titanic-eda.ipynb

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     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns"
   ]
  },
  {
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   "source": [
    "df = pd.read_csv('/kaggle/input/titanic/train.csv')"
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       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
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       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
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       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
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       "PassengerId      int64\n",
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       "dtype: object"
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "62a9c33c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-22T20:30:04.031373Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62d0183b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-22T20:30:04.062653Z",
     "iopub.status.busy": "2023-08-22T20:30:04.062219Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d831b54c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-22T20:30:04.089242Z",
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f4053527",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-22T20:30:04.134154Z",
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64ab6bb8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-22T20:30:04.192928Z",
     "iopub.status.busy": "2023-08-22T20:30:04.192494Z",
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     "status": "completed"
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   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>347082</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name   Sex  Ticket    Cabin Embarked\n",
       "count                       891   891     891      204      889\n",
       "unique                      891     2     681      147        3\n",
       "top     Braund, Mr. Owen Harris  male  347082  B96 B98        S\n",
       "freq                          1   577       7        4      644"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5ee2ab3",
   "metadata": {
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    {
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>C85</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>C123</td>\n",
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       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>886</th>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
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       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
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       "      <th>889</th>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>C148</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
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      ],
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       "                                                  Name Cabin\n",
       "0                              Braund, Mr. Owen Harris   NaN\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...   C85\n",
       "2                               Heikkinen, Miss. Laina   NaN\n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  C123\n",
       "4                             Allen, Mr. William Henry   NaN\n",
       "..                                                 ...   ...\n",
       "886                              Montvila, Rev. Juozas   NaN\n",
       "887                       Graham, Miss. Margaret Edith   B42\n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"   NaN\n",
       "889                              Behr, Mr. Karl Howell  C148\n",
       "890                                Dooley, Mr. Patrick   NaN\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
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       "array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
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       "    PassengerId  Survived  Pclass  \\\n",
       "77           78         0       3   \n",
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       "80           81         0       3   \n",
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       "86           87         0       3   \n",
       "87           88         0       3   \n",
       "88           89         1       1   \n",
       "\n",
       "                                                 Name     Sex    Age  SibSp  \\\n",
       "77                           Moutal, Mr. Rahamin Haim    male    NaN      0   \n",
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       "80                               Waelens, Mr. Achille    male  22.00      0   \n",
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       "\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Embarked  \n",
       "0        0         A/5 21171   7.2500        S  \n",
       "1        0          PC 17599  71.2833        C  \n",
       "2        0  STON/O2. 3101282   7.9250        S  \n",
       "3        0            113803  53.1000        S  \n",
       "4        0            373450   8.0500        S  \n",
       "..     ...               ...      ...      ...  \n",
       "886      0            211536  13.0000        S  \n",
       "887      0            112053  30.0000        S  \n",
       "888      2        W./C. 6607  23.4500        S  \n",
       "889      0            111369  30.0000        C  \n",
       "890      0            370376   7.7500        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('Cabin',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e5334b2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-22T20:30:04.398465Z",
     "iopub.status.busy": "2023-08-22T20:30:04.398012Z",
     "iopub.status.idle": "2023-08-22T20:30:04.744683Z",
     "shell.execute_reply": "2023-08-22T20:30:04.743564Z"
    },
    "papermill": {
     "duration": 0.360979,
     "end_time": "2023-08-22T20:30:04.747184",
     "exception": false,
     "start_time": "2023-08-22T20:30:04.386205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = sns.barplot(x = df['Sex'],y = df['Survived'], palette ='husl')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ff48190",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-22T20:30:04.771255Z",
     "iopub.status.busy": "2023-08-22T20:30:04.770411Z",
     "iopub.status.idle": "2023-08-22T20:30:05.870663Z",
     "shell.execute_reply": "2023-08-22T20:30:05.869673Z"
    },
    "papermill": {
     "duration": 1.114735,
     "end_time": "2023-08-22T20:30:05.873242",
     "exception": false,
     "start_time": "2023-08-22T20:30:04.758507",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "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": [
    "sns.countplot(x = 'Age', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a0a0397f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-22T20:30:05.897419Z",
     "iopub.status.busy": "2023-08-22T20:30:05.897003Z",
     "iopub.status.idle": "2023-08-22T20:30:06.348849Z",
     "shell.execute_reply": "2023-08-22T20:30:06.347697Z"
    },
    "papermill": {
     "duration": 0.467598,
     "end_time": "2023-08-22T20:30:06.352086",
     "exception": false,
     "start_time": "2023-08-22T20:30:05.884488",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7906ff786fe0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df.Parch,bins = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2af6bfb",
   "metadata": {
    "papermill": {
     "duration": 0.011896,
     "end_time": "2023-08-22T20:30:06.376375",
     "exception": false,
     "start_time": "2023-08-22T20:30:06.364479",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 16.139733,
   "end_time": "2023-08-22T20:30:07.411779",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-08-22T20:29:51.272046",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}