P5 - Frequency Tables .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "549dbf56",
   "metadata": {},
   "source": [
    "Discovering relationships between variables is the fundamental goal of data analysis. Frequency tables are a basic tool we can use to explore data and get an idea of the relationships between variables. A frequency table is just a data table that shows the counts of one or more categorical variables.\n",
    "\n",
    "To explore frequency tables, we'll revisit the Titanic training set. We will start by performing a couple of the same preprocessing steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a9aeef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e16b078",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df8b8941",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b873418e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b04a3856",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
       "       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n",
       "       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n",
       "       'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n",
       "       'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n",
       "       'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n",
       "       'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n",
       "       'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n",
       "       'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n",
       "       'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n",
       "       'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n",
       "       'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n",
       "       'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n",
       "       'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n",
       "       'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n",
       "       'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n",
       "       'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n",
       "       'C148'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's have a look on the cabin column of the dataset\n",
    "\n",
    "df['Cabin'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f03caca",
   "metadata": {},
   "source": [
    "* As we ccan see that it does not follow any categorical pattern, but still we can categories this using it's first letter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98eba771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['n', 'C', 'n', 'C', 'n', 'n', 'E', 'n', 'n', 'n', 'G', 'C', 'n',\n",
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       "       'C', 'n', 'D', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'A', 'n',\n",
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       "       'B', 'n', 'n', 'n', 'E', 'n', 'C', 'n', 'C', 'n', 'E', 'C', 'B',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'C', 'E', 'n', 'n', 'n', 'n', 'n',\n",
       "       'C', 'n', 'D', 'n', 'B', 'n', 'C', 'C', 'n', 'n', 'n', 'C', 'E',\n",
       "       'n', 'T', 'F', 'C', 'n', 'n', 'n', 'F', 'n', 'n', 'n', 'n', 'n',\n",
       "       'C', 'n', 'n', 'n', 'n', 'E', 'n', 'n', 'n', 'n', 'n', 'n', 'n',\n",
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       "       'C', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'B', 'n', 'n', 'D', 'G', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n',\n",
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       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'E', 'C', 'n', 'n', 'n', 'E', 'B', 'n', 'n', 'C', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'A', 'n', 'n', 'n', 'C', 'n', 'n', 'C', 'C', 'n',\n",
       "       'n', 'E', 'D', 'n', 'n', 'E', 'n', 'E', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'D', 'n', 'A', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'B', 'n', 'C', 'B', 'n', 'n', 'n', 'n', 'C', 'n',\n",
       "       'n', 'n', 'D', 'n', 'C', 'n', 'n', 'n', 'n', 'n', 'B', 'C', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'E', 'n', 'n', 'D', 'F', 'n', 'n', 'n',\n",
       "       'B', 'n', 'n', 'B', 'n', 'n', 'n', 'C', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'B', 'n', 'n', 'B', 'B', 'n', 'n', 'n', 'C', 'n',\n",
       "       'n', 'n', 'n', 'n', 'C', 'n', 'n', 'n', 'n', 'n', 'A', 'n', 'E',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'C',\n",
       "       'E', 'n', 'n', 'n', 'n', 'E', 'n', 'n', 'n', 'C', 'n', 'A', 'n',\n",
       "       'E', 'n', 'B', 'n', 'n', 'n', 'D', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'A', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'C', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'F', 'n', 'n', 'D', 'n', 'n',\n",
       "       'n', 'D', 'n', 'D', 'n', 'n', 'A', 'n', 'B', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'B', 'n', 'n', 'n', 'D', 'n', 'A', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'D', 'n', 'n', 'E',\n",
       "       'n', 'n', 'n', 'n', 'n', 'n', 'C', 'n', 'B', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'B', 'n', 'D', 'n', 'n', 'n', 'n', 'n', 'n', 'n',\n",
       "       'B', 'B', 'n', 'n', 'n', 'n', 'n', 'n', 'n', 'C', 'F', 'C', 'E',\n",
       "       'n', 'n', 'n', 'n', 'n', 'E', 'n', 'n', 'C', 'C', 'C', 'n', 'n',\n",
       "       'F', 'C', 'E', 'n', 'n', 'n', 'n', 'n', 'n', 'E', 'n', 'n', 'n',\n",
       "       'n', 'n', 'B', 'n', 'n', 'n', 'n', 'n', 'n', 'B', 'n', 'n', 'D',\n",
       "       'C', 'B', 'n', 'n', 'B', 'n', 'n', 'D', 'n', 'n', 'E', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'B', 'n', 'n', 'n', 'B', 'n', 'D', 'n',\n",
       "       'n', 'n', 'n', 'n', 'n', 'E', 'n', 'n', 'n', 'F', 'n', 'n', 'B',\n",
       "       'n', 'B', 'D', 'n', 'n', 'n', 'n', 'n', 'n', 'B', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'D', 'n', 'n', 'n', 'n', 'n', 'B', 'n', 'n', 'n',\n",
       "       'A', 'n', 'n', 'E', 'n', 'n', 'n', 'n', 'n', 'B', 'n', 'n', 'n',\n",
       "       'n', 'B', 'n', 'n', 'E', 'n', 'n', 'n', 'n', 'n', 'B', 'n', 'n',\n",
       "       'n', 'n', 'n', 'E', 'n', 'n', 'n', 'C', 'n', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'n', 'C', 'n', 'n', 'n', 'D', 'n', 'n', 'n', 'E',\n",
       "       'n', 'n', 'n', 'n', 'D', 'n', 'n', 'n', 'n', 'A', 'n', 'n', 'n',\n",
       "       'D', 'B', 'n', 'n', 'n', 'n', 'n', 'n', 'C', 'n', 'n', 'n', 'n',\n",
