Analize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c569c23",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "pd.set_option('display.max_column', None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d0bc113",
   "metadata": {},
   "outputs": [],
   "source": [
    "analize_data = pd.read_csv(\"train.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e1d56e8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oconnor, Frankie</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>209245</td>\n",
       "      <td>27.14</td>\n",
       "      <td>C12239</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Bryan, Drew</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27323</td>\n",
       "      <td>13.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Owens, Kenneth</td>\n",
       "      <td>male</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>CA 457703</td>\n",
       "      <td>71.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kramer, James</td>\n",
       "      <td>male</td>\n",
       "      <td>19.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A. 10866</td>\n",
       "      <td>13.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Bond, Michael</td>\n",
       "      <td>male</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>427635</td>\n",
       "      <td>7.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass              Name   Sex    Age  SibSp  Parch  \\\n",
       "0            0         1       1  Oconnor, Frankie  male    NaN      2      0   \n",
       "1            1         0       3       Bryan, Drew  male    NaN      0      0   \n",
       "2            2         0       3    Owens, Kenneth  male   0.33      1      2   \n",
       "3            3         0       3     Kramer, James  male  19.00      0      0   \n",
       "4            4         1       3     Bond, Michael  male  25.00      0      0   \n",
       "\n",
       "      Ticket   Fare   Cabin Embarked  \n",
       "0     209245  27.14  C12239        S  \n",
       "1      27323  13.35     NaN        S  \n",
       "2  CA 457703  71.29     NaN        S  \n",
       "3   A. 10866  13.04     NaN        S  \n",
       "4     427635   7.76     NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94b67ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    male\n",
       "1    male\n",
       "2    male\n",
       "3    male\n",
       "4    male\n",
       "Name: Sex, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[\"Sex\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf4c96e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x00000263EB3836A0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[\"Sex\"].groupby(analize_data[\"Sex\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a226fe18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x00000263EB383A90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[\"Sex\"].groupby(analize_data[\"Sex\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67b61567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[\"Age\"].max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "57a8f913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[analize_data[\"Age\"]=='NaN'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d4f45e4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.35547214294576"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[\"Age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8e91fd4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[analize_data[\"Cabin\"]=='NaN'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b582d829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[analize_data[\"Pclass\"]==0].count()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4a5e031d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    30315\n",
       "Survived       30315\n",
       "Pclass         30315\n",
       "Name           30315\n",
       "Sex            30315\n",
       "Age            29353\n",
       "SibSp          30315\n",
       "Parch          30315\n",
       "Ticket         30288\n",
       "Fare           30275\n",
       "Cabin          26463\n",
       "Embarked       30229\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[analize_data[\"Pclass\"]==1].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a9da0463",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    28679\n",
       "Survived       28679\n",
       "Pclass         28679\n",
       "Name           28679\n",
       "Sex            28679\n",
       "Age            27731\n",
       "SibSp          28679\n",
       "Parch          28679\n",
       "Ticket         27855\n",
       "Fare           28637\n",
       "Cabin           3520\n",
       "Embarked       28609\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[analize_data[\"Pclass\"]==2].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c85a657f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    41006\n",
       "Survived       41006\n",
       "Pclass         41006\n",
       "Name           41006\n",
       "Sex            41006\n",
       "Age            39624\n",
       "SibSp          41006\n",
       "Parch          41006\n",
       "Ticket         37234\n",
       "Fare           40954\n",
       "Cabin           2151\n",
       "Embarked       40912\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[analize_data[\"Pclass\"]==3].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1e8e834d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[analize_data[\"Pclass\"]==4].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a29f5b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Bryan, Drew</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27323</td>\n",
       "      <td>13.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Owens, Kenneth</td>\n",
       "      <td>male</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>CA 457703</td>\n",
       "      <td>71.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kramer, James</td>\n",
       "      <td>male</td>\n",
       "      <td>19.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A. 10866</td>\n",
       "      <td>13.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Bond, Michael</td>\n",
       "      <td>male</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>427635</td>\n",
       "      <td>7.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Conway, Jose</td>\n",
