Pandas-Pivot(Titanic).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f086e3f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4fc3ba12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  Unnamed: 15  \n",
       "0      man        True  NaN  Southampton    no  False          NaN  \n",
       "1    woman       False    C    Cherbourg   yes  False          NaN  \n",
       "2    woman       False  NaN  Southampton   yes   True          NaN  \n",
       "3    woman       False    C  Southampton   yes  False          NaN  \n",
       "4      man        True  NaN  Southampton    no   True          NaN  \n",
       "..     ...         ...  ...          ...   ...    ...          ...  \n",
       "886    man        True  NaN  Southampton    no   True          NaN  \n",
       "887  woman       False    B  Southampton   yes   True          NaN  \n",
       "888  woman       False  NaN  Southampton    no  False          NaN  \n",
       "889    man        True    C    Cherbourg   yes   True          NaN  \n",
       "890    man        True  NaN   Queenstown    no   True          NaN  \n",
       "\n",
       "[891 rows x 16 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"titanic.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "687baee2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 16 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   survived     891 non-null    int64  \n",
      " 1   pclass       891 non-null    int64  \n",
      " 2   sex          891 non-null    object \n",
      " 3   age          714 non-null    float64\n",
      " 4   sibsp        891 non-null    int64  \n",
      " 5   parch        891 non-null    int64  \n",
      " 6   fare         891 non-null    float64\n",
      " 7   embarked     889 non-null    object \n",
      " 8   class        891 non-null    object \n",
      " 9   who          891 non-null    object \n",
      " 10  adult_male   891 non-null    bool   \n",
      " 11  deck         203 non-null    object \n",
      " 12  embark_town  889 non-null    object \n",
      " 13  alive        891 non-null    object \n",
      " 14  alone        891 non-null    bool   \n",
      " 15  Unnamed: 15  0 non-null      float64\n",
      "dtypes: bool(2), float64(3), int64(4), object(7)\n",
      "memory usage: 99.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b06b872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         int64\n",
       "pclass           int64\n",
       "sex             object\n",
       "age            float64\n",
       "sibsp            int64\n",
       "parch            int64\n",
       "fare           float64\n",
       "embarked        object\n",
       "class           object\n",
       "who             object\n",
       "adult_male        bool\n",
       "deck            object\n",
       "embark_town     object\n",
       "alive           object\n",
       "alone             bool\n",
       "Unnamed: 15    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d2ff7b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n",
       "       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',\n",
       "       'alive', 'alone', 'Unnamed: 15'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0e8fce9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 16)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8d0d2b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>alone</th>\n",
       "      <th>fare</th>\n",
       "      <th>parch</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">female</th>\n",
       "      <th>0.75</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.5166</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.8750</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>259.4750</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62.6542</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>114.1417</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">male</th>\n",
       "      <th>70.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>81.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.50</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>84.1584</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Unnamed: 15  adult_male  alone      fare  parch  pclass  sibsp  \\\n",
       "sex    age                                                                     \n",
       "female 0.75           0.0           0      0   38.5166      2       6      4   \n",
       "       1.00           0.0           0      0   26.8750      3       6      1   \n",
       "       2.00           0.0           0      0  259.4750      9      15      9   \n",
       "       3.00           0.0           0      0   62.6542      3       5      4   \n",
       "       4.00           0.0           0      0  114.1417      6      13      4   \n",
       "...                   ...         ...    ...       ...    ...     ...    ...   \n",
       "male   70.00          0.0           2      1   81.5000      1       3      1   \n",
       "       70.50          0.0           1      1    7.7500      0       3      0   \n",
       "       71.00          0.0           2      2   84.1584      0       2      0   \n",
       "       74.00          0.0           1      1    7.7750      0       3      0   \n",
       "       80.00          0.0           1      1   30.0000      0       1      0   \n",
       "\n",
       "              survived  \n",
       "sex    age              \n",
       "female 0.75          2  \n",
       "       1.00          2  \n",
       "       2.00          2  \n",
       "       3.00          1  \n",
       "       4.00          5  \n",
       "...                ...  \n",
