applying EDA  (Titanic dataset).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "868dd4bb",
   "metadata": {},
   "source": [
    "using Library such pandas for DataFram load or to read\n",
    "using "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "85385db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "titanic = pd.read_csv(\"train (1).csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "410b6f4a",
   "metadata": {},
   "source": [
    "# summary first five records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "445674e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "                                                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",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec612d94",
   "metadata": {},
   "source": [
    "# summary information a bout titanic dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "542c90ec",
   "metadata": {},
   "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": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b0b7f5a",
   "metadata": {},
   "source": [
    "### this line return a series of labeled data and the missing value in each label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0b15b10c",
   "metadata": {},
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1abe3cea",
   "metadata": {},
   "source": [
    "# Countplot \n",
    "# Survived vs Sex(Binary feature)\n",
    "## Just by observing the graph, it can be approximated that the survival rate of men is around 20% and that of women is around 75%. Therefore, whether a passenger is a male or a female plays an important role in determining if one is going to survive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "70f618dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f898e133d0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 411.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "sns.catplot(x =\"Sex\", hue =\"Survived\",\n",
    "kind =\"count\", data = titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07f757c2",
   "metadata": {},
   "source": [
    "# Pclass (Ordinal Feature) vs Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3d4878bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='Pclass'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the dataset by Pclass and Survived and then unstack them\n",
    "group = titanic.groupby(['Pclass', 'Survived'])\n",
    "pclass_survived = group.size().unstack()\n",
    " \n",
    "# Heatmap - Color encoded 2D representation of data.\n",
    "# fmt = format which is in this case decimal.\n",
    "sns.heatmap(pclass_survived, annot = True ,fmt =\"d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09bb0c16",
   "metadata": {},
   "source": [
    "# Age (Continuous Feature) vs Survived\n",
    "### Violin plot Displays distribution of data across all levels of a category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c87465c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4f2e2814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='Age'>"
      ]
     },
     "execution_count": 68,
     "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": [
    "sns.violinplot(x =\"Sex\", y =\"Age\", hue =\"Survived\",\n",
    "data = titanic, split = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "901e687f",
   "metadata": {},
   "source": [
    "#### This graph gives(ABOVE) a summary of the age range of men, women and children who were saved. The survival rate is –  \n",
    "### Good for children.\n",
    "### High for women in the age range 20-50.\n",
    "### Less for men as the age increases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1988559d",
   "metadata": {},
   "source": [
    "### Family_Size denotes the number of people in a passenger’s family. It is calculated by summing the SibSp and Parch columns of a respective passenger. Also, another column Alone is added to check the chances of survival of a lone passenger against the one with a family.\n",
    "\n",
    "### Important observations – \n",
    "\n",
    "### If a passenger is alone, the survival rate is less.\n",
    "### If the family size is greater than 5, chances of survival decrease considerably."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8c99b828",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3717: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3717: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f8992b2bb0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adding a column Family_Size\n",
    "titanic['Family_Size'] = 0\n",
    "titanic['Family_Size'] = titanic['Parch']+titanic['SibSp']\n",
    " \n",
    "# Adding a column Alone\n",
    "titanic['Alone'] = 0\n",
    "titanic.loc[titanic.Family_Size == 0, 'Alone'] = 1\n",
    " \n",
    "# Factorplot for Family_Size\n",
    "sns.factorplot(x ='Family_Size', y ='Survived', data = titanic)\n",
    " \n",
    "# Factorplot for Alone\n",
    "sns.factorplot(x ='Alone', y ='Survived', data = titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe79932",
   "metadata": {},
   "source": [
    "# Bar Plot for Fare (Continuous Feature)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ea304240",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Fare_Range', ylabel='Survived'>"
      ]
     },
     "execution_count": 70,
     "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": [
    "# Divide Fare into 3 bins\n",
    "titanic['Fare_Range'] = pd.qcut(titanic['Fare'], 3)\n",
    " \n",
    "# Barplot - Shows approximate values based\n",
    "# on the height of bars.\n",
    "sns.barplot(x ='Fare_Range', y ='Survived',\n",
    "data = titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2b7abd0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age_Range', ylabel='Survived'>"
      ]
     },
     "execution_count": 71,
     "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": [
    "titanic['Age_Range'] = pd.qcut(titanic['Age'], 5)\n",
    " \n",
    "# Barplot - Shows approximate values based\n",
    "# on the height of bars.\n",
    "sns.barplot(x ='Age_Range', y ='Survived',\n",
    "data = titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "562de35b",
   "metadata": {},
   "source": [
    "### Categorical Count Plots for Embarked Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "cb66bc71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f89b282880>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1131.88x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Countplot\n",
    "sns.catplot(x ='Embarked', hue ='Survived',\n",
    "kind ='count', col ='Pclass', data = titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f94776dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "corrl = titanic.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d228d8e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corrl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da08394c",
   "metadata": {},
   "source": [
    "### Pclass - Survived\n",
    "###  Sex- Survived\n",
    "### SibSp - Survived\n",
    "### Parch - Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "03b14ac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plcass vs Survived\n",
    "titanic[[\"Pclass\",\"Survived\"]].groupby([\"Pclass\"], as_index = False).mean().sort_values(by=\"Survived\",ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a402321d",
   "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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sex vs Survived\n",
    "titanic[[\"Sex\",\"Survived\"]].groupby([\"Sex\"], as_index = False).mean().sort_values(\n",
    "    by=\"Survived\",ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7c25d84d",
   "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>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sibsp vs Survived\n",
    "titanic[[\"SibSp\",\"Survived\"]].groupby([\"SibSp\"], as_index = False).mean().sort_values(by=\"Survived\",ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9a04bed6",
   "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>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Parch vs Survived\n",
    "titanic[[\"Parch\",\"Survived\"]].groupby([\"Parch\"], as_index = False).mean().sort_values(by=\"Survived\",ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2cd2b9",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33bcc810",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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