdu3bptttuU5MmTdS/f38lJydr586dioiIMMru3r1bJSUlRmAqr+vSfVyqtLRUDz74oHx9fbVs2bIqy5UfDvnwww8rXG1ktVolXbnPV9K8eXOdO3dOJSUlatiwoaSqx+Xiz+9K+x0yZIiCg4O1bNkyBQUFqaysTGFhYSopKam0/Nq1azVhwgTNmjVLvXv3lo+Pj2bOnKkvvvhCkvT6668rLi5OGzdudNjul19+kXThZOm6jhkUALgOfH19FRQUpJSUFIflqampxiGPTp066csvvzTOJ5BU4VLdoqIi3XvvverQoYPDCa2V6dq1q06cOKFDhw5Vur5Tp06Vtqddu3bGeSrl56GsW7fOCCPh4eHavn17hfNPNm3apHvuucfYtmPHjjp//rz27NljlDl48KBOnjzpsM8JEyZo//792rBhgzw9PavsT6dOnWS1WnXs2DHdeuutDq/g4GCjz7t27brqq6bK7yuSlZXlsN/z588b4UCSfv75Zx06dMgYu65du+qTTz6ptM6ff/5Z2dnZevnllzVgwAB17NjROHm2Krt27VKfPn00btw4devWTbfeeutlT6otd+DAAXl4eBjnANVlBBQAcNKUKVP02GOPOb3d888/r+nTp+uDDz7QwYMH9eKLLyojI0PPPfecJGnEiBFq0KCBxowZo6ysLG3dulVvv/22Qx1jx47V8ePH9de//lU//vijfvjhB+Xm5lb6P/Hw8HD1799f999/v5KSknT06FF99NFHxpVDEydO1CeffKI33nhDhw4d0qpVq7RgwQKHE3PLz0N57733jIASERGhjRs3qrCw0OHw0qWXF7dv316//e1v9cQTT+iLL75Qenq6/vjHPzrMNqxYsUKLFi3SkiVL1KBBA+Xm5io3N1dnzpyp0B8fHx9NmjRJEyZM0KpVq3TkyBHt3btXCxcu1KpVqyRJzzzzjAoKCvTQQw9pz549Onz4sN59910dPHiwWmPUokULde/e3SG4hYaG6t5779UTTzyhlJQUffnll3r00UfVunVro79TpkxRWlqann76aR04cED/+7//q8WLF+unn35S06ZN5e/vr6VLl+qrr77Sjh07FBsbe9l23HrrrdqzZ48+/vhjHTp0SK+88orS0tKu2P5du3apX79+1zyTZAYEFABwUk5OzmXPHajKs88+q4kTJ2rixInq0qWLtm3bps2bNys0NFSS1LhxY23ZskVZWVnq1q2bpk6dqunTpzvUkZycrJycHHXq1EmtW7dWhw4d1Lp1a6Wmpla6z/Xr1+uOO+7Qww8/rE6dOmny5MnGlSXdu3fX2rVrlZCQoLCwML366qt6/fXXNWrUKGN7i8VizJL069dP0oXZAj8/P3Xr1k2+vr6SLpxj8dVXXyk6Otph/ytWrFBwcLDCw8M1bNgwPfnkkw4noCYnJ6u0tFRDhw5Vq1atjNelwazcG2+8oVdffVXTpk1Tx44dFR0drS1bthjndPj7+2vHjh06c+aMwsPD1aNHDy1btsypc1KefPLJCpdDr1ixQj169NDgwYPVu3dv2e12bd261eFE3MTERO3bt08DBw5U3759tWnTJrm7u6tBgwZKSEhQenq6wsLCNGHCBM2cOfOybXjqqac0bNgwPfjgg+rVq5d+/vlnjRs37optf//99/XEE09Uu69mZrHX5C36rpOCggL5+fnp1KlTxi9HTbHZbNq6datWHW8qm73u5Lct4ys/Sa6uKx+Pu++++6pPekPNuZbxKCoq0tGjRxUSEnLZaXxUX1lZmXHFSIMGrv17NXv2bG3fvl1bt251aTtqQlFRkdq3b6+EhIQqT1KuiivH5MMPP9Tzzz+vffv2yd3ddaeYXu533Znv77rzDQwAMK02bdo43LCtLvP09NQ777xj3P+lrjh79qxWrFjh0nBSk26MXgAAXGr48OGubkKNuvjk37riRhsDZlAAAIDpOB1QvvvuOz366KPy9/eXt7e3br/9dqWnpxvr7Xa74uLiFBQUJC8vL+MStYsVFxdr/Pjxat68uRo1aqShQ4fqxIkT194bAABwQ3AqoOTn56tv377y8PDQRx99pKysLM2aNcvhroAzZszQ7NmztWDBAqWlpSkwMFCRkZE6ffq0USYmJkYbNmxQQkKCUlJSdObMGQ0ePNg4sxwAANRvTp2DMn36dAUHB2vFihXGsltuucX4t91u19y5czV16lTj+ROrVq1SQECA1qxZo7Fjx+rUqVNavny53n33XQ0cOFCStHr1agUHB2v79u0VLlEDcGOpgxcOAnBCTf2OOxVQNm/erOjoaD3wwANKTk5W69atNW7cOOOa66NHjyo3N1dRUVHGNlarVeHh4UpNTdXYsWOVnp4um83mUCYoKEhhYWFKTU2tNKAUFxeruLjYeF9QUCDpwiWPV3u3wKqU1+dhsUuq/CmTZlTTn4NZlPfrRu1fXXOt42G323XmzBnjtuS4NuVfBHa7vcqn4uL6YkwufGfb7XbZ7fYKfyuc+dvhVED5+uuvtXjxYsXGxuqll17S7t279eyzz8pqteqxxx5Tbm6uJCkgIMBhu4CAAOPRz7m5uWrYsKGaNm1aoUz59peaNm2aw1MdyyUmJjo8VKsmjWhzslbqrS03wr0HLicpKcnVTcBFrnY8fHx8VFxcrKKiIjVs2PCKz2hB9fz888+ubgIuUV/HxG6368cff9Qvv/yiw4cPV1jvzEMMnQooZWVl6tmzp+Lj4yVJ3bp1U2ZmphYvXuxw2+fLPQyrKpcrM2XKFIfbAhcUFCg4OFhRUVG1cqO2pKQkrTnRRDZ73fnj+cFY524mVFeUj0dkZCQ3ajOBax0Pu92uvLw8YxYU18Zut6uoqEienp6EPZNgTGQ8wbqyvxHO/O47FVBatWqlTp06OSzr2LGj1q9fL0kKDAyUdGGWpFWrVkaZvLw8Y1YlMDBQJSUlys/Pd5hFycvLU58+fSrdr9VqrXRK2MPDo9a+tGx2S526k+yN/uVdm2MN513LeLRp00alpaUctqsBNptNn376qfr378/vh0kwJlLDhg2rvIuuM5+JUwGlb9++FR64dOjQId18882SpJCQEAUGBiopKUndunWTJJWUlCg5Odl4nkSPHj3k4eGhpKQk46YyOTk5OnDggGbMmOFMcwDUUW5ubsYTb3H13NzcdP78eXl6etbbL0OzYUxqjlMBZcKECerTp4/i4+M1fPhw7d69W0uXLtXSpUslXTi0ExMTo/j4eIWGhio0NFTx8fHy9vbWiBEjJEl+fn4aM2aMJk6cKH9/fzVr1kyTJk1Sly5djKt6AABA/eZUQLnjjju0YcMGTZkyRa+//rpCQkI0d+5cPfLII0aZyZMnq7CwUOPGjVN+fr569eqlxMRE+fj4GGXmzJkjd3d3DR8+XIWFhRowYIBWrlzJ/6gAAICkq3gWz+DBgzV48OAq11ssFsXFxSkuLq7KMp6enpo/f77mz5/v7O4BAEA9UHfOAgUAAPUGAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJgOAQUAAJiOUwElLi5OFovF4RUYGGist9vtiouLU1BQkLy8vBQREaHMzEyHOoqLizV+/Hg1b95cjRo10tChQ3XixIma6Q0AALghOD2D0rlzZ+Xk5Biv/fv3G+tmzJih2bNna8GCBUpLS1NgYKAiIyN1+vRpo0xMTIw2bNighIQEpaSk6MyZMxo8eLBKS0trpkcAAKDOc3d6A3d3h1mTcna7XXPnztXUqVM1bNgwSdKqVasUEBCgNWvWaOzYsTp16pSWL1+ud999VwMHDpQkrV69WsHBwdq+fbuio6OvsTsAAOBG4HRAOXz4sIKCgmS1WtWrVy/Fx8erbdu2Onr0qHJzcxUVFWWUtVqtCg8PV2pqqsaOHav09HTZbDaHMkFBQQoLC1NqamqVAaW4uFjFxcXG+4KCAkmSzWaTzWZztguXVV6fh8UuqaxG665NNf05mEV5v27U/tU1jIe5MB7mw5hcnjOfi1MBpVevXnrnnXfUrl07/fDDD3rzzTfVp08fZWZmKjc3V5IUEBDgsE1AQIC+/fZbSVJubq4aNmyopk2bVihTvn1lpk2bptdee63C8sTERHl7ezvThWob0eZkrdRbW7Zu3erqJtSqpKQkVzcBF2E8zIXxMB/GpHLnzp2rdlmnAsqgQYOMf3fp0kW9e/fWr371K61atUp33nmnJMlisThsY7fbKyy71JXKTJkyRbGxscb7goICBQcHKyoqSr6+vs504YpsNpuSkpK05kQT2eyXb7eZfDC2t6ubUCvKxyMyMlIeHh6ubk69x3iYC+NhPozJ5ZUfAakOpw/xXKxRo0bq0qWLDh8+rPvuu0/ShVmSVq1aGWXy8vKMWZXAwECVlJQoPz/fYRYlLy9Pffr0qXI/VqtVVqu1wnIPD49a+wGw2S2y2evOVdg3+i9CbY41nMd4mAvjYT6MSeWc+Uyu6Ru4uLhY2dnZatWqlUJCQhQYGOgwrVVSUqLk5GQjfPTo0UMeHh4OZXJycnTgwIHLBhQAAFC/ODWDMmnSJA0ZMkQ33XST8vLy9Oabb6qgoECPP/64LBaLYmJiFB8fr9DQUIWGhio+Pl7e3t4aMWKEJMnPz09jxozRxIkT5e/vr2bNmmnSpEnq0qWLcVUPAACAUwHlxIkTevjhh/XTTz+pRYsWuvPOO/X555/r5ptvliRNnjxZhYWFGjdunPLz89WrVy8lJibKx8fHqGPOnDlyd3fX8OHDVVhYqAEDBmjlypVyc3Or2Z4BAIA6y6mAkpCQcNn1FotFcXFxiouLq7KMp6en5s+fr/nz5zuzawAAUI/UnbNAAQBAvUFAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApnNNAWXatGmyWCyKiYkxltntdsXFxSkoKEheXl6KiIhQZmamw3bFxcUaP368mjdvrkaNGmno0KE6ceLEtTQFAADcQK46oKSlpWnp0qXq2rWrw/IZM2Zo9uzZWrBggdLS0hQYGKjIyEidPn3aKBMTE6MNGzYoISFBKSkpOnPmjAYPHqzS0tKr7wkAALhhXFVAOXPmjB555BEtW7ZMTZs2NZbb7XbNnTtXU6dO1bBhwxQWFqZVq1bp3LlzWrNmjSTp1KlTWr58uWbNmqWBAweqW7duWr16tfbv36/t27fXTK8AAECd5n41Gz399NO65557NHDgQL355pvG8qNHjyo3N1dRUVHGMqvVqvDwcKWmpmrs2LFKT0+XzWZzKBMUFKSwsDClpqYqOjq6wv6Ki4tVXFxsvC8oKJAk2Ww22Wy2q+lClcrr87DYJZXVaN21qaY/B7Mo79eN2r+6hvEwF8bDfBiTy3Pmc3E6oCQkJOjf//630tLSKqzLzc2VJAUEBDgsDwgI0LfffmuUadiwocPMS3mZ8u0vNW3aNL322msVlicmJsrb29vZLlTLiDYna6Xe2rJ161ZXN6FWJSUluboJuAjjYS6Mh/kwJpU7d+5ctcs6FVCOHz+u5557TomJifL09KyynMVicXhvt9srLLvU5cpMmTJFsbGxxvuCggIFBwcrKipKvr6+TvTgymw2m5KSkrTmRBPZ7Jdvs5l8MLa3q5tQK8rHIzIyUh4eHq5uTr3HeJgL42E+jMnllR8BqQ6nAkp6erry8vLUo0cPY1lpaak+/fRTLViwQAcPHpR0YZakVatWRpm8vDxjViUwMFAlJSXKz893mEXJy8tTnz59Kt2v1WqV1WqtsNzDw6PWfgBsdots9rpzFfaN/otQm2MN5zEe5sJ4mA9jUjlnPhOnvoEHDBig/fv3KyMjw3j17NlTjzzyiDIyMtS2bVsFBgY6TG2VlJQoOTnZCB89evSQh4eHQ5mcnBwdOHCgyoACAADqF6dmUHx8fBQWFuawrFGjRvL39zeWx8TEKD4+XqGhoQoNDVV8fLy8vb01YsQISZKfn5/GjBmjiRMnyt/fX82aNdOkSZPUpUsXDRw4sIa6BQAA6rKruornciZPnqzCwkKNGzdO+fn56tWrlxITE+Xj42OUmTNnjtzd3TV8+HAVFhZqwIABWrlypdzc3Gq6OQAAoA665oCyc+dOh/cWi0VxcXGKi4urchtPT0/Nnz9f8+fPv9bdAwCAG1DdOQsUAADUGwQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOgQUAABgOu6uboBZxZ96QW5lJbVWf2yTebVWNwAAdR0zKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQIKAAAwHScCiiLFy9W165d5evrK19fX/Xu3VsfffSRsd5utysuLk5BQUHy8vJSRESEMjMzHeooLi7W+PHj1bx5czVq1EhDhw7ViRMnaqY3AADghuBUQGnTpo3+8pe/aM+ePdqzZ4/+67/+S/fee68RQmbMmKHZs2drwYIFSktLU2BgoCIjI3X69GmjjpiYGG3YsEEJCQlKSUnRmTNnNHjwYJWWltZszwAAQJ3lVEAZMmSI7r77brVr107t2rXTW2+9pcaNG+vzzz+X3W7X3LlzNXXqVA0bNkxhYWFatWqVzp07pzVr1kiSTp06peXLl2vWrFkaOHCgunXrptWrV2v//v3avn17rXQQAADUPe5Xu2Fpaan+8Y9/6OzZs+rdu7eOHj2q3NxcRUVFGWWsVqvCw8OVmpqqsWPHKj09XTabzaFMUFCQwsLClJqaqujo6Er3VVxcrOLiYuN9QUGBJMlms8lms11tFypVXl9pg4Y1Wu+lPCxlNVpfTX8OZlHerxu1f3UN42EujIf5MCaX58zn4nRA2b9/v3r37q2ioiI1btxYGzZsUKdOnZSamipJCggIcCgfEBCgb7/9VpKUm5urhg0bqmnTphXK5ObmVrnPadOm6bXXXquwPDExUd7e3s52oVq+6hRbK/WWe1z5NVrf1q1ba7Q+s0lKSnJ1E3ARxsNcGA/zYUwqd+7cuWqXdTqgtG/fXhkZGTp58qTWr1+vxx9/XMnJycZ6i8XiUN5ut1dYdqkrlZkyZYpiY/8vMBQUFCg4OFhRUVHy9fV1tguXZbPZlJSUpFuzZsutrKRG677YS37Ta7S+D8b2rtH6zKJ8PCIjI+Xh4eHq5tR7jIe5MB7mw5hcXvkRkOpwOqA0bNhQt956qySpZ8+eSktL07x58/TCCy9IujBL0qpVK6N8Xl6eMasSGBiokpIS5efnO8yi5OXlqU+fPlXu02q1ymq1Vlju4eFRaz8AbmUltRpQbPaavcL7Rv9FqM2xhvMYD3NhPMyHMamcM5/JNX9L2u12FRcXKyQkRIGBgQ7TWiUlJUpOTjbCR48ePeTh4eFQJicnRwcOHLhsQAEAAPWLUzMoL730kgYNGqTg4GCdPn1aCQkJ2rlzp7Zt2yaLxaKYmBjFx8crNDRUoaGhio+Pl7e3t0aMGCFJ8vPz05gxYzRx4kT5+/urWbNmmjRpkrp06aKBAwfWSgcBAEDd41RA+eGHHzRy5Ejl5OTIz89PXbt21bZt2xQZGSlJmjx5sgoLCzVu3Djl5+erV69eSkxMlI+Pj1HHnDlz5O7uruHDh6uwsFADBgzQypUr5ebmVrM9AwAAdZZTAWX58uWXXW+xWBQXF6e4uLgqy3h6emr+/PmaP3++M7sGAAD1CM/iAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApkNAAQAApuNUQJk2bZruuOMO+fj4qGXLlrrvvvt08OBBhzJ2u11xcXEKCgqSl5eXIiIilJmZ6VCmuLhY48ePV/PmzdWoUSMNHTpUJ06cuPbeAACAG4JTASU5OVlPP/20Pv/8cyUlJen8+fOKiorS2bNnjTIzZszQ7NmztWDBAqWlpSkwMFCRkZE6ffq0USYmJkYbNmxQQkKCUlJSdObMGQ0ePFilpaU11zMAAFBnuTtTeNu2bQ7vV6xYoZYtWyo9PV39+/eX3W7X3LlzNXXqVA0bNkyStGrVKgUEBGjNmjUaO3asTp06peXLl+vdd9/VwIEDJUmrV69WcHCwtm/frujo6BrqGgAAqKuu6RyUU6dOSZKaNWsmSTp69Khyc3MVFRVllLFarQoPD1dqaqokKT09XTabzaFMUFCQwsLCjDIAAKB+c2oG5WJ2u12xsbG66667FBYWJknKzc2VJAUEBDiUDQgI0LfffmuUadiwoZo2bVqhTPn2lyouLlZxcbHxvqCgQJJks9lks9mutguVKq+vtEHDGq33Uh6Wshqtr6Y/B7Mo79eN2r+6hvEwF8bDfBiTy3Pmc7nqgPLMM89o3759SklJqbDOYrE4vLfb7RWWXepyZaZNm6bXXnutwvLExER5e3s70erq+6pTbK3UW+5x5ddofVu3bq3R+swmKSnJ1U3ARRgPc2E8zIcxqdy5c+eqXfaqAsr48eO1efNmffrpp2rTpo2xPDAwUNKFWZJWrVoZy/Py8oxZlcDAQJWUlCg/P99hFiUvL099+vSpdH9TpkxRbOz/BYaCggIFBwcrKipKvr6+V9OFKtlsNiUlJenWrNlyKyup0bov9pLf9Bqt74OxvWu0PrMoH4/IyEh5eHi4ujn1HuNhLoyH+TAml1d+BKQ6nAoodrtd48eP14YNG7Rz506FhIQ4rA8JCVFgYKCSkpLUrVs3SVJJSYmSk5M1ffqFL+QePXrIw8NDSUlJGj58uCQpJydHBw4c0IwZMyrdr9VqldVqrbDcw8Oj1n4A3MpKajWg2Ow1ewuaG/0XoTbHGs5jPMyF8TAfxqRyznwmTgWUp59+WmvWrNGmTZvk4+NjnDPi5+cnLy8vWSwWxcTEKD4+XqGhoQoNDVV8fLy8vb01YsQIo+yYMWM0ceJE+fv7q1mzZpo0aZK6dOliXNUDAADqN6cCyuLFiyVJERERDstXrFihUaNGSZImT56swsJCjRs3Tvn5+erVq5cSExPl4+NjlJ8zZ47c3d01fPhwFRYWasCAAVq5cqXc3NyurTcAAOCG4PQhniuxWCyKi4tTXFxclWU8PT01f/58zZ8/35ndAwCAeoJn8QAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANMhoAAAANNxOqB8+umnGjJkiIKCgmSxWLRx40aH9Xa7XXFxcQoKCpKXl5ciIiKUmZnpUKa4uFjjx49X8+bN1ahRIw0dOlQnTpy4po4AAIAbh9MB5ezZs7rtttu0YMGCStfPmDFDs2fP1oIFC5SWlqbAwEBFRkbq9OnTRpmYmBht2LBBCQkJSklJ0ZkzZzR48GCVlpZefU8AAMANw93ZDQYNGqRBgwZVus5ut2vu3LmaOnWqhg0bJklatWqVAgICtGbNGo0dO1anTp3S8uXL9e6772rgwIGSpNWrVys4OFjbt29XdHT0NXQHAADcCJwOKJdz9OhR5ebmKioqylhmtVoVHh6u1NRUjR07Vunp6bLZbA5lgoKCFBYWptTU1EoDSnFxsYqLi433BQUFkiSbzSabzVaTXTDqK23QsEbrvZSHpaxG66vpz8Esyvt1o/avrmE8zIXxMB/G5PKc+VxqNKDk5uZKkgICAhyWBwQE6NtvvzXKNGzYUE2bNq1Qpnz7S02bNk2vvfZaheWJiYny9vauiaZX8FWn2Fqpt9zjyq/R+rZu3Vqj9ZlNUlKSq5uAizAe5sJ4mA9jUrlz585Vu2yNBpRyFovF4b3dbq+w7FKXKzNlyhTFxv5fYCgoKFBwcLCioqLk6+t77Q2+iM1mU1JSkm7Nmi23spIarftiL/lNr9H6Phjbu+LCFXfX6D4q9YfaDUbl4xEZGSkPD49a3ReujPEwF8bDfBiTyys/AlIdNRpQAgMDJV2YJWnVqpWxPC8vz5hVCQwMVElJifLz8x1mUfLy8tSnT59K67VarbJarRWWe3h41NoPgFtZSa0GFJu9Zq/wrvRzsNde+y/ace3vQ7U71nAe42EujIf5MCaVc+YzqdFvyZCQEAUGBjpMbZWUlCg5OdkIHz169JCHh4dDmZycHB04cKDKgAIAAOoXp2dQzpw5o6+++sp4f/ToUWVkZKhZs2a66aabFBMTo/j4eIWGhio0NFTx8fHy9vbWiBEjJEl+fn4aM2aMJk6cKH9/fzVr1kyTJk1Sly5djKt6AABA/eZ0QNmzZ49+85vfGO/Lzw15/PHHtXLlSk2ePFmFhYUaN26c8vPz1atXLyUmJsrHx8fYZs6cOXJ3d9fw4cNVWFioAQMGaOXKlXJzc6uBLgEAgLrO6YASEREhu91e5XqLxaK4uDjFxcVVWcbT01Pz58/X/Pnznd09AACoB3gWDwAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAAMB0CCgAAMJ0afZoxXGfI/JQKy2afPFPr+42tZL/VtWX8XTXYEgDAjYQZFAAAYDoEFAAAYDoEFAAAYDoEFAAAYDoEFAAAYDoEFAAAYDpcZoy6YcXdkr2k9uofm1x7dQMAnEZAgctUdu+WS3lYyvR4sHTkxzNyK6u9gFLd+7lw7xYAuD44xAMAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEyHgAIAAEzHpQFl0aJFCgkJkaenp3r06KFdu3a5sjkAAMAkXBZQPvjgA8XExGjq1Knau3ev+vXrp0GDBunYsWOuahIAADAJd1ftePbs2RozZoz++Mc/SpLmzp2rjz/+WIsXL9a0adNc1Syg9vwtvPb3MTa5wqIh81NqrHoPS5keD5Ye/Nv/yGbnCPHFtoy/y9VNAG4oLgkoJSUlSk9P14svvuiwPCoqSqmpqRXKFxcXq7i42Hh/6tQpSdIvv/wim81Wo22z2Ww6d+6cTpU0kFtZLf4BLjpde3X/x6mS6/AFUtv9sNhNNR5DZn501bv4c8G5q962ul67hvZVy3/GQ0Uekt1Su/uqY67lZ+NqeVjseqD1OT04L0m2qxyPlaN/XcOtqt/Kv0N+/vlneXh4GMtH/X23C1t1dWrjZ+P06Qt/a+12+5UL213gu+++s0uyf/bZZw7L33rrLXu7du0qlP/zn/9sl8SLFy9evHjxugFex48fv2JWcNkhHkmyWBwTv91ur7BMkqZMmaLY2FjjfVlZmX755Rf5+/tXWv5aFBQUKDg4WMePH5evr2+N1g3nMR7mwniYC+NhPozJ5dntdp0+fVpBQUFXLOuSgNK8eXO5ubkpNzfXYXleXp4CAgIqlLdarbJarQ7LmjRpUptNlK+vLz9cJsJ4mAvjYS6Mh/kwJlXz8/OrVjmXnOXWsGFD9ejRQ0lJSQ7Lk5KS1KdPH1c0CQAAmIjLDvHExsZq5MiR6tmzp3r37q2lS5fq2LFjeuqpp1zVJAAAYBIuCygPPvigfv75Z73++uvKyclRWFiYtm7dqptvvtlVTZJ04XDSn//85wqHlOAajIe5MB7mwniYD2NScyx2e3Wu9QEAALh+uNMSAAAwHQIKAAAwHQIKAAAwHQIKAAAwHQLKRRYtWqSQkBB5enqqR48e2rVrl6ubVG9NmzZNd9xxh3x8fNSyZUvdd999OnjwoKubhf+YNm2aLBaLYmJiXN2Ueuu7777To48+Kn9/f3l7e+v2229Xenq6q5tVL50/f14vv/yyQkJC5OXlpbZt2+r1119XWVmZq5tWpxFQ/uODDz5QTEyMpk6dqr1796pfv34aNGiQjh075uqm1UvJycl6+umn9fnnnyspKUnnz59XVFSUzp496+qm1XtpaWlaunSpunbt6uqm1Fv5+fnq27evPDw89NFHHykrK0uzZs2q9Ttso3LTp0/XkiVLtGDBAmVnZ2vGjBmaOXOm5s+f7+qm1WlcZvwfvXr1Uvfu3bV48WJjWceOHXXfffdp2rRpLmwZJOnHH39Uy5YtlZycrP79+7u6OfXWmTNn1L17dy1atEhvvvmmbr/9ds2dO9fVzap3XnzxRX322WfM8prE4MGDFRAQoOXLlxvL7r//fnl7e+vdd991YcvqNmZQJJWUlCg9PV1RUVEOy6OiopSamuqiVuFip06dkiQ1a9bMxS2p355++mndc889GjhwoKubUq9t3rxZPXv21AMPPKCWLVuqW7duWrZsmaubVW/ddddd+uSTT3To0CFJ0pdffqmUlBTdfffdLm5Z3ebSpxmbxU8//aTS0tIKDyoMCAio8EBDXH92u12xsbG66667FBYW5urm1FsJCQn697//rbS0NFc3pd77+uuvtXjxYsXGxuqll17S7t279eyzz8pqteqxxx5zdfPqnRdeeEGnTp1Shw4d5ObmptLSUr311lt6+OGHXd20Oo2AchGLxeLw3m63V1iG6++ZZ57Rvn37lJKS4uqm1FvHjx/Xc889p8TERHl6erq6OfVeWVmZevbsqfj4eElSt27dlJmZqcWLFxNQXOCDDz7Q6tWrtWbNGnXu3FkZGRmKiYlRUFCQHn/8cVc3r84ioEhq3ry53NzcKsyW5OXlVZhVwfU1fvx4bd68WZ9++qnatGnj6ubUW+np6crLy1OPHj2MZaWlpfr000+1YMECFRcXy83NzYUtrF9atWqlTp06OSzr2LGj1q9f76IW1W/PP/+8XnzxRT300EOSpC5duujbb7/VtGnTCCjXgHNQJDVs2FA9evRQUlKSw/KkpCT16dPHRa2q3+x2u5555hn9v//3/7Rjxw6FhIS4ukn12oABA7R//35lZGQYr549e+qRRx5RRkYG4eQ669u3b4XL7g8dOuTyh63WV+fOnVODBo5fp25ublxmfI2YQfmP2NhYjRw5Uj179lTv3r21dOlSHTt2TE899ZSrm1YvPf3001qzZo02bdokHx8fY3bLz89PXl5eLm5d/ePj41Ph/J9GjRrJ39+f84JcYMKECerTp4/i4+M1fPhw7d69W0uXLtXSpUtd3bR6aciQIXrrrbd00003qXPnztq7d69mz56t0aNHu7ppdZsdhoULF9pvvvlme8OGDe3du3e3Jycnu7pJ9ZakSl8rVqxwddPwH+Hh4fbnnnvO1c2ot7Zs2WIPCwuzW61We4cOHexLly51dZPqrYKCAvtzzz1nv+mmm+yenp72tm3b2qdOnWovLi52ddPqNO6DAgAATIdzUAAAgOkQUAAAgOkQUAAAgOkQUAAAgOkQUAAAgOkQUAAAgOkQUAAAgOkQUAAAgOkQUAAAgOkQUAAAgOkQUAAAgOkQUAAAgOn8f6QXiG6VMLSbAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# OK, widzimy już co nieco jak to trochę działało. Na uwagę przykuwa to, że to, czy ocalał, nie ma \n",
    "# 100% korelacji z tym, czy miał łódkę - przyjrzymy się temu bliżej. Widać, że fakt posiadania kabiny był \n",
    "# pewnym luksusem, który korelował z ceną, klasą biletu, a nawet miało jakieś znaczenie w tym, czy ten ktoś ocalał\n",
    "# widać, że wiek koreluje z klasą biletu - wiadomo - statystycznie tych starszych bardziej może na to stać, jak\n",
    "# i może się przekładać na rosnącą potrzebę komfortu wraz z wiekiem\n",