       "male   70.00         0  \n",
       "       70.50         0  \n",
       "       71.00         0  \n",
       "       74.00         0  \n",
       "       80.00         1  \n",
       "\n",
       "[145 rows x 8 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, index = [\"sex\", \"age\"], aggfunc=\"sum\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a876a43b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class      First    Second     Third\n",
      "sex                                 \n",
      "female  0.968085  0.921053  0.500000\n",
      "male    0.368852  0.157407  0.135447\n"
     ]
    }
   ],
   "source": [
    "result = df.pivot_table('survived', index='sex', columns='class')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8b2a5152",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        survived\n",
      "sex             \n",
      "female  0.742038\n",
      "male    0.188908\n"
     ]
    }
   ],
   "source": [
    "result=df.groupby('sex')[['survived']].mean()\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a688d11a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class         First  Second  Third\n",
      "sex    age                        \n",
      "female 0.75     NaN     NaN   1.00\n",
      "       1.00     NaN     NaN   1.00\n",
      "       2.00     0.0     1.0   0.25\n",
      "       3.00     NaN     1.0   0.00\n",
      "       4.00     NaN     1.0   1.00\n",
      "...             ...     ...    ...\n",
      "male   70.00    0.0     0.0    NaN\n",
      "       70.50    NaN     NaN   0.00\n",
      "       71.00    0.0     NaN    NaN\n",
      "       74.00    NaN     NaN   0.00\n",
      "       80.00    1.0     NaN    NaN\n",
      "\n",
      "[145 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "result  =  df.pivot_table('survived', index=['sex','age'], columns='class')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "247d38e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      (10.0, 30.0]\n",
      "1      (30.0, 60.0]\n",
      "2      (10.0, 30.0]\n",
      "3      (30.0, 60.0]\n",
      "4      (30.0, 60.0]\n",
      "           ...     \n",
      "886    (10.0, 30.0]\n",
      "887    (10.0, 30.0]\n",
      "888             NaN\n",
      "889    (10.0, 30.0]\n",
      "890    (30.0, 60.0]\n",
      "Name: age, Length: 891, dtype: category\n",
      "Categories (4, interval[int64, right]): [(0, 10] < (10, 30] < (30, 60] < (60, 80]]\n"
     ]
    }
   ],
   "source": [
    "result = pd.cut(df['age'], [0, 10, 30, 60, 80])\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f4afe83d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class               First    Second     Third\n",
      "sex    age                                   \n",
      "female (0, 20]   0.928571  1.000000  0.510638\n",
      "       (20, 55]  0.968750  0.912281  0.407407\n",
      "male   (0, 20]   0.571429  0.526316  0.197368\n",
      "       (20, 55]  0.440000  0.054054  0.134503\n"
     ]
    }
   ],
   "source": [
    "age = pd.cut(df['age'], [0, 20, 55])\n",
    "result = df.pivot_table('survived', index=['sex', age], columns='class')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9b6ed096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class               First    Second     Third\n",
      "sex    age                                   \n",
      "female (0, 20]   0.928571  1.000000  0.510638\n",
      "       (20, 55]  0.968750  0.912281  0.407407\n",
      "male   (0, 20]   0.571429  0.526316  0.197368\n",
      "       (20, 55]  0.440000  0.054054  0.134503\n"
     ]
    }
   ],
   "source": [
    "age = pd.cut(df['age'], [0,20,55])\n",
    "result = df.pivot_table('survived', index=['sex', age], columns='class')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d8149482",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fare            (-0.001, 14.454]                     (14.454, 512.329]  \\\n",
      "pclass                         1         2         3                 1   \n",
      "sex    age                                                               \n",
      "female (0, 10]               NaN       NaN  0.800000          0.000000   \n",
      "       (10, 30]              NaN  0.933333  0.568182          0.970588   \n",
      "       (30, 60]              NaN  0.846154  0.142857          0.979167   \n",
      "       (60, 80]              NaN       NaN  1.000000          1.000000   \n",
      "male   (0, 10]               NaN       NaN  1.000000          1.000000   \n",
      "       (10, 30]              NaN  0.034483  0.140625          0.458333   \n",
      "       (30, 60]              0.0  0.130435  0.109375          0.440678   \n",
      "       (60, 80]              NaN  0.333333  0.000000          0.083333   \n",
      "\n",
      "fare                                 \n",
      "pclass                  2         3  \n",
      "sex    age                           \n",
      "female (0, 10]   1.000000  0.411765  \n",
      "       (10, 30]  0.904762  0.307692  \n",
      "       (30, 60]  0.941176  0.333333  \n",
      "       (60, 80]       NaN       NaN  \n",
      "male   (0, 10]   1.000000  0.263158  \n",
      "       (10, 30]  0.000000  0.130435  \n",
      "       (30, 60]  0.047619  0.166667  \n",
      "       (60, 80]       NaN       NaN  \n"
     ]
    }
   ],
   "source": [
    "fare = pd.qcut(df['fare'], 2)\n",
    "age = pd.cut(df['age'], [0, 10, 30, 60, 80])\n",
    "result = df.pivot_table('survived', index=['sex', age], columns=[fare, 'pclass'])\n",
    "print(result)"
   ]
  }
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