       "       'n', 'n', 'n', 'B', 'n', 'C', 'n'], dtype='<U1')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Before changing the cabin column into categorical value we have to convert it into the str types \n",
    "\n",
    "char_cabin  = df['Cabin'].astype(str)\n",
    "# Creating an array of the first letter of the Cabin column\n",
    "\n",
    "new_cabin = np.array([i[0] for i in char_cabin])\n",
    "new_cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95c2e2ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now categories the cabin column\n",
    "\n",
    "df['Cabin'] =   pd.Categorical(new_cabin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f574fb94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       int64\n",
       "Survived          int64\n",
       "Pclass            int64\n",
       "Name             object\n",
       "Sex              object\n",
       "Age             float64\n",
       "SibSp             int64\n",
       "Parch             int64\n",
       "Ticket           object\n",
       "Fare            float64\n",
       "Cabin          category\n",
       "Embarked         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93813cda",
   "metadata": {},
   "source": [
    "**One-Way Tables**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b0f6f42",
   "metadata": {},
   "source": [
    "Create frequency tables (also known as crosstabs) in pandas using the pd.crosstab() function. The function takes one or more array-like objects as indexes or columns and then constructs a new DataFrame of variable counts based on the supplied arrays. Let's make a one-way table of the survived variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8b9f1f93",
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0     count\n",
       "Survived       \n",
       "0           549\n",
       "1           342"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = pd.crosstab(index = df['Survived'],\n",
    "                columns = 'count')\n",
    "t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "782c81d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bff097dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <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>n</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>C</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>n</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",
       "\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",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500     n        S  \n",
       "1      0          PC 17599  71.2833     C        C  \n",
       "2      0  STON/O2. 3101282   7.9250     n        S  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1acb5fc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>184</td>\n",
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       "      <th>3</th>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0   count\n",
       "Pclass       \n",
       "1         216\n",
       "2         184\n",
       "3         491"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index = df['Pclass'],\n",
    "           columns = 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "45eef949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
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       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0   count\n",
       "Sex          \n",
       "female    314\n",
       "male      577"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index = df['Sex'],\n",
    "           columns = 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "16609dd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "    <tr>\n",
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       "      <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>C</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>n</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",
       "\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",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500     n        S  \n",
       "1      0          PC 17599  71.2833     C        C  \n",
       "2      0  STON/O2. 3101282   7.9250     n        S  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3b2276ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr>\n",
       "      <th>SibSp</th>\n",
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       "      <td>28</td>\n",
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       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0  count\n",
       "SibSp       \n",
       "0        608\n",
       "1        209\n",
       "2         28\n",
       "3         16\n",
       "4         18\n",
       "5          5\n",
       "8          7"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index = df['SibSp'],\n",
    "           columns = 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c468d915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0  count\n",
       "Cabin       \n",
       "A         15\n",
       "B         47\n",
       "C         59\n",
       "D         33\n",
       "E         32\n",
       "F         13\n",
       "G          4\n",
       "T          1\n",
       "n        687"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabin_tab = pd.crosstab(index = df['Cabin'],\n",
    "           columns = 'count')\n",
    "cabin_tab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eb1dcc6",
   "metadata": {},
   "source": [
    "We can also use the value_counts() function to on a pandas series (a single column) to check its counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2c23e4b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb888bef",
   "metadata": {},
   "source": [
    "Even these simple one-way tables give us some useful insight: we immediately get a sense of distribution of records across the categories. For instance, we see that males outnumbered females by a significant margin and that there were more third class passengers than first and second class passengers combined."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "999df4ee",
   "metadata": {},
   "source": [
    "One of the most useful aspects of frequency tables is that they allow us to extract the proportion of the data that belongs to each category. With a one-way table, we can do this by dividing each table value by the total number of records in the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4464224b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.016835</td>\n",