       "      <td>male</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5</td>\n",
       "      <td>9.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Werner, Linda</td>\n",
       "      <td>female</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>434426</td>\n",
       "      <td>31.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Nichols, William</td>\n",
       "      <td>male</td>\n",
       "      <td>31.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>459690</td>\n",
       "      <td>8.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Stewart, Pat</td>\n",
       "      <td>male</td>\n",
       "      <td>36.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>434108</td>\n",
       "      <td>9.34</td>\n",
       "      <td>C8674</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Estrada, Nancy</td>\n",
       "      <td>female</td>\n",
       "      <td>57.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/S 53862</td>\n",
       "      <td>9.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Feller, Jimmie</td>\n",
       "      <td>male</td>\n",
       "      <td>53.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Lim, Tiana</td>\n",
       "      <td>female</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A. 38515</td>\n",
       "      <td>8.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Brown, Pat</td>\n",
       "      <td>female</td>\n",
       "      <td>23.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>428754</td>\n",
       "      <td>7.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Fetterolf, Andy</td>\n",
       "      <td>male</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>433460</td>\n",
       "      <td>10.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Liang, Amanda</td>\n",
       "      <td>female</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A. 12030</td>\n",
       "      <td>10.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Robinson, James</td>\n",
       "      <td>male</td>\n",
       "      <td>36.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>419996</td>\n",
       "      <td>8.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Escareno, William</td>\n",
       "      <td>male</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14288</td>\n",
       "      <td>31.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hollander, Richard</td>\n",
       "      <td>male</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hall, Booker</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>422605</td>\n",
       "      <td>5.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Dixon, Ray</td>\n",
       "      <td>male</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12720</td>\n",
       "      <td>6.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hickey, Gregory</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11059</td>\n",
       "      <td>27.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gallagher, Theresa</td>\n",
       "      <td>female</td>\n",
       "      <td>68.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>SC/PARIS 11177</td>\n",
       "      <td>31.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Bennett, Christopher</td>\n",
       "      <td>male</td>\n",
       "      <td>71.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>318079</td>\n",
       "      <td>25.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sohn, Shane</td>\n",
       "      <td>male</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A. 440907</td>\n",
       "      <td>9.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Persad, Elbert</td>\n",
       "      <td>male</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>424265</td>\n",
       "      <td>80.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsson, Aaron</td>\n",
       "      <td>male</td>\n",
       "      <td>37.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14214</td>\n",
       "      <td>10.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass                  Name     Sex    Age  SibSp  \\\n",
       "1             1         0       3           Bryan, Drew    male    NaN      0   \n",
       "2             2         0       3        Owens, Kenneth    male   0.33      1   \n",
       "3             3         0       3         Kramer, James    male  19.00      0   \n",
       "4             4         1       3         Bond, Michael    male  25.00      0   \n",
       "6             6         0       3          Conway, Jose    male   7.00      0   \n",
       "7             7         1       3         Werner, Linda  female   6.00      1   \n",
       "10           10         0       3      Nichols, William    male  31.00      0   \n",
       "12           12         0       3          Stewart, Pat    male  36.00      0   \n",
       "15           15         1       3        Estrada, Nancy  female  57.00      0   \n",
       "16           16         0       3        Feller, Jimmie    male  53.00      0   \n",
       "20           20         0       3            Lim, Tiana  female  17.00      0   \n",
       "23           23         1       3            Brown, Pat  female  23.00      0   \n",
       "24           24         0       3       Fetterolf, Andy    male  21.00      0   \n",
       "26           26         0       3         Liang, Amanda  female  26.00      0   \n",
       "29           29         0       3       Robinson, James    male  36.00      0   \n",
       "30           30         1       3     Escareno, William    male   5.00      1   \n",
       "31           31         1       3    Hollander, Richard    male   7.00      1   \n",
       "35           35         0       3          Hall, Booker    male    NaN      0   \n",
       "41           41         1       3            Dixon, Ray    male  22.00      0   \n",
       "44           44         0       3       Hickey, Gregory    male    NaN      1   \n",
       "45           45         0       3    Gallagher, Theresa  female  68.00      0   \n",
       "46           46         0       3  Bennett, Christopher    male  71.00      0   \n",
       "49           49         0       3           Sohn, Shane    male  22.00      0   \n",
       "55           55         0       3        Persad, Elbert    male   3.00      1   \n",
       "58           58         0       3         Olsson, Aaron    male  37.00      0   \n",
       "\n",
       "    Parch          Ticket   Fare  Cabin Embarked  \n",