    "\n",
    "# W każdym razie na wszelki wypadek narysujmy sobie jeszcze rozkłady w zależności \"ocalał\" od innych czynników\n",
    "# aby zobaczyć, czy da się z tego coś wyciągnąć\n",
    "plt.clf()\n",
    "\n",
    "for k, desc in [\n",
    "    ('klasa_biletu', 'klasa biletu'),\n",
    "    ('wiek', 'Wiek'),\n",
    "    ('oplata', 'Opłata'),\n",
    "    ('l_rdz_młż', 'l. rodzeństwa/małż'),\n",
    "    ('l_dzieci_rodz', 'l. rodziców/dzieci'),\n",
    "    ('mial_lodke', 'Czy miał łódkę'),\n",
    "    ('mial_kabine', 'Czy miał kabinę'),\n",
    "    ('cena_1os', 'Cena za osobę'),\n",
    "]:\n",
    "    df4 = df3[df3[k].notnull()][['ocalal', k]]\n",
    "    for survived in [0, 1]:\n",
    "        add_to_title = ' (ocalał)' if survived else ' (nie ocalał)'\n",
    "        df5 = df4[df4['ocalal'] == survived][k].copy()\n",
    "        df5.name = desc + add_to_title\n",
    "        # print(df5)\n",
    "        # df4.plot(kind='hist', subplots=True, sharex=True, sharey=True, title=k + add_to_title)\n",
    "        df5.hist(legend=True, alpha=0.8)\n",
    "    plt.show()\n",
    "    plt.clf()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d78a5e38-dddf-4623-b6ec-799057170be4",
   "metadata": {},
   "source": [
    "## Wnioski z wykresów\n",
    "- To co się rzuca w oczy, to klasa biletu zdawała się mieć kluczowe znaczenie na fakt przeżycia, albo nie Mniej więcej tyle samo osób ocalało z klasy 1 co i z klasy 3, ale za to zginęło prawie 5x więcej z klasy 3 co z klasy 1 A to jest bardzo duża dysproporcja.\n",
    "\n",
    "- Co do kwestii wieku, to raczej nie odgrywa on zbyt wielkiej roli, ale dzięki rozkładowi można było zobaczyć jeden szczegół niewidoczny na tablicy korelacji - tzn. starano się jednak uratować przede wszystkim dzieci...\n",
    "\n",
    "- Widać też dysproporcję pomiędzy tymi, co ocaleli i nie mieli kabiny od tych, co mieli kabinę wobec proporcji tych, co nie ocaleli i nie mieli kabiny od tych, co mieli kabinę. Tutaj ci co mieli kabinę - to właśnie byli VIP-ami i najwyraźniej mieli pierwszeństwo do łódek ratunkowych.\n",
    "\n",
    "- Tak jak wcześniej zauważyłem - Będąc kobietą miałeś 72.7% szans na przeżycie, a będąc mężczyzną miałeś 19.1% szans na przeżycie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9936308c-bd86-49d7-b26c-218502999ee3",
   "metadata": {},
   "outputs": [
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       "      <td>23.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>B</td>\n",
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       "      <td>0</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>7.8792</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.6833</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      klasa_biletu  ocalal plec  wiek  l_rdz_młż  l_dzieci_rodz    oplata  \\\n",
       "19             1.0     0.0    M  36.0        0.0            0.0   75.2417   \n",
       "166            1.0     0.0    M   NaN        0.0            0.0   30.6958   \n",
       "192            1.0     1.0    K  58.0        0.0            0.0  146.5208   \n",
       "358            2.0     1.0    K  42.0        0.0            0.0   13.0000   \n",
       "395            2.0     1.0    K  18.0        0.0            1.0   23.0000   \n",
       "396            2.0     1.0    K  34.0        0.0            1.0   23.0000   \n",
       "458            2.0     1.0    K  17.0        0.0            0.0   10.5000   \n",
       "489            2.0     1.0    K  42.0        1.0            0.0   26.0000   \n",
       "513            2.0     1.0    K  14.0        1.0            0.0   30.0708   \n",
       "544            2.0     0.0    M  34.0        1.0            0.0   21.0000   \n",
       "545            2.0     1.0    K  30.0        3.0            0.0   21.0000   \n",
       "572            2.0     1.0    K  28.0        0.0            0.0   12.6500   \n",
       "655            3.0     0.0    M  32.0        1.0            0.0   15.8500   \n",
       "656            3.0     1.0    K  33.0        3.0            0.0   15.8500   \n",
       "780            3.0     1.0    K  23.0        0.0            0.0    8.0500   \n",
       "853            3.0     0.0    M  25.0        0.0            0.0    7.2500   \n",
       "860            3.0     1.0    K  26.0        0.0            0.0    7.9250   \n",
       "870            3.0     1.0    K  27.0        0.0            0.0    7.9250   \n",
       "921            3.0     0.0    M   NaN        0.0            0.0    7.2500   \n",
       "926            3.0     1.0    M   NaN        0.0            0.0    7.7500   \n",
       "968            3.0     0.0    M  36.0        1.0            0.0   15.5500   \n",
       "969            3.0     0.0    K  30.0        1.0            0.0   15.5500   \n",
       "1000           3.0     1.0    M   NaN        0.0            0.0    7.7500   \n",
       "1007           3.0     1.0    K  15.0        0.0            0.0    8.0292   \n",
       "1037           3.0     1.0    K   NaN        0.0            0.0    7.2292   \n",
       "1071           3.0     1.0    K   NaN        1.0            0.0   15.5000   \n",
       "1078           3.0     1.0    K   NaN        0.0            0.0    7.8792   \n",
       "1094           3.0     1.0    K  31.0        0.0            0.0    8.6833   \n",
       "1198           3.0     1.0    K   NaN        0.0            0.0    7.7792   \n",
       "1290           3.0     1.0    K  47.0        1.0            0.0    7.0000   \n",
       "1299           3.0     0.0    M  27.0        1.0            0.0   14.4542   \n",
       "1300           3.0     1.0    K  15.0        1.0            0.0   14.4542   \n",
       "\n",
       "     kabina port lodz  cialo                                 dest  mial_lodke  \\\n",
       "19       C6    C    A    NaN                         Winnipeg, MN           1   \n",
       "166     NaN    C   14    NaN                         New York, NY           1   \n",
       "192     B80    C  NaN    NaN                                  NaN           0   \n",
       "358     NaN    S  NaN    NaN                         New York, NY           0   \n",
       "395     NaN    S  NaN    NaN                          Southampton           0   \n",
       "396     NaN    S  NaN    NaN                          Southampton           0   \n",
       "458     NaN    S  NaN    NaN                             Guernsey           0   \n",
       "489     NaN    S  NaN    NaN          Weston-Super-Mare, Somerset           0   \n",
       "513     NaN    C  NaN    NaN                         New York, NY           0   \n",
       "544     NaN    S   12    NaN                        Elizabeth, NJ           1   \n",
       "545     NaN    S  NaN    NaN                        Elizabeth, NJ           0   \n",
       "572     NaN    S  NaN    NaN                         Columbus, OH           0   \n",