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       "      <th>B</th>\n",
       "      <td>0.052750</td>\n",
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       "      <th>D</th>\n",
       "      <td>0.037037</td>\n",
       "    </tr>\n",
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       "      <td>0.035915</td>\n",
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       "      <th>F</th>\n",
       "      <td>0.014590</td>\n",
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       "      <th>G</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>0.771044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0     count\n",
       "Cabin          \n",
       "A      0.016835\n",
       "B      0.052750\n",
       "C      0.066218\n",
       "D      0.037037\n",
       "E      0.035915\n",
       "F      0.014590\n",
       "G      0.004489\n",
       "T      0.001122\n",
       "n      0.771044"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabin_tab/cabin_tab.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8f18f078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.683502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5.274972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>6.621773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>3.703704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>3.591470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>1.459035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.448934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.112233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>77.104377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0      count\n",
       "Cabin           \n",
       "A       1.683502\n",
       "B       5.274972\n",
       "C       6.621773\n",
       "D       3.703704\n",
       "E       3.591470\n",
       "F       1.459035\n",
       "G       0.448934\n",
       "T       0.112233\n",
       "n      77.104377"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can change the above result as the percentage \n",
    "\n",
    "cabin_tab_dis = (cabin_tab/cabin_tab.sum())*100\n",
    "cabin_tab_dis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "138a7f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cabin_tab_dis.rename(columns = {'count':'Percent'},inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5839de52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.683502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5.274972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>6.621773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>3.703704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>3.591470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>1.459035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.448934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.112233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>77.104377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0    Percent\n",
       "Cabin           \n",
       "A       1.683502\n",
       "B       5.274972\n",
       "C       6.621773\n",
       "D       3.703704\n",
       "E       3.591470\n",
       "F       1.459035\n",
       "G       0.448934\n",
       "T       0.112233\n",
       "n      77.104377"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabin_tab_dis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74bd8a8e",
   "metadata": {},
   "source": [
    "**Two-Way Tables**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce7786ef",
   "metadata": {},
   "source": [
    "Two-way frequency tables, also called contingency tables, are tables of counts with two dimensions where each dimension is a different variable. Two-way tables can give you insight into the relationship between two variables. To create a two way table, pass two variables to the pd.crosstab() function instead of one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e5465289",
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>died</th>\n",
       "      <td>81</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>233</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "died          81   468\n",
       "survived     233   109"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table of survival vs sex\n",
    "\n",
    "sur_sex = pd.crosstab(index = df['Survived'],\n",
    "                     columns = df['Sex'])\n",
    "sur_sex.index = ['died','survived']\n",
    "sur_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "03b6784b",
   "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>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>died</th>\n",
       "      <td>80</td>\n",
       "      <td>97</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>136</td>\n",
       "      <td>87</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass      1   2    3\n",
       "died       80  97  372\n",
       "survived  136  87  119"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table of survival vs passenger class\n",
    "\n",
    "df.head(2)\n",
    "sur_pclass = pd.crosstab(index = df['Survived'],\n",
    "           columns  = df['Pclass'])\n",
    "\n",
    "sur_pclass.index = ['died','survived']\n",
    "sur_pclass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a62d0b",
   "metadata": {},
   "source": [
    "we can get the marginal counts (totals for each row and column) by including the argument margins=True:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "61b4a668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class1</th>\n",
       "      <th>class2</th>\n",
       "      <th>class3</th>\n",
       "      <th>row_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>died</th>\n",
       "      <td>80</td>\n",
       "      <td>97</td>\n",
       "      <td>372</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>136</td>\n",
       "      <td>87</td>\n",
       "      <td>119</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>col_total</th>\n",
       "      <td>216</td>\n",
       "      <td>184</td>\n",
       "      <td>491</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           class1  class2  class3  row_total\n",
       "died           80      97     372        549\n",
       "survived      136      87     119        342\n",