       "1       0           27323  13.35    NaN        S  \n",
       "2       2       CA 457703  71.29    NaN        S  \n",
       "3       0        A. 10866  13.04    NaN        S  \n",
       "4       0          427635   7.76    NaN        S  \n",
       "6       0            A/5    9.77    NaN        S  \n",
       "7       2          434426  31.50    NaN        S  \n",
       "10      0          459690   8.06    NaN        S  \n",
       "12      0          434108   9.34  C8674        S  \n",
       "15      0       A/S 53862   9.31    NaN        S  \n",
       "16      0             NaN   7.92    NaN        S  \n",
       "20      0        A. 38515   8.28    NaN        S  \n",
       "23      0          428754   7.95    NaN        Q  \n",
       "24      0          433460  10.38    NaN        S  \n",
       "26      0        A. 12030  10.24    NaN        S  \n",
       "29      0          419996   8.60    NaN        S  \n",
       "30      1           14288  31.35    NaN        S  \n",
       "31      0             NaN  28.75    NaN        S  \n",
       "35      0          422605   5.77    NaN        C  \n",
       "41      0           12720   6.03    NaN        S  \n",
       "44      0           11059  27.49    NaN        S  \n",
       "45      1  SC/PARIS 11177  31.01    NaN        C  \n",
       "46      0          318079  25.60    NaN        C  \n",
       "49      3       A. 440907   9.29    NaN        Q  \n",
       "55      2          424265  80.84    NaN        S  \n",
       "58      0           14214  10.46    NaN        S  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[analize_data[\"Pclass\"]==3].head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f5161444",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    2151\n",
       "Survived       2151\n",
       "Pclass         2151\n",
       "Name           2151\n",
       "Sex            2151\n",
       "Age            2076\n",
       "SibSp          2151\n",
       "Parch          2151\n",
       "Ticket         2105\n",
       "Fare           2147\n",
       "Cabin          2151\n",
       "Embarked       2144\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[(analize_data[\"Pclass\"]==3)&((analize_data[\"Cabin\"]))].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dd0f3364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    2076\n",
       "Survived       2076\n",
       "Pclass         2076\n",
       "Name           2076\n",
       "Sex            2076\n",
       "Age            2076\n",
       "SibSp          2076\n",
       "Parch          2076\n",
       "Ticket         2032\n",
       "Fare           2072\n",
       "Cabin          2076\n",
       "Embarked       2069\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[(analize_data[\"Pclass\"]==3)&((analize_data[\"Cabin\"]))&((analize_data[\"Age\"]>0))].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f56e87fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Stewart, Pat</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>434108</td>\n",
       "      <td>9.34</td>\n",
       "      <td>C8674</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>94</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Level, Tammara</td>\n",
       "      <td>female</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10156</td>\n",
       "      <td>24.93</td>\n",
       "      <td>C11610</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>165</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Troxel, Barbara</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>440065</td>\n",
       "      <td>30.29</td>\n",
       "      <td>F2767</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mahr, Patsy</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345263</td>\n",
       "      <td>28.06</td>\n",
       "      <td>E7752</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>232</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Plowden, Joni</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26696</td>\n",
       "      <td>29.88</td>\n",
       "      <td>D9019</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass             Name     Sex   Age  SibSp  \\\n",
       "12            12         0       3     Stewart, Pat    male  36.0      0   \n",
       "94            94         1       3   Level, Tammara  female  59.0      0   \n",
       "165          165         1       3  Troxel, Barbara  female  62.0      0   \n",
       "170          170         1       3      Mahr, Patsy  female  29.0      0   \n",
       "232          232         1       3    Plowden, Joni  female  28.0      0   \n",
       "\n",
       "     Parch  Ticket   Fare   Cabin Embarked  \n",
       "12       0  434108   9.34   C8674        S  \n",
       "94       0   10156  24.93  C11610        C  \n",
       "165      0  440065  30.29   F2767        C  \n",
       "170      0  345263  28.06   E7752        C  \n",
       "232      0   26696  29.88   D9019        C  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[(analize_data[\"Pclass\"]==3)&((analize_data[\"Cabin\"]))&((analize_data[\"Age\"]>0))].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9454244c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[(analize_data[\"Pclass\"]==3)&((analize_data[\"Cabin\"]))&((analize_data[\"Age\"]>0))]['Age'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "882b216b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[(analize_data[\"Pclass\"]==3)&((analize_data[\"Cabin\"]))&((analize_data[\"Age\"]>0))]['Age'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "df12101e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.441594412331405"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analize_data[(analize_data[\"Pclass\"]==3)&((analize_data[\"Cabin\"]))&((analize_data[\"Age\"]>0))]['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ea5f730a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analize_data[(analize_data[\"Pclass\"]==3)&((analize_data[\"Cabin\"]))&((analize_data[\"Age\"]>0))]['Age'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3229de0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analize_data[(analize_data[\"Pclass\"]==3)&((analize_data[\"Cabin\"]))&((analize_data[\"Age\"]>0))]['Age'](\"Age\").plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00371938",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "854c3255",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}