       "655     NaN    S    D    NaN  Ruotsinphytaa, Finland New York, NY           1   \n",
       "656     NaN    S  NaN    NaN  Ruotsinphytaa, Finland New York, NY           0   \n",
       "780     NaN    S  NaN    NaN                  London New York, NY           0   \n",
       "853     NaN    S    B    NaN                                  NaN           1   \n",
       "860     NaN    S  NaN    NaN                                  NaN           0   \n",
       "870     NaN    S  NaN    NaN                                  NaN           0   \n",
       "921     NaN    S    A    NaN                                  NaN           1   \n",
       "926     NaN    Q  NaN    NaN                                  NaN           0   \n",
       "968     NaN    S    A    NaN                                  NaN           1   \n",
       "969     NaN    S    A    NaN                                  NaN           1   \n",
       "1000    NaN    Q  NaN    NaN                                  NaN           0   \n",
       "1007    NaN    Q  NaN    NaN                                  NaN           0   \n",
       "1037    NaN    C  NaN    NaN                                  NaN           0   \n",
       "1071    NaN    Q  NaN    NaN                                  NaN           0   \n",
       "1078    NaN    Q  NaN    NaN                                  NaN           0   \n",
       "1094    NaN    S  NaN    NaN                                  NaN           0   \n",
       "1198    NaN    Q  NaN    NaN                                  NaN           0   \n",
       "1290    NaN    S  NaN    NaN                                  NaN           0   \n",
       "1299    NaN    C    C    NaN                                  NaN           1   \n",
       "1300    NaN    C  NaN    NaN                                  NaN           0   \n",
       "\n",
       "      mial_kabine  \n",
       "19              1  \n",
       "166             0  \n",
       "192             1  \n",
       "358             0  \n",
       "395             0  \n",
       "396             0  \n",
       "458             0  \n",
       "489             0  \n",
       "513             0  \n",
       "544             0  \n",
       "545             0  \n",
       "572             0  \n",
       "655             0  \n",
       "656             0  \n",
       "780             0  \n",
       "853             0  \n",
       "860             0  \n",
       "870             0  \n",
       "921             0  \n",
       "926             0  \n",
       "968             0  \n",
       "969             0  \n",
       "1000            0  \n",
       "1007            0  \n",
       "1037            0  \n",
       "1071            0  \n",
       "1078            0  \n",
       "1094            0  \n",
       "1198            0  \n",
       "1290            0  \n",
       "1299            0  \n",
       "1300            0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dziwne, że są tacy. co mieli łódkę, ale zginęli. Są też tacy, co łódki nie mieli, ale przeżyli... \n",
    "# Spójrzmy na nich\n",
    "df2[((df2['ocalal'] == 1) ^ (df2['mial_lodke'] == 1))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cd1e17fb-8281-4778-84e2-d7e183c17746",
   "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>klasa_biletu</th>\n",
       "      <th>ocalal</th>\n",
       "      <th>plec</th>\n",
       "      <th>wiek</th>\n",
       "      <th>l_rdz_młż</th>\n",
       "      <th>l_dzieci_rodz</th>\n",
       "      <th>oplata</th>\n",
       "      <th>kabina</th>\n",
       "      <th>port</th>\n",
       "      <th>lodz</th>\n",
       "      <th>cialo</th>\n",
       "      <th>dest</th>\n",
       "      <th>mial_lodke</th>\n",
       "      <th>mial_kabine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.2417</td>\n",
       "      <td>C6</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Winnipeg, MN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris /  New York, NY</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61.3792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Geneva, Switzerland / Radnor, PA</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>K</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East Providence, RI</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>F G63</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Perkins County, SD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>K</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      klasa_biletu  ocalal plec  wiek  l_rdz_młż  l_dzieci_rodz   oplata  \\\n",
       "19             1.0     0.0    M  36.0        0.0            0.0  75.2417   \n",
       "235            1.0     1.0    M   NaN        0.0            0.0  39.6000   \n",
       "317            1.0     1.0    M  21.0        0.0            1.0  61.3792   \n",
       "603            3.0     1.0    K  35.0        1.0            1.0  20.2500   \n",
       "605            3.0     1.0    M  25.0        0.0            0.0   7.6500   \n",
       "630            3.0     1.0    M  27.0        0.0            0.0   7.7958   \n",
       "881            3.0     1.0    M  21.0        0.0            0.0   7.7958   \n",
       "921            3.0     0.0    M   NaN        0.0            0.0   7.2500   \n",
       "968            3.0     0.0    M  36.0        1.0            0.0  15.5500   \n",
       "969            3.0     0.0    K  30.0        1.0            0.0  15.5500   \n",
       "1088           3.0     1.0    M  32.0        0.0            0.0   7.7750   \n",
       "\n",
       "     kabina port lodz  cialo                              dest  mial_lodke  \\\n",
       "19       C6    C    A    NaN                      Winnipeg, MN           1   \n",
       "235     NaN    S    A    NaN             Paris /  New York, NY           1   \n",
       "317     NaN    C    A    NaN  Geneva, Switzerland / Radnor, PA           1   \n",
       "603     NaN    S    A    NaN               East Providence, RI           1   \n",
       "605   F G63    S    A    NaN                Perkins County, SD           1   \n",
       "630     NaN    S    A    NaN                               NaN           1   \n",
       "881     NaN    S    A    NaN                               NaN           1   \n",
       "921     NaN    S    A    NaN                               NaN           1   \n",
       "968     NaN    S    A    NaN                               NaN           1   \n",
       "969     NaN    S    A    NaN                               NaN           1   \n",
       "1088    NaN    S    A    NaN                               NaN           1   \n",
       "\n",
       "      mial_kabine  \n",
       "19              1  \n",
       "235             0  \n",
       "317             0  \n",
       "603             0  \n",
       "605             1  \n",
       "630             0  \n",
