Pandas-Pivot(Titanic2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b3d4fbcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67078040",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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",
       "  </tbody>\n",
       "</table>\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",
       "     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  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('titanic.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8807f880",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Unnamed: 15</th>\n",
       "      <th colspan=\"3\" halign=\"left\">adult_male</th>\n",
       "      <th colspan=\"3\" halign=\"left\">age</th>\n",
       "      <th>alive</th>\n",
       "      <th>...</th>\n",
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       "      <th colspan=\"3\" halign=\"left\">who</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>sex</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>76</td>\n",
       "      <td>144</td>\n",
       "      <td>85</td>\n",
       "      <td>74</td>\n",
       "      <td>102</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>94</td>\n",
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       "      <td>94</td>\n",
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       "      <td>76</td>\n",
       "      <td>144</td>\n",
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       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>108</td>\n",
       "      <td>347</td>\n",
       "      <td>101</td>\n",
       "      <td>99</td>\n",
       "      <td>253</td>\n",
       "      <td>122</td>\n",
       "      <td>...</td>\n",
       "      <td>347</td>\n",
       "      <td>122</td>\n",
       "      <td>108</td>\n",
       "      <td>347</td>\n",
       "      <td>122</td>\n",
       "      <td>108</td>\n",
       "      <td>347</td>\n",
       "      <td>122</td>\n",
       "      <td>108</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 15       adult_male            age          alive  ... parch  \\\n",
       "pclass           1  2  3          1    2    3    1   2    3     1  ...     3   \n",
       "sex                                                                ...         \n",
       "female           0  0  0         94   76  144   85  74  102    94  ...   144   \n",
       "male             0  0  0        122  108  347  101  99  253   122  ...   347   \n",
       "\n",
       "       sibsp           survived            who            \n",
       "pclass     1    2    3        1    2    3    1    2    3  \n",
       "sex                                                       \n",
       "female    94   76  144       94   76  144   94   76  144  \n",
       "male     122  108  347      122  108  347  122  108  347  \n",
       "\n",
       "[2 rows x 42 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = df.pivot_table(index=['sex'], columns=\"pclass\", aggfunc='count')\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ff55a44",
   "metadata": {},
   "outputs": [
    {
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       "      <th>class</th>\n",
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       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
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       "      <th>sex</th>\n",
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       "  <tbody>\n",
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       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
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      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "result = df.groupby(['sex', 'class'])['survived'].agg('mean').unstack()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69eb4858",
   "metadata": {},
   "outputs": [
    {
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       "      <th>class</th>\n",
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       "      <th>All</th>\n",
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       "    <tr>\n",
       "      <th>sex</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
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       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
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       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
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      "text/plain": [
       "class      First    Second     Third       All\n",
       "sex                                           \n",
       "female  0.968085  0.921053  0.500000  0.742038\n",
       "male    0.368852  0.157407  0.135447  0.188908\n",
       "All     0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = df.pivot_table('survived', index='sex', columns='class', margins=True)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3eafa0cf",
   "metadata": {},
   "outputs": [
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       "      <th>pclass</th>\n",
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       "      <th>female</th>\n",
       "      <td>94</td>\n",
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       "      <td>144</td>\n",
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       "      <th>male</th>\n",
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      "text/plain": [
       "pclass    1    2    3\n",
       "sex                  \n",
       "female   94   76  144\n",
       "male    122  108  347"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='sex', columns='pclass', values='survived', aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f5cf6368",
   "metadata": {},
   "outputs": [
    {
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       "      <th>female</th>\n",
       "      <td>106.125798</td>\n",
       "      <td>21.970121</td>\n",
       "      <td>16.118810</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
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       "      <th>male</th>\n",
       "      <td>67.226127</td>\n",
       "      <td>19.741782</td>\n",
       "      <td>12.661633</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
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       "              fare                       survived             \n",
       "class        First     Second      Third    First Second Third\n",
       "sex                                                           \n",
       "female  106.125798  21.970121  16.118810       91     70    72\n",
       "male     67.226127  19.741782  12.661633       45     17    47"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    "df.pivot_table(index='sex', columns='class', aggfunc={'survived':sum, 'fare':'mean'})"
   ]
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   "execution_count": 12,
   "id": "c451f98a",
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   "execution_count": 14,
   "id": "4ea2164c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "survived        0\n",
       "pclass          0\n",
       "sex             0\n",
       "age             0\n",
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   "execution_count": 15,
   "id": "208a8fe1",
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   "execution_count": 16,
   "id": "cf334282",
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       "embark_town  Cherbourg                     Queenstown                   \\\n",
       "class            First    Second     Third      First Second     Third   \n",
       "sex    alone                                                             \n",
       "female False  1.000000  1.000000  0.611111        1.0    NaN  0.625000   \n",
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       "\n",
       "embark_town  Southampton                      \n",
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       "sex    alone                                  \n",
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    "df.pivot_table('survived', ['sex' , 'alone' ], [ 'embark_town', 'class' ])"
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