       "col_total     216     184     491        891"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sur_class = pd.crosstab(index = df['Survived'],\n",
    "           columns = df['Pclass'],\n",
    "           margins = True)\n",
    "\n",
    "sur_class.index = ['died','survived','col_total']\n",
    "sur_class.columns = ['class1','class2','class3','row_total']\n",
    "\n",
    "sur_class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c03f72a2",
   "metadata": {},
   "source": [
    "**Higher Dimensional Tables**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f027e78b",
   "metadata": {},
   "source": [
    "The crosstab() function lets you create tables out of more than two categories. Higher dimensional tables can be a little confusing to look at, but they can also yield finer-grained insight into interactions between multiple variables. Let's create a 3-way table inspecting survival, sex and passenger class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1366dd28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>77</td>\n",
       "      <td>6</td>\n",
       "      <td>91</td>\n",
       "      <td>72</td>\n",
       "      <td>300</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>45</td>\n",
       "      <td>70</td>\n",
       "      <td>17</td>\n",
       "      <td>72</td>\n",
       "      <td>47</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>94</td>\n",
       "      <td>122</td>\n",
       "      <td>76</td>\n",
       "      <td>108</td>\n",
       "      <td>144</td>\n",
       "      <td>347</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass        1           2           3       All\n",
       "Sex      female male female male female male     \n",
       "Survived                                         \n",
       "0             3   77      6   91     72  300  549\n",
       "1            91   45     70   17     72   47  342\n",
       "All          94  122     76  108    144  347  891"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sur_sex_class = pd.crosstab(index = df['Survived'],\n",
    "                           columns = [df['Pclass'],df['Sex']],\n",
    "                           margins = True)\n",
    "sur_sex_class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba7bdec7",
   "metadata": {},
   "source": [
    "Notice that by passing a second variable to the columns argument, the resulting table has columns categorized by both Pclass and Sex. The outermost index (Pclass) returns sections of the table instead of individual columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "df73606c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>76</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Survived              \n",
       "0              6    91\n",
       "1             70    17\n",
       "All           76   108"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sur_sex_class[2]  # for getting the subtable under pclass2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d6df044c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0       6\n",
       "1      70\n",
       "All    76\n",
       "Name: female, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sur_sex_class[2]['female'] # get the female column within the pclass2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "53f9711e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.031915</td>\n",
       "      <td>0.631148</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.842593</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>0.616162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass           1                   2                3                 All\n",
       "Sex         female      male    female      male female      male          \n",
       "Survived                                                                   \n",
       "0         0.031915  0.631148  0.078947  0.842593    0.5  0.864553  0.616162\n",
       "1         0.968085  0.368852  0.921053  0.157407    0.5  0.135447  0.383838\n",
       "All       1.000000  1.000000  1.000000  1.000000    1.0  1.000000  1.000000"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get the fractional value of the totaldeivide the each column by the total\n",
    "\n",
    "sur_sex_class/sur_sex_class.loc['All']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f8ea64c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.191489</td>\n",
       "      <td>63.114754</td>\n",
       "      <td>7.894737</td>\n",
       "      <td>84.259259</td>\n",
       "      <td>50.0</td>\n",
       "      <td>86.455331</td>\n",
       "      <td>61.616162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>96.808511</td>\n",
       "      <td>36.885246</td>\n",
       "      <td>92.105263</td>\n",
       "      <td>15.740741</td>\n",
       "      <td>50.0</td>\n",
       "      <td>13.544669</td>\n",
       "      <td>38.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass             1                       2                  3              \\\n",
       "Sex           female        male      female        male female        male   \n",
       "Survived                                                                      \n",
       "0           3.191489   63.114754    7.894737   84.259259   50.0   86.455331   \n",
       "1          96.808511   36.885246   92.105263   15.740741   50.0   13.544669   \n",
       "All       100.000000  100.000000  100.000000  100.000000  100.0  100.000000   \n",
       "\n",
       "Pclass           All  \n",
       "Sex                   \n",
       "Survived              \n",
       "0          61.616162  \n",
       "1          38.383838  \n",
       "All       100.000000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting the above result in the percentage form\n",
    "\n",
    "(sur_sex_class/sur_sex_class.loc['All'])*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "408d1ca0",
   "metadata": {},
   "source": [
    "* Here we see something quite interesting: over 90% of women in first class and second class survived, but only 50% of women in third class survived. Men in first class also survived at a greater rate than men in lower classes. Passenger class seems to have a significant impact on survival, so it would likely be useful to include as a feature in a predictive model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cce0bf33",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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