       "881             0  \n",
       "921             0  \n",
       "968             0  \n",
       "969             0  \n",
       "1088            0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Niesamowita sprawa - najwięcej z tych, którzy ocaleli pomimo braku łódki - to były kobiety... \n",
    "# Jest to o tyle dziwne, że za burtą było znacznie więcej mężczyzn, niż kobiet.\n",
    "# Czy to może spowodowane tym, że były na jakieś łodzi, ale tych danych brakuje? \n",
    "# Czy to może dlatego, że ci z łodzi ratowali kobiety i dobijali ponad miarową ilość?\n",
    "# przyjrzyjmy się też łodzi A - coś się z nią stało? Czy zatonęła? Najwięcej tych, \n",
    "# którzy nie przeżyli pomimo posiadania łodzi, to było w łodzi A. Ile tam było osób?\n",
    "\n",
    "df2[df2['lodz'] == 'A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87a12738-bb84-4130-8668-14f13593cdee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Jednak więcej z tej łodzi się uratowała. Może miała jakąś wywrotkę, zatonięcie,\n",
    "# albo był tam jakiś inny incydent?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "b679bd6e-609e-4b78-b9a3-03dea4b08bb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o0KFDk+NryKBBg3D58uU6v7e16gs2Tz31FM6ePSsezgQenGhbez5M//79cfPmTfzwww/17kU5c+YMpkyZgr/85S/ie1y7dq3e82kA4LvvvsPt27exadMmqFQPDsRfvHixWfVlZ2fj+eefb1ZbImoYA4pEmZiYiLv5a0/0e5iNjQ2WL1+OtWvXYu3atXBzc4Narca1a9dgaWkJlUqF0NBQ5OfnY8+ePeLu9pqaGpiamqJ79+4ICgrCvHnzxJNkc3NzUVhYiMDAQISGhiIvLw9ffvklfvvtN/F97e3txT968+fPF7/gHz65taCgAM899xxmzpyJgIAA8Q6yJiYmbfI/y+eeew579uzB7NmzYWpqWu8fj549e6KqqgpRUVGYNGkSzp07V+deNOHh4Rg3bhx69+6NoqIinDx5Uqy/V69e+Oijj3D8+HG4urpiz549SE9PF6+iaczatWvRsWNHODo6YvXq1XBwcMDUqVObVVtERAReeeUV2NraYty4cVCr1bh48SKKioqwdOlSdO7cGRYWFjh27BhcXFxgbm4OOzs7PPvss1i6dCk+//xz9OzZE1u3bm3yiqiBAweiU6dOOHfuHCZOnNis8dXn9ddfx7Bhw7B48WLxhN6rV68iJSVF3CP1qL/+9a8IDAzEoEGDMGbMGHz66ac4dOgQTpw4AQDw8fHByJEjMX36dERGRqJXr1747rvvIJPJMHbsWPTq1QsHDx5EWloaOnTogMjISBQUFDQYULp27QozMzNERUVh4cKFyM7Oxrp165qs7fr167h161a9oZ+IdMNDPBJma2sLW1vbBuevW7cOM2fOxJIlS+Dm5gZ3d3esX79eDDSpqanIy8uDu7s7nJyc4OTkhC5duuDrr78G8OCyzeeffx6LFi1Cnz59sGDBAvFSz9TUVOTn56Nv377isk5OTkhLSxPf383NDd7e3nB3d9c60fC7777Dr7/+ivj4eK1la0+obQvPP/884uPjMXv2bK1LuGs9/fTTiIyMxDvvvAMPDw98/PHH2Lhxo1ab6upqLF68GE899RTGjh0Ld3d38QTahQsXYtq0aZgxYwaGDh2KO3fuaO1NacymTZvw6quvwsvLC/n5+Thy5Eizr7aZP38+PvzwQ8TFxcHT0xM+Pj6Ii4sTg5GpqSl27NiBmJgYODs7Y8qUKQAeHEIMCgrCnDlz4OPjA1dX10b3ngAPAua8efO0zh1pif79+yM1NRXXrl3Dn//8ZwwcOBBvvvmmeB5SfaZOnYrt27fjH//4B/r164eYmBjs3r1bvGwaAA4ePIghQ4bghRdeQN++fbFixQpxz9Gbb76JQYMGISAgAKNGjYJSqWw0BHbq1AlxcXH45JNP0LdvX2zatAnvvvtuk7Xt27cP/v7+6NatW7M/DyKqn0wwwMdqlpSUwM7ODsXFxXX+gFdUVCAnJweurq4tuhqkpqYGJSUlsLW1NagnIqvVagwbNgxnzpwRL32tjz7rEwQBffr0QWhoKJYuXfpYfemToa7D5mrL+n799Vf069cPGRkZrfZH2FDXn1qthpubG/bt24cRI0bU26b2+8nFxQUnT57E+PHjm3U5uaHRaDQ4evQo63uIYV5mXKT3ddjY3+9HGc7WT43Kzs6GRqOBtbU1qqqqWv39CgsLERkZiVu3bmHu3Lmt/n4kDY6OjoiNjcWNGzfaeiiSk5ubi9WrVzcYTohINzwHxUj07t0bCoUCXbp0wfvvv48JEya06vs5OjrCwcEB77///mOdMEmGp/YwEWnr3bt3g5c5E5HuGFCMhI2NDTIyMv5r72eARwaJiMiAPNYhno0bN0Imk2ndgVMQBERERMDZ2RkWFhYYNWoULl++rLWcWq1GWFgYHBwcYGVlhcmTJ+PmzZuPMxQiIiIyIi0OKOnp6Xj//fe17iAJAJs3b0ZkZCR27tyJ9PR0KJVK+Pn54d69e2Kb8PBwJCUlITExEWfPnkVpaSkmTpzIJ4ASERERgBYGlNLSUrz44ov44IMPtM4/EAQB27Ztw+rVqzFt2jR4eHggPj4e9+/fR0JCAgCguLgYsbGx2LJlC3x9fTFw4EDs3bsXly5dEu9pQERERE+2Fp2DsnjxYkyYMAG+vr54++23xek5OTkoKCjQenCWQqGAj48P0tLSEBoaioyMDGg0Gq02zs7O8PDwQFpaWr13Ta19bkatkpISAA8u9Xr09twajUZ8rkdLbptee25FbR/GxtjrA4y/RtZnuGpqaiAIgnilXVOPFzBUtXWxvj/IZYb1uyyXPdgO9b0OdelP54CSmJiI//u//0N6enqdebV3DH304VmOjo7Izc0V25iZmdW58sPR0VFc/lEbN27UelJsreTkZK3bqwMPbkylVCpRWlra4uegANA6JGWMjL0+wPhrZH2Gp7KyEuXl5eIND1NSUtp4RK2L9f0hSNWKA2lF+l6HujxIU6eAkpeXh1dffRXJycmN3gStsYd6NaSxNitXrtS6EVhJSQlUKhX8/f3rvVFbXl4erK2tW3SjNkEQcO/ePdjY2DTrgW2Gpi3qu3v3Lnbs2IGXXnoJSqWy1d+P69CwGXN9FRUVsLCwgLe3N06fPg0/Pz+jvZFZSkoK63vIjJjzrTwq/ZLLBMxyuav3dVh7BKQ5dAooGRkZKCwshJeXlzituroap0+fxs6dO/H9998DeLCX5OHbVhcWFop7VZRKJSorK+s8cKywsBDe3t71vq9CoRAfgPcwuVxe54Orrq6GTCZDu3btWnQXytpdyrV9GIOIiAgcPnwYWVlZbVLfvHnz0LdvXzg7Ozd7mYfHXJ9Tp05h9OjRKCoqQvv27bXm1dZoamqKpKSkBm9p3r17d4SHh2tdhfbf1FgNteLi4hAeHi4+J6f2czl16lSb/Y6OHDkSCxcuxKxZs1ql/0d/R5vzOT0qODgYd+/exeHDhwE8eNSBt7d3m9/xuF27dpDJZDA1ffDVW993mDFhfX/QCIb29+TBdqjvdahLXzoFlDFjxuDSpUta0+bOnYs+ffrg9ddfR48ePaBUKpGSkoKBAwcCeLBLMzU1Fe+88w4AwMvLC3K5HCkpKQgMDAQA5OfnIzs7u8HHl+tNjE+TTWQArKurIavnAX06C03VqXlwcDDi4+MRGhpa50F1ixYtQnR0NIKCghAXF6dTv8uXL0dYWJhOy+jLli1bYGNjg/Xr1+u0XFNj9vb2Rn5+Puzs7B53iJI2Y8YMjB8/vsXLX79+Ha6ursjMzMTTTz/92OP57LPPUFBQ0OBToaXq73//O0aPHo358+c3eXttIpIGnQKKjY0NPDw8tKZZWVmhY8eO4vTw8HBs2LABbm5ucHNzw4YNG2BpaSn+b8vOzg4hISFYtmwZOnbsCHt7eyxfvhyenp58AigAlUqFxMREbN26FRYWFgAe7Bbet28funbt2qI+ra2tG30+T2tatmxZi5ZrasxmZmb/lcNFbc3CwkL8PZCCHTt2YO7cuQa3d7F///7o3r07Pv74Y7z88sttPRwiaga9f8usWLEC4eHhWLRoEQYPHoxbt24hOTkZNjY2YputW7di6tSpCAwMxIgRI2BpaYlPP/1UfArvk2zQoEHo2rWr1hN3Dx06BJVKJe6VqiUIAjZv3owePXpAoVBgwIAB+Oqrr8T5wcHBkMlkWj8mJiY4e/bBQ6vUajVWrFgBlUoFhUIBNzc3xMbGNrisTCbDqVOnsHbtWnh6etYZu5eXF/7+978DQL3Ldu/eHQDEwxNffvklBg8eDEtLS3h7e4uHCIEHhzIa+x9/bR+1hz6aY+3atXB0dGzwsFFkZCQ8PT1hZWUFlUqFRYsWobS0VJyfm5uLSZMmoUOHDrCyskK/fv1w9OhRAA8OLYaEhMDV1RUWFhZwd3fH9u3bmzWuc+fOYcCAATA3N8fQoUO19lLGxcU1eVhj9+7deOqpp2Bubo4+ffqIT1gGID7VeODAgZDJZOLTf0eNGlXn0NbUqVMRHBzc4Pvcvn0bJ06cwOTJk7Wm37hxA1OmTIG1tTVsbW0RGBiIX3/9VavNkSNHMHjwYJibm8PBwQHTpk0T5+3duxeDBw+GjY0NlEolXnzxRfz2228NjuPOnTt44YUX4OLiAktLS3h6emLfvn31tr127Zr478mTJzfYjoik57EDyqlTp7Bt2zbxtUwmQ0REBPLz81FRUYHU1NQ6e13Mzc0RFRWFO3fu4P79+/j000+hUhnoKc6tYO7cudi9e7f4+p///CfmzZtXp93f/vY3vP3224iIiMC3336L8ePHY8KECcjPzwcAbN++Hfn5+eLPq6++is6dO8PNzQ0AMGfOHCQmJmLHjh24evUq3nvvPXGvRUPL9unTB/PmzcOVK1e0ruT69ttvkZmZKf6Be3jZH3/8Eb169cLIkSO1xr969Wps2bIFFy9ehKmpab016oMgCHj11VcRGxuLs2fPNhh82rVrhx07diA7Oxvx8fE4efIkVqxYIc5fvHgx1Go1Tp8+jUuXLuGdd94RP6+amhq4uLjgwIEDuHLlCv7+979j1apVOHDgQJPj++tf/4p3330X6enp6Ny5MyZPntzsS/E++OADrF69GuvXr8fVq1exYcMGvPnmm4iPjwcAfP311wCAEydOID8/Xyv46urs2bOwtLTEU089JU4TBAFTp07F77//jtTUVKSkpOCnn37CjBkzxDaff/45pk2bhgkTJiAzM1MMprUqKyuxbt06fPPNNzh8+DCuX7+ORYsWNTiOiooKeHl54bPPPkN2djZeeuklzJ49G//5z3+02qWkpODll18WL1v+05/+hK+//lrrlgVEJF18Fo8EzZ49GytXrsT169chk8lw7tw5JCYm4tSpU2KbsrIyREZGYv369ZgzZw6AB5djp6SkYNeuXXj77bdhZ2cnnqNx6NAhxMTEIDk5GY6Ojvjhhx9w4MABpKSkiIfWevToIfb/6LLvvfceTpw4IR5WCQgIwO7duzFkyBAAD/4X7+PjI/ZR204QBEyfPh12dnaIiYnRqnP9+vXw8XlwXtAbb7yBCRMmoKKiokVXXzWkqqoKc+bMwcWLF3Hu3Dm4uLg02PbhPQqurq5Yt24dXn75ZXGPxI0bNzB9+nRx79HDn5dcLte6FN7V1RVpaWk4cOCAeK5VQ9566y34+fkBAOLj4+Hi4oKkpKQmlwOAdevWYcuWLeIeCVdXV1y5cgUxMTEICgpCp06dAAAdO3Z87ENi169fh6Ojo9bhnRMnTuDbb79FTk6O+J+MPXv2oF+/fkhPT8eQIUOwfv16zJw5U+vzGTBggPjvh4Npjx49sG3bNgwbNgylpaX1ni/SpUsXLF++XHwdFhaGY8eO4ZNPPsHQoUPxf//3f/jqq68waNAgHD9+XLwSqEuXLlCr1SgoKEC3bt0e67MgotbHgCJBDg4OmDBhAuLj4yEIAiZMmAAHBwetNleuXEFFRQXGjRunNX3EiBH45ptvtKZlZmZizpw5iI6OxogRI1BSUoKsrCyYmJiIAaEhtcvu2rULzzzzjDh9wYIFmDdvHiIjI2FiYoKPP/4YW7ZsqbP8qlWrcP78eaSnp9c5l+LhxyTUXvVVWFjY4nNt6vPaa69BoVDgwoULdT7DR3311VfYsGEDrly5gpKSElRVVaGiogJlZWWwsrLCK6+8gpdffhnJycnw9fXF9OnTtWp477338OGHHyI3Nxfl5eWorKxs1ompw4cPF/9tb28Pd3d3XL16tcnlfvvtN+Tl5SEkJAQLFiwQp1dVVbXKycPl5eV1wuPVq1ehUqm09oD27dsX7du3x9WrVzFkyBBkZWVpje9RmZmZiIiIQFZWFn7//XfxKp4bN27U2fsKPDictmnTJuzfvx+3bt0Sb+RoZWUFAIiNjYVSqUTHjh21DhvX/v7pch8GImo7hnWm2xNk3rx5iIuLQ3x8fL2HPmq/xIcMGSKeUGptbY3o6Gj88ssvYruCggJMnjwZCxcu1Dq/oDknXtYuGxISgpCQEK15kyZNgkKhQFJSEj799FOo1WpMnz5dq83evXuxdetWJCUl1bvn4uHLzWr/l6vvO4f6+fnh1q1bOH78eKPtcnNzMX78eHh4eODgwYPIyMjArl27APxx58P58+fj559/xuzZs3Hp0iUMHjwYUVFRAIADBw7gtddew7x585CcnIysrCzMnTu3xTcLbM79P2o/qw8++ABZWVniT3Z2Ni5cuNDosu3atavzROqmDis5ODigqKhIa1pD9y96eHpjv2tlZWXw9/eHtbU19u7di/T0dBw8eBAAGvzstmzZgq1bt2LFihU4efIksrKyEBAQILbftWuX1mGoWr///jsAiHuViEjauAdFosaOHSt+4dZ3+/++fftCoVDgX//6F3r16qU1r/aeMRUVFZgyZQo8PT3rXMLt6emJmpoapKam1nv1VO2yffr0QWRkZJ35pqamCAoKwu7du6FQKDBz5kytu/qeP38e8+fPR0xMDIYNG6b7B6AnkydPxqRJkzBr1iyYmJg0eHnsxYsXUVVVhS1btoiHMOo7f0SlUmHhwoVYuHAhVq5ciQ8++ABhYWE4c+YMvL29tc6d+Omnn5o1xgsXLoh7jYqKivDDDz+gT58+TS7n6OiILl264Oeff8aLL75YbxszMzMAqPMgzk6dOonnKtXOz87OxujRoxt8v4EDB6KgoEDrHkZ9+/bFjRs3kJeXJ+5FuXLlCoqLi8WQ0L9/f3z55ZeYO3dunT6/++473L59G5s2bRKXrz1vpiFnzpzBlClT8Je//AXAg6B27dq1ekPJw7Kzs+Hi4tLknjQikgYGFIkyMTERd/PXd3WTjY0Nli9fjrVr12Lt2rVwc3ODWq3GtWvXYGlpCZVKhdDQUOTn52PPnj0oLCwE8ODL3NTUFN27d0dQUBDmzZuHHTt2YMCAAcjNzUVhYSECAwMRGhqKvLw8fPnll1pXVNjb24t/9ObPny/+UTh37pzYpqCgAM899xxmzpyJgIAA8REGJiYmbfK/1+eeew579uzB7NmzYWpqiueff75Om549e6KqqgpRUVGYNGkSzp07V+deNOHh4Rg3bhx69+6NoqIinDx5Uqy/V69e+Oijj3D8+HG4urpiz549SE9PF6+iaczatWvRsWNHODo6YvXq1XBwcGjw5nKPioiIwCuvvAJbW1uMGzcOarUaFy9eRFFREZYuXYrOnTvDwsICx44dg4uLC8zNzWFnZ4dnn30WS5cuxeeff46ePXti69atTV4RNXDgQHTq1Annzp3DxIkTAQC+vr7o378/XnzxRWzbtg1VVVVYtGgRfHx8xBNh33rrLYwZMwY9e/bEzJkzUVVVhS+++AIrVqxA165dYWZmhqioKCxcuBDZ2dlN3jOnV69eOHjwINLS0tChQwdERkaioKCgyYBy5swZrWeAEZG08RCPhNna2jZ6U6l169Zh5syZWLJkCdzc3ODu7o7169eLgSY1NRV5eXlwd3eHk5MTnJyc0KVLF/F/qNHR0Xj++eexaNEi9OnTBwsWLEBZWZm4bH5+Pvr27Ssu6+TkJD5DBADc3Nzg7e0Nd3d3DB06VJz+3Xff4ddff0V8fLzWsrUn1LaF559/HvHx8Zg9e3a9V7I8/fTTiIyMxDvvvAMPDw98/PHH2Lhxo1ab6upqLF68GE899RTGjh0Ld3d38QTahQsXYtq0aZgxYwaGDh2KO3fuNHolysM2bdqEV199FV5eXsjPz8eRI0fEENiU+fPn48MPP0RcXBw8PT3h4+ODuLg4MRiZmppix44diImJgbOzM6ZMmQLgwSHEoKAgzJkzBz4+PnB1dW107wnwIGDOmzcPH3/8sThNJpPh8OHD6NChA0aOHAlfX1/06NED+/fvF9uMGjUKn3zyCY4cOYKnn34azz77rHjFTadOnRAXF4dPPvkEffv2xaZNm5q8YeObb76JQYMGISAgAKNGjYJSqWwy0FVUVCApKanRc2GISFpkwqMHog1ASUkJ7OzsUFxcXO+zeHJycuDq6tqiq0FqampQUlICW1tbg7oZlVqtxrBhw3DmzJlGb3Cmz/oEQUCfPn0QGhra5rcQf5ihrsPmasv6fv31V/Tr1w8ZGRmtdiVMa9S3a9cu/Pvf/0ZycrJe+mup2u8nFxcXnDx5EuPHjzfKW8FrNBocPXqU9T1kUtTZVh6VfsllNQhSFel9HTb29/tRxvft/YTKzs6GRqOBtbW1+Cj31lRYWIjIyEjcunWr3nMLyDg5OjoiNjYWN27caOuh6EQul4snNBORYeA5KEaid+/eUCgU6NKlC95//31MmDChVd/P0dERDg4OeP/997Ue+kjGr/YwkSF56aWX2noIRKQjBhQjYWNjg4yMjP/a+xngkUEiIjIgPMRDREREksOAQkRERJJjtAGFhyCISGpqv5eac6dgoied0QWU2suh+LwNIpKa2u8lU1Oe/kfUFKPbSkxMTNC+fXvxzqmWlpY6/W+lpqYGlZWVqKioMNp7aBhzfYDx18j6DI8gCLh//z4KCwvRvn37eu8OTUTajC6gABAfK18bUnQhCALKy8thYWFhlLthjb0+wPhrZH2Gq3379lAqlf+VexURGTqjDCgymQxOTk7o3Llzk09ofZRGo8Hp06cxcuRIo70DojHXBxh/jazPMMnlcu45IdKBUQaUWiYmJjp/IZiYmKCqqgrm5uZG9eVYy9jrA4y/RtZHRE8C4zjAS0REREaFAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCRHp4ASHR2N/v37w9bWFra2thg+fDi++OILcX5wcDBkMpnWz7Bhw7T6UKvVCAsLg4ODA6ysrDB58mTcvHlTP9UQERGRUdApoLi4uGDTpk24ePEiLl68iGeffRZTpkzB5cuXxTZjx45Ffn6++HP06FGtPsLDw5GUlITExEScPXsWpaWlmDhxIqqrq/VTERERERk8U10aT5o0Sev1+vXrER0djQsXLqBfv34AAIVCAaVSWe/yxcXFiI2NxZ49e+Dr6wsA2Lt3L1QqFU6cOIGAgICW1EBERERGpsXnoFRXVyMxMRFlZWUYPny4OP3UqVPo3LkzevfujQULFqCwsFCcl5GRAY1GA39/f3Gas7MzPDw8kJaW1tKhEBERkZHRaQ8KAFy6dAnDhw9HRUUFrK2tkZSUhL59+wIAxo0bh//5n/9Bt27dkJOTgzfffBPPPvssMjIyoFAoUFBQADMzM3To0EGrT0dHRxQUFDT4nmq1Gmq1WnxdUlICANBoNNBoNLqW0Kja/vTdr1QYe32A8dfI+gyfsdfI+uqSy2paazitQi4TAOh/HerSn0wQBEGXzisrK3Hjxg3cvXsXBw8exIcffojU1FQxpDwsPz8f3bp1Q2JiIqZNm4aEhATMnTtXK2wAgJ+fH3r27In33nuv3veMiIjAmjVr6kxPSEiApaWlLsMnIiKiNnL//n3MmjULxcXFsLW1bbStzntQzMzM0KtXLwDA4MGDkZ6eju3btyMmJqZOWycnJ3Tr1g3Xrl0DACiVSlRWVqKoqEhrL0phYSG8vb0bfM+VK1di6dKl4uuSkhKoVCr4+/s3WaCuNBoNUlJS4OfnB7lcrte+pcDY6wOMv0bWZ/iMvUbWV9eMmPOtPCr9kssEzHK5q/d1WHsEpDl0DiiPEgShzh6RWnfu3EFeXh6cnJwAAF5eXpDL5UhJSUFgYCCAB3tZsrOzsXnz5gbfQ6FQQKFQ1Jkul8tb7Ze/NfuWAmOvDzD+Glmf4TP2GlnfHzSCod127MEhKX2vQ1360imgrFq1CuPGjYNKpcK9e/eQmJiIU6dO4dixYygtLUVERASmT58OJycnXL9+HatWrYKDgwOee+45AICdnR1CQkKwbNkydOzYEfb29li+fDk8PT3Fq3qIiIiIdAoov/76K2bPno38/HzY2dmhf//+OHbsGPz8/FBeXo5Lly7ho48+wt27d+Hk5ITRo0dj//79sLGxEfvYunUrTE1NERgYiPLycowZMwZxcXEwMTHRe3FERERkmHQKKLGxsQ3Os7CwwPHjx5vsw9zcHFFRUYiKitLlrYmIiOgJYmgHxYiIiOgJwIBCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSo1NAiY6ORv/+/WFrawtbW1sMHz4cX3zxhThfEARERETA2dkZFhYWGDVqFC5fvqzVh1qtRlhYGBwcHGBlZYXJkyfj5s2b+qmGiIiIjIJOAcXFxQWbNm3CxYsXcfHiRTz77LOYMmWKGEI2b96MyMhI7Ny5E+np6VAqlfDz88O9e/fEPsLDw5GUlITExEScPXsWpaWlmDhxIqqrq/VbGRERERksnQLKpEmTMH78ePTu3Ru9e/fG+vXrYW1tjQsXLkAQBGzbtg2rV6/GtGnT4OHhgfj4eNy/fx8JCQkAgOLiYsTGxmLLli3w9fXFwIEDsXfvXly6dAknTpxolQKJiIjI8LT4HJTq6mokJiairKwMw4cPR05ODgoKCuDv7y+2USgU8PHxQVpaGgAgIyMDGo1Gq42zszM8PDzENkRERESmui5w6dIlDB8+HBUVFbC2tkZSUhL69u0rBgxHR0et9o6OjsjNzQUAFBQUwMzMDB06dKjTpqCgoMH3VKvVUKvV4uuSkhIAgEajgUaj0bWERtX2p+9+pcLY6wOMv0bWZ/iMvUbWV5dcVtNaw2kVcpkAQP/rUJf+dA4o7u7uyMrKwt27d3Hw4EEEBQUhNTVVnC+TybTaC4JQZ9qjmmqzceNGrFmzps705ORkWFpa6lhB86SkpLRKv1Jh7PUBxl8j6zN8xl4j6/tDkKoVB9KK9L0O79+/3+y2OgcUMzMz9OrVCwAwePBgpKenY/v27Xj99dcBPNhL4uTkJLYvLCwU96oolUpUVlaiqKhIay9KYWEhvL29G3zPlStXYunSpeLrkpISqFQq+Pv7w9bWVtcSGqXRaJCSkgI/Pz/I5XK99i0Fxl4fYPw1sj7DZ+w1sr66ZsScb+VR6ZdcJmCWy129r8PaIyDNoXNAeZQgCFCr1XB1dYVSqURKSgoGDhwIAKisrERqaireeecdAICXlxfkcjlSUlIQGBgIAMjPz0d2djY2b97c4HsoFAooFIo60+Vyeav98rdm31Jg7PUBxl8j6zN8xl4j6/uDRjC02449OCSl73WoS186BZRVq1Zh3LhxUKlUuHfvHhITE3Hq1CkcO3YMMpkM4eHh2LBhA9zc3ODm5oYNGzbA0tISs2bNAgDY2dkhJCQEy5YtQ8eOHWFvb4/ly5fD09MTvr6+ulVJRERERkungPLrr79i9uzZyM/Ph52dHfr3749jx47Bz88PALBixQqUl5dj0aJFKCoqwtChQ5GcnAwbGxuxj61bt8LU1BSBgYEoLy/HmDFjEBcXBxMTE/1WRkRERAZLp4ASGxvb6HyZTIaIiAhEREQ02Mbc3BxRUVGIiorS5a2JiIjoCWJoB8WIiIjoCcCAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJKjU0DZuHEjhgwZAhsbG3Tu3BlTp07F999/r9UmODgYMplM62fYsGFabdRqNcLCwuDg4AArKytMnjwZN2/efPxqiIiIyCjoFFBSU1OxePFiXLhwASkpKaiqqoK/vz/Kysq02o0dOxb5+fniz9GjR7Xmh4eHIykpCYmJiTh79ixKS0sxceJEVFdXP35FREREZPBMdWl87Ngxrde7d+9G586dkZGRgZEjR4rTFQoFlEplvX0UFxcjNjYWe/bsga+vLwBg7969UKlUOHHiBAICAnStgYiIiIyMTgHlUcXFxQAAe3t7remnTp1C586d0b59e/j4+GD9+vXo3LkzACAjIwMajQb+/v5ie2dnZ3h4eCAtLa3egKJWq6FWq8XXJSUlAACNRgONRvM4JdRR25+++5UKY68PMP4aWZ/hM/YaWV9dcllNaw2nVchlAgD9r0Nd+pMJgiC05E0EQcCUKVNQVFSEM2fOiNP3798Pa2trdOvWDTk5OXjzzTdRVVWFjIwMKBQKJCQkYO7cuVqBAwD8/f3h6uqKmJiYOu8VERGBNWvW1JmekJAAS0vLlgyfiIiI/svu37+PWbNmobi4GLa2to22bfEelCVLluDbb7/F2bNntabPmDFD/LeHhwcGDx6Mbt264fPPP8e0adMa7E8QBMhksnrnrVy5EkuXLhVfl5SUQKVSwd/fv8kCdaXRaJCSkgI/Pz/I5XK99i0Fxl4fYPw1sj7DZ+w1sr66ZsScb+VR6ZdcJmCWy129r8PaIyDN0aKAEhYWhiNHjuD06dNwcXFptK2TkxO6deuGa9euAQCUSiUqKytRVFSEDh06iO0KCwvh7e1dbx8KhQIKhaLOdLlc3mq//K3ZtxQYe32A8dfI+gyfsdfI+v6gEQztrh4PDknpex3q0pdOn5ggCFiyZAkOHTqEkydPwtXVtcll7ty5g7y8PDg5OQEAvLy8IJfLkZKSIrbJz89HdnZ2gwGFiIiIniw67UFZvHgxEhIS8O9//xs2NjYoKCgAANjZ2cHCwgKlpaWIiIjA9OnT4eTkhOvXr2PVqlVwcHDAc889J7YNCQnBsmXL0LFjR9jb22P58uXw9PQUr+ohIiKiJ5tOASU6OhoAMGrUKK3pu3fvRnBwMExMTHDp0iV89NFHuHv3LpycnDB69Gjs378fNjY2YvutW7fC1NQUgYGBKC8vx5gxYxAXFwcTE5PHr4iIiIgMnk4BpakLfiwsLHD8+PEm+zE3N0dUVBSioqJ0eXsiIiJ6QhjaWTtERET0BGBAISIiIslhQCEiIiLJYUAhIiIiyWFAISIiIslhQCEiIiLJYUAhIiIiyWFAISIiIslhQCEiIiLJYUAhIiIiyWFAISIiIslhQCEiIiLJYUAhIiIiyWFAISIiIslhQCEiIiLJYUAhIiIiyWFAISIiIslhQCEiIiLJYUAhIiIiyWFAISIiIslhQCEiIiLJYUAhIiIiyWFAISIiIslhQCEiIiLJYUAhIiIiyWFAISIiIslhQCEiIiLJYUAhIiIiyWFAISIiIslhQCEiIiLJYUAhIiIiydEpoGzcuBFDhgyBjY0NOnfujKlTp+L777/XaiMIAiIiIuDs7AwLCwuMGjUKly9f1mqjVqsRFhYGBwcHWFlZYfLkybh58+bjV0NERERGQaeAkpqaisWLF+PChQtISUlBVVUV/P39UVZWJrbZvHkzIiMjsXPnTqSnp0OpVMLPzw/37t0T24SHhyMpKQmJiYk4e/YsSktLMXHiRFRXV+uvMiIiIjJYpro0PnbsmNbr3bt3o3PnzsjIyMDIkSMhCAK2bduG1atXY9q0aQCA+Ph4ODo6IiEhAaGhoSguLkZsbCz27NkDX19fAMDevXuhUqlw4sQJBAQE6Kk0IiIiMlQ6BZRHFRcXAwDs7e0BADk5OSgoKIC/v7/YRqFQwMfHB2lpaQgNDUVGRgY0Go1WG2dnZ3h4eCAtLa3egKJWq6FWq8XXJSUlAACNRgONRvM4JdRR25+++5UKY68PMP4aWZ/hM/YaWV9dcllNaw2nVchlAgD9r0Nd+mtxQBEEAUuXLsUzzzwDDw8PAEBBQQEAwNHRUauto6MjcnNzxTZmZmbo0KFDnTa1yz9q48aNWLNmTZ3pycnJsLS0bGkJjUpJSWmVfqXC2OsDjL9G1mf4jL1G1veHIFUrDqQV6Xsd3r9/v9ltWxxQlixZgm+//RZnz56tM08mk2m9FgShzrRHNdZm5cqVWLp0qfi6pKQEKpUK/v7+sLW1bcHoG6bRaJCSkgI/Pz/I5XK99i0Fxl4fYPw1sj7DZ+w1sr66ZsScb+VR6ZdcJmCWy129r8PaIyDN0aKAEhYWhiNHjuD06dNwcXERpyuVSgAP9pI4OTmJ0wsLC8W9KkqlEpWVlSgqKtLai1JYWAhvb+9630+hUEChUNSZLpfLW+2XvzX7lgJjrw8w/hpZn+Ez9hpZ3x80gqHd1ePBISl9r0Nd+tLpExMEAUuWLMGhQ4dw8uRJuLq6as13dXWFUqnU2iVUWVmJ1NRUMXx4eXlBLpdrtcnPz0d2dnaDAYWIiIieLDrtQVm8eDESEhLw73//GzY2NuI5I3Z2drCwsIBMJkN4eDg2bNgANzc3uLm5YcOGDbC0tMSsWbPEtiEhIVi2bBk6duwIe3t7LF++HJ6enuJVPURERPRk0ymgREdHAwBGjRqlNX337t0IDg4GAKxYsQLl5eVYtGgRioqKMHToUCQnJ8PGxkZsv3XrVpiamiIwMBDl5eUYM2YM4uLiYGJi8njVEBERkVHQKaAIgtBkG5lMhoiICERERDTYxtzcHFFRUYiKitLl7YmIiOgJYWhn7RAREdETgAGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkR+eAcvr0aUyaNAnOzs6QyWQ4fPiw1vzg4GDIZDKtn2HDhmm1UavVCAsLg4ODA6ysrDB58mTcvHnzsQohIiIi46FzQCkrK8OAAQOwc+fOBtuMHTsW+fn54s/Ro0e15oeHhyMpKQmJiYk4e/YsSktLMXHiRFRXV+teARERERkdU10XGDduHMaNG9doG4VCAaVSWe+84uJixMbGYs+ePfD19QUA7N27FyqVCidOnEBAQICuQyIiIiIjo3NAaY5Tp06hc+fOaN++PXx8fLB+/Xp07twZAJCRkQGNRgN/f3+xvbOzMzw8PJCWllZvQFGr1VCr1eLrkpISAIBGo4FGo9Hr2Gv703e/UmHs9QHGXyPrM3zGXiPrq0suq2mt4bQKuUwAoP91qEt/MkEQhJa+kUwmQ1JSEqZOnSpO279/P6ytrdGtWzfk5OTgzTffRFVVFTIyMqBQKJCQkIC5c+dqBQ4A8Pf3h6urK2JiYuq8T0REBNasWVNnekJCAiwtLVs6fCIiIvovun//PmbNmoXi4mLY2to22lbve1BmzJgh/tvDwwODBw9Gt27d8Pnnn2PatGkNLicIAmQyWb3zVq5ciaVLl4qvS0pKoFKp4O/v32SButJoNEhJSYGfnx/kcrle+5YCY68PMP4aWZ/hM/YaWV9dM2LOt/Ko9EsuEzDL5a7e12HtEZDmaJVDPA9zcnJCt27dcO3aNQCAUqlEZWUlioqK0KFDB7FdYWEhvL296+1DoVBAoVDUmS6Xy1vtl781+5YCY68PMP4aWZ/hM/YaWd8fNIKh3dXjwSEpfa9DXfpq9YBy584d5OXlwcnJCQDg5eUFuVyOlJQUBAYGAgDy8/ORnZ2NzZs3t/ZwiMhAzIg5b1Bf6p+GPdPWQyAyKjoHlNLSUvz444/i65ycHGRlZcHe3h729vaIiIjA9OnT4eTkhOvXr2PVqlVwcHDAc889BwCws7NDSEgIli1bho4dO8Le3h7Lly+Hp6eneFUPERERPdl0DigXL17E6NGjxde154YEBQUhOjoaly5dwkcffYS7d+/CyckJo0ePxv79+2FjYyMus3XrVpiamiIwMBDl5eUYM2YM4uLiYGJiooeSiIiIyNDpHFBGjRqFxi78OX78eJN9mJubIyoqClFRUbq+PRERET0BDOcALxERET0xGFCIiIhIchhQiIiISHIYUIiIiEhyGFCIiIhIchhQiIiISHIYUIiIiEhyGFCIiIhIchhQiIiISHIYUIiIiEhyGFCIiIhIchhQiIiISHIYUIiIiEhyGFCIiIhIchhQiIiISHIYUIiIiEhyGFCIiIhIchhQiIiISHIYUIiIiEhyGFCIiIhIckzbegBSNSPmPDSC4eS3T8OeaeshEBER6Y3h/AUmIiKiJwYDChEREUkOAwoRERFJDgMKERERSQ4DChEREUkOAwoRERFJDgMKERERSQ4DChEREUkOAwoRERFJDgMKERERSY7OAeX06dOYNGkSnJ2dIZPJcPjwYa35giAgIiICzs7OsLCwwKhRo3D58mWtNmq1GmFhYXBwcICVlRUmT56MmzdvPlYhREREZDx0DihlZWUYMGAAdu7cWe/8zZs3IzIyEjt37kR6ejqUSiX8/Pxw7949sU14eDiSkpKQmJiIs2fPorS0FBMnTkR1dXXLKyEiIiKjofPDAseNG4dx48bVO08QBGzbtg2rV6/GtGnTAADx8fFwdHREQkICQkNDUVxcjNjYWOzZswe+vr4AgL1790KlUuHEiRMICAh4jHKIiIjIGOj1acY5OTkoKCiAv7+/OE2hUMDHxwdpaWkIDQ1FRkYGNBqNVhtnZ2d4eHggLS2t3oCiVquhVqvF1yUlJQAAjUYDjUajzxLE/uQyAUCNXvtuTc39HGrb6ftzkxJjr/FJqc9Yt8GH2xr7OmR9f5DLDOd3Gajd/vS/DnXpT68BpaCgAADg6OioNd3R0RG5ubliGzMzM3To0KFOm9rlH7Vx40asWbOmzvTk5GRYWlrqY+h1zHK52yr9tpajR4/q1D4lJaWVRiIdxl6jsddn7NsgYPzrkPX9IUjVigNpRfpeh/fv3292W70GlFoymUzrtSAIdaY9qrE2K1euxNKlS8XXJSUlUKlU8Pf3h62t7eMP+CEajQYpKSlIuNkeGqHxMUvJ/tDhzWpXW5+fnx/kcnkrj6ptGHuNT0p9xroNAk/OOmR9f5gRc76VR6VfcpmAWS539b4Oa4+ANIdeA4pSqQTwYC+Jk5OTOL2wsFDcq6JUKlFZWYmioiKtvSiFhYXw9vaut1+FQgGFQlFnulwub7Vffo0gg0YwnKuwdf0cWvOzkwpjr9HY6zP2bbB2GWNeh6zvD4b0u/zAg0NS+l6HuvSl10/M1dUVSqVSa5dQZWUlUlNTxfDh5eUFuVyu1SY/Px/Z2dkNBhQiIiJ6sui8B6W0tBQ//vij+DonJwdZWVmwt7dH165dER4ejg0bNsDNzQ1ubm7YsGEDLC0tMWvWLACAnZ0dQkJCsGzZMnTs2BH29vZYvnw5PD09xat6iIiI6Mmmc0C5ePEiRo8eLb6uPTckKCgIcXFxWLFiBcrLy7Fo0SIUFRVh6NChSE5Oho2NjbjM1q1bYWpqisDAQJSXl2PMmDGIi4uDiYmJHkoiIiIiQ6dzQBk1ahQEQWhwvkwmQ0REBCIiIhpsY25ujqioKERFRen69kRERPQEMLSzdoiIiOgJwIBCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREkmPa1gMgIiIydpF3X23rIeikup0Zvle90aZj4B4UIiIikhwGFCIiIpIcBhQiIiKSHAYUIiIikhwGFCIiIpIcBhQiIiKSHAYUIiIikhwGFCIiIpIcBhQiIiKSHAYUIiIikhwGFCIiIpIcPounARuKX4dJTWVbD0MHGW09ACIiIr3hHhQiIiKSHAYUIiIikhwGFCIiIpIcBhQiIiKSHL0HlIiICMhkMq0fpVIpzhcEAREREXB2doaFhQVGjRqFy5cv63sYREREZMBaZQ9Kv379kJ+fL/5cunRJnLd582ZERkZi586dSE9Ph1KphJ+fH+7du9caQyEiIiID1CoBxdTUFEqlUvzp1KkTgAd7T7Zt24bVq1dj2rRp8PDwQHx8PO7fv4+EhITWGAoREREZoFa5D8q1a9fg7OwMhUKBoUOHYsOGDejRowdycnJQUFAAf39/sa1CoYCPjw/S0tIQGhpab39qtRpqtVp8XVJSAgDQaDTQaDR6HXttf9XtzPTab2tr7udQ207fn5uUGHuNT0p9cpkAoKZtB6MDXdbHk7IOWd8fDO1vSu14W+tvbHPIBEEQ9PnmX3zxBe7fv4/evXvj119/xdtvv43vvvsOly9fxvfff48RI0bg1q1bcHZ2Fpd56aWXkJubi+PHj9fbZ0REBNasWVNnekJCAiwtLfU5fCIiImol9+/fx6xZs1BcXAxbW9tG2+o9oDyqrKwMPXv2xIoVKzBs2DCMGDECv/zyC5ycnMQ2CxYsQF5eHo4dO1ZvH/XtQVGpVLh9+3aTBepKo9EgJSUFva5EGtSdZHuuON2sdrX1+fn5QS6Xt/Ko2oax1/ik1Jdwsz00gqyth9Ns+0OHN7vtk7IOWd8ffto8spVHpV/V7czwY9+lel+HJSUlcHBwaFZAafVb3VtZWcHT0xPXrl3D1KlTAQAFBQVaAaWwsBCOjo4N9qFQKKBQKOpMl8vlrfbLb1JTaVABRdfPoTU/O6kw9hqNvT6NIINGMJw7IbRkXRj7OmR9fzCkvycP0/c61KWvVt/61Wo1rl69CicnJ7i6ukKpVCIlJUWcX1lZidTUVHh7e7f2UIiIiMhA6H0PyvLlyzFp0iR07doVhYWFePvtt1FSUoKgoCDIZDKEh4djw4YNcHNzg5ubGzZs2ABLS0vMmjVL30MhIiIiA6X3gHLz5k288MILuH37Njp16oRhw4bhwoUL6NatGwBgxYoVKC8vx6JFi1BUVIShQ4ciOTkZNjY2+h4KERERGSi9B5TExMRG58tkMkRERCAiIkLfb01ERERGwnDOQCMiIqInBgMKERERSQ4DChEREUkOAwoRERFJDgMKERERSQ4DChEREUkOAwoRERFJDgMKERERSQ4DChEREUkOAwoRERFJDgMKERERSQ4DChEREUkOAwoRERFJDgMKERERSY5pWw+AiKg+G4pfh0lNZVsPQwcZbT0AIqPCPShEREQkOQwoREREJDkMKERERCQ5DChEREQkOQwoREREJDkMKERERCQ5DChEREQkOQwoREREJDkMKERERCQ5DChEREQkOQwoREREJDkMKERERCQ5DChEREQkOQwoREREJDkMKERERCQ5DChEREQkOW0aUP73f/8Xrq6uMDc3h5eXF86cOdOWwyEiIiKJaLOAsn//foSHh2P16tXIzMzEn//8Z4wbNw43btxoqyERERGRRLRZQImMjERISAjmz5+Pp556Ctu2bYNKpUJ0dHRbDYmIiIgkwrQt3rSyshIZGRl44403tKb7+/sjLS2tTnu1Wg21Wi2+Li4uBgD8/vvv0Gg0eh2bRqPB/fv3UVzZDiY1hnOKzp07d5rVrra+O3fuQC6Xt/Ko2oax1/ik1Ges2yDw5KxD1veH4krD+V0GgOp27VplHd67dw8AIAhCk23bJKDcvn0b1dXVcHR01Jru6OiIgoKCOu03btyINWvW1Jnu6uraamM0OG87tPUIiJ5s3AbJ6NTdYaAv9+7dg52dXaNt2iSg1JLJZFqvBUGoMw0AVq5ciaVLl4qva2pq8Pvvv6Njx471tn8cJSUlUKlUyMvLg62trV77lgJjrw8w/hpZn+Ez9hpZn+FrrRoFQcC9e/fg7OzcZNs2CSgODg4wMTGps7eksLCwzl4VAFAoFFAoFFrT2rdv35pDhK2trdH+4gHGXx9g/DWyPsNn7DWyPsPXGjU2teekVpscFDMzM4OXlxdSUlK0pqekpMDb27sthkREREQS0maHeJYuXYrZs2dj8ODBGD58ON5//33cuHEDCxcubKshERERkUS0WUCZMWMG7ty5g7Vr1yI/Px8eHh44evQounXr1lZDAvDgcNJbb71V55CSsTD2+gDjr5H1GT5jr5H1GT4p1CgTmnOtDxEREdF/kWFdmE1ERERPBAYUIiIikhwGFCIiIpIcBhQiIiKSHKMOKKdPn8akSZPg7OwMmUyGw4cPN7lMamoqvLy8YG5ujh49euC9996r0+bgwYPo27cvFAoF+vbti6SkpFYYfdN0re/QoUPw8/NDp06dYGtri+HDh+P48eNabeLi4iCTyer8VFRUtGIlDdO1xlOnTtU7/u+++06rnaGuw+Dg4Hrr69evn9hGSutw48aNGDJkCGxsbNC5c2dMnToV33//fZPLGcp22JL6DG07bEmNhrQdtqQ+Q9oOo6Oj0b9/f/GGa8OHD8cXX3zR6DJS2f6MOqCUlZVhwIAB2LlzZ7Pa5+TkYPz48fjzn/+MzMxMrFq1Cq+88goOHjwotjl//jxmzJiB2bNn45tvvsHs2bMRGBiI//znP61VRoN0re/06dPw8/PD0aNHkZGRgdGjR2PSpEnIzMzUamdra4v8/HytH3Nz89YooUm61ljr+++/1xq/m5ubOM+Q1+H27du16srLy4O9vT3+53/+R6udVNZhamoqFi9ejAsXLiAlJQVVVVXw9/dHWVlZg8sY0nbYkvoMbTtsSY21DGE7bEl9hrQduri4YNOmTbh48SIuXryIZ599FlOmTMHly5frbS+p7U94QgAQkpKSGm2zYsUKoU+fPlrTQkNDhWHDhomvAwMDhbFjx2q1CQgIEGbOnKm3sbZEc+qrT9++fYU1a9aIr3fv3i3Y2dnpb2B61Jwav/rqKwGAUFRU1GAbY1qHSUlJgkwmE65fvy5Ok/I6LCwsFAAIqampDbYx5O2wOfXVx5C2w+bUaMjbYUvWoaFthx06dBA+/PDDeudJafsz6j0oujp//jz8/f21pgUEBODixYvQaDSNtklLa72nPraWmpoa3Lt3D/b29lrTS0tL0a1bN7i4uGDixIl1/mdnCAYOHAgnJyeMGTMGX331ldY8Y1qHsbGx8PX1rXODQ6muw+LiYgCo8zv3MEPeDptT36MMbTvUpUZD3A5bsg4NZTusrq5GYmIiysrKMHz48HrbSGn7Y0B5SEFBQZ2HFTo6OqKqqgq3b99utM2jDz40BFu2bEFZWRkCAwPFaX369EFcXByOHDmCffv2wdzcHCNGjMC1a9facKTN5+TkhPfffx8HDx7EoUOH4O7ujjFjxuD06dNiG2NZh/n5+fjiiy8wf/58relSXYeCIGDp0qV45pln4OHh0WA7Q90Om1vfowxpO2xujYa6HbZkHRrCdnjp0iVYW1tDoVBg4cKFSEpKQt++fettK6Xtr81udS9VMplM67Xw/2+0+/D0+to8Ok3q9u3bh4iICPz73/9G586dxenDhg3DsGHDxNcjRozAoEGDEBUVhR07drTFUHXi7u4Od3d38fXw4cORl5eHd999FyNHjhSnG8M6jIuLQ/v27TF16lSt6VJdh0uWLMG3336Ls2fPNtnWELdDXeqrZWjbYXNrNNTtsCXr0BC2Q3d3d2RlZeHu3bs4ePAggoKCkJqa2mBIkcr2xz0oD1EqlXUSYGFhIUxNTdGxY8dG2zyaJqVs//79CAkJwYEDB+Dr69to23bt2mHIkCFt/j+3xzFs2DCt8RvDOhQEAf/85z8xe/ZsmJmZNdpWCuswLCwMR44cwVdffQUXF5dG2xridqhLfbUMbTtsSY0Pk/p22JL6DGU7NDMzQ69evTB48GBs3LgRAwYMwPbt2+ttK6XtjwHlIcOHD0dKSorWtOTkZAwePBhyubzRNt7e3v+1cT6Offv2ITg4GAkJCZgwYUKT7QVBQFZWFpycnP4Lo2sdmZmZWuM39HUIPLjy4Mcff0RISEiTbdtyHQqCgCVLluDQoUM4efIkXF1dm1zGkLbDltQHGNZ22NIaHyXV7fBx6jOU7bC+sajV6nrnSWr70+sptxJz7949ITMzU8jMzBQACJGRkUJmZqaQm5srCIIgvPHGG8Ls2bPF9j///LNgaWkpvPbaa8KVK1eE2NhYQS6XC//617/ENufOnRNMTEyETZs2CVevXhU2bdokmJqaChcuXJB8fQkJCYKpqamwa9cuIT8/X/y5e/eu2CYiIkI4duyY8NNPPwmZmZnC3LlzBVNTU+E///nPf70+QdC9xq1btwpJSUnCDz/8IGRnZwtvvPGGAEA4ePCg2MaQ12Gtv/zlL8LQoUPr7VNK6/Dll18W7OzshFOnTmn9zt2/f19sY8jbYUvqM7TtsCU1GtJ22JL6ahnCdrhy5Urh9OnTQk5OjvDtt98Kq1atEtq1ayckJycLgiDt7c+oA0rtpW6P/gQFBQmCIAhBQUGCj4+P1jKnTp0SBg4cKJiZmQndu3cXoqOj6/T7ySefCO7u7oJcLhf69OmjtdH9N+lan4+PT6PtBUEQwsPDha5duwpmZmZCp06dBH9/fyEtLe2/W9hDdK3xnXfeEXr27CmYm5sLHTp0EJ555hnh888/r9Ovoa5DQRCEu3fvChYWFsL7779fb59SWof11QZA2L17t9jGkLfDltRnaNthS2o0pO2wpb+jhrIdzps3T+jWrZs4jjFjxojhRBCkvf3JBOH/n/1CREREJBE8B4WIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCTn/wFqrgR/37IweQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 \n",
      "       Liczebność\n",
      "ocalal           \n",
      "Nie           682\n",
      "Tak           161\n",
      "                                  \n",
      "                        Liczebność\n",
      "Czy ocalał Klasa biletu           \n",
      "Nie        1.0                 118\n",
      "           2.0                 146\n",
      "           3.0                 418\n",
      "Tak        1.0                  61\n",
      "           2.0                  25\n",
      "           3.0                  75\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# No i chciałbym przeprowadzić analizę samych mężczyzn (ich przeżycie, albo zatonięcie\n",
    "# w zależności od posiadanej klasy biletu. Jak wiadomo - ich ocalało jedynie 161 osób, \n",
    "# a powyższy wykres histogramu przeżywalności w zależności od klasy biletu mógł nie \n",
    "# odzwierciedlać dobrze rzeczywistości, bo najwyraźniej za główny cel postawiono sobie\n",
    "# ratowanie kobiet...\n",
    "\n",
    "df4 = df3[df3['plec'] == 'M'][['ocalal', 'klasa_biletu', 'plec']]\n",
    "for survived in [0, 1]:\n",
    "    add_to_title = ' (ocalał)' if survived else ' (nie ocalał)'\n",
    "    df5 = df4[df4['ocalal'] == survived]['klasa_biletu'].copy()\n",
    "    df5.name = 'Mężczyźni i klasa biletu' + add_to_title\n",
    "    df5.hist(legend=True, alpha=0.8)\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "stat = df4.copy()\n",
    "stat['ocalal'].replace({0: 'Nie', 1: 'Tak'}, inplace=True)\n",
    "\n",
    "stat1 = stat.groupby(['ocalal']).agg({'plec': ['count']})\n",
    "stat1 = stat1.rename(columns={'count': 'Liczebność'}, level=1)\n",
    "stat1 = stat1.rename(columns={'plec': ''}, level=0)\n",
    "print(stat1)\n",
    "\n",
    "stat2 = stat.groupby(['ocalal', 'klasa_biletu']).agg({'plec': ['count']})\n",
    "stat2.axes[0].names = ['Czy ocalał', 'Klasa biletu']\n",
    "stat2 = stat2.rename(columns={'count': 'Liczebność'}, level=1)\n",
    "stat2 = stat2.rename(columns={'plec': ''}, level=0)\n",
    "print(stat2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c701aa3-2a02-4751-a815-c38a761abbd7",
   "metadata": {},
   "source": [
    "Z tych danych wynika ogromna dysproporcja pomiędzy uratowanymi z klasą 1 pomiędzy uratowanymi z klasą 3 w skali wszystkich mężczyzn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "dada5c91-9378-4077-962d-46ab82833dfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           liczba ocalałych\n",
      "plec                       \n",
      "Kobiety                  21\n",
      "Mężczyźni                 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Teraz przeanalizujemy tych, którzy przeżyli pomimo braku łódki ratunkowej\n",
    "df5 = df3[(df3['ocalal'] == 1) & (df3['mial_lodke'] == 0)]\n",
    "\n",
    "by_sex = df5.groupby('plec').agg({'ocalal': ['count']})\n",
    "by_sex.rename(index={'M': 'Mężczyźni', 'K': 'Kobiety'}, inplace=True)\n",
    "by_sex.columns = ['liczba ocalałych pomimo braku łódki']\n",
    "print(by_sex)\n",
    "by_sex.plot(kind='pie', subplots=True)\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92214b90-3e2d-4368-95e2-b21e7eeaada4",
   "metadata": {},
   "source": [
    "Widzimy tutaj zdumiewającą rzecz. Jaka miażdżąca statystyka na rzecz kobiet! Ciekawe z czego to wynika? \n",
    "Czy z tego, że kobietom bardziej pomagano przeżyć (np. użyczając im jakieś przedmioty, których mogły się złapać, \n",
    "a które wypłynęły na wierzch, czy może z tego, że co do natury mają większą wytrzymałość?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "3d178fdd-9932-4ad3-8078-b3cd09c3644e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'wiek'}>]], dtype=object)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a teraz sprawdzimy, czy wiek miał tutaj znaczenie w powyższym zjawisku\n",
    "\n",
    "df5[['wiek']].hist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f76fd1c-1a82-4094-ae40-281ffdd300d2",
   "metadata": {},
   "source": [
    "Okazuje się, że to również może mieć znaczenie. Czyżby młodszy organizm to silniejszy i wytrzymalszy?\n",
    "Ale liczna tych próbek jest jednak zbyt mała, aby wyciągać jakieś wnioski."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}