{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e5aab67e-ab68-4112-9b52-2170cf335d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student_no</th>\n",
       "      <th>class</th>\n",
       "      <th>science</th>\n",
       "      <th>english</th>\n",
       "      <th>math</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>98</td>\n",
       "      <td>50</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>60</td>\n",
       "      <td>97</td>\n",
       "      <td>60</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>78</td>\n",
       "      <td>86</td>\n",
       "      <td>45</td>\n",
       "      <td>w</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>58</td>\n",
       "      <td>98</td>\n",
       "      <td>30</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>w</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>B</td>\n",
       "      <td>98</td>\n",
       "      <td>89</td>\n",
       "      <td>50</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>B</td>\n",
       "      <td>25</td>\n",
       "      <td>78</td>\n",
       "      <td>90</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "      <td>98</td>\n",
       "      <td>20</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>C</td>\n",
       "      <td>45</td>\n",
       "      <td>93</td>\n",
       "      <td>50</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student_no class  science  english  math sex\n",
       "0           1     A       50       98    50   m\n",
       "1           2     A       60       97    60   w\n",
       "2           3     A       78       86    45   w\n",
       "3           4     A       58       98    30   m\n",
       "4           5     B       65       80    90   w\n",
       "5           6     B       98       89    50   m\n",
       "6           7     B       45       90    80   m\n",
       "7           8     B       25       78    90   w\n",
       "8           9     C       15       98    20   w\n",
       "9          10     C       45       93    50   w"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"../data_set/2.데이터 클린징/group_sample.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9907fbde-69f2-48e7-b136-60cc6fcfcf85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000029EFA338BD0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# class 컬럼을 기준으로 그루핑\n",
    "df_class = df.groupby(['class'])\n",
    "df_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eefe2e5c-e5a6-4548-a362-38233bc12f4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\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>student_no</th>\n",
       "      <th>class</th>\n",
       "      <th>science</th>\n",
       "      <th>english</th>\n",
       "      <th>math</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>A</td>\n",
       "      <td>50</td>\n",
       "      <td>98</td>\n",
       "      <td>50</td>\n",
       "      <td>m</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
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       "      <td>97</td>\n",
       "      <td>60</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>78</td>\n",
       "      <td>86</td>\n",
       "      <td>45</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>58</td>\n",
       "      <td>98</td>\n",
       "      <td>30</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student_no class  science  english  math sex\n",
       "0           1     A       50       98    50   m\n",
       "1           2     A       60       97    60   w\n",
       "2           3     A       78       86    45   w\n",
       "3           4     A       58       98    30   m"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 원하는 그룹을 확인하고자 할때\n",
    "# get_group() 을 사용\n",
    "df_class.get_group(\"A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1f4096b9-72b7-42ee-83dd-b7a867c71b84",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>math</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.5</td>\n",
       "      <td>61.50</td>\n",
       "      <td>94.75</td>\n",
       "      <td>46.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>6.5</td>\n",
       "      <td>58.25</td>\n",
       "      <td>84.25</td>\n",
       "      <td>77.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>9.5</td>\n",
       "      <td>30.00</td>\n",
       "      <td>95.50</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       student_no  science  english   math\n",
       "class                                     \n",
       "A             2.5    61.50    94.75  46.25\n",
       "B             6.5    58.25    84.25  77.50\n",
       "C             9.5    30.00    95.50  35.00"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 각 그룹의 평균을 구하는 방법\n",
    "# numeric_only 속성을 True 로 넣어\n",
    "# 문자 값을 제외한 숫자만 평균을 구한다\n",
    "df_class.mean( numeric_only = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fc43159e-b8da-4574-8253-09d63253eb30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000029E850A38D0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 여러 기준으로 그룹화 가능\n",
    "df.groupby(['class', 'sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2c3d8a3c-0e7f-4f16-9865-7e7cddb28d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>student_no</th>\n",
       "      <th>science</th>\n",
       "      <th>english</th>\n",
       "      <th>math</th>\n",
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       "      <th>class</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>m</th>\n",
       "      <td>2.5</td>\n",
       "      <td>54.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>2.5</td>\n",
       "      <td>69.0</td>\n",
       "      <td>91.5</td>\n",
       "      <td>52.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>m</th>\n",
       "      <td>6.5</td>\n",
       "      <td>71.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>6.5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <th>w</th>\n",
       "      <td>9.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>95.5</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           student_no  science  english  math\n",
       "class sex                                    \n",
       "A     m           2.5     54.0     98.0  40.0\n",
       "      w           2.5     69.0     91.5  52.5\n",
       "B     m           6.5     71.5     89.5  65.0\n",
       "      w           6.5     45.0     79.0  90.0\n",
       "C     w           9.5     30.0     95.5  35.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 여러 기준으로 그룹화된 그룹의 각 평균을 출력\n",
    "df.groupby(['class', 'sex']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "30470683-0e91-4f1f-a785-cd55f01c6050",
   "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",
       "    .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></th>\n",
       "      <th>math</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>46.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>77.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        math\n",
       "class       \n",
       "A      46.25\n",
       "B      77.50\n",
       "C      35.00"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 수학의 평균만 출력\n",
    "df_class[['math']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f89a3893-43d2-425a-802d-13a58406e110",
   "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",
       "    .dataframe tbody tr th {\n",
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       "\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></th>\n",
       "      <th>student_no</th>\n",
       "      <th>science</th>\n",
       "      <th>english</th>\n",
       "      <th>math</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</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>A</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       student_no  science  english  math  sex\n",
       "class                                         \n",
       "A               4        4        4     4    4\n",
       "B               4        4        4     4    4\n",
       "C               2        2        2     2    2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# class 별로 묶인 것들의 갯수를 확인\n",
    "df_class.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f25288ce-c84a-445b-9b3c-5e974fc918e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5af4813e-6d0b-4adc-9fd9-31277152a90c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f74ad207-a40c-43c1-b78f-ea8c856b21b1",
   "metadata": {},
   "source": [
    "* agg : 집계 연산기능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "603d8ceb-bcaf-4798-9346-33f9e52a01cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       math  english\n",
       "class               \n",
       "A        60       86\n",
       "B        90       78\n",
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      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# numpy 를 사용해 수학의 최댓값과 영어의 최솟값을 구함\n",
    "df_class.agg( { 'math':'max', 'english':'min' } )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1f9d4c9f-b512-4678-8919-e6394cc3807a",
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   "outputs": [
    {
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       "       math  english\n",
       "class               \n",
       "A        60       86\n",
       "B        90       78\n",
       "C        50       93"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 최댓값, 최솟값을 다른 형태로 출력\n",
    "df_class.agg({'math':'max', 'english':'min'})"
   ]
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  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "73019448-5d34-4a71-8b3a-81476da22287",
   "metadata": {},
   "outputs": [
    {
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       "      <td>94.75</td>\n",
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       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>310</td>\n",
       "      <td>84.25</td>\n",
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       "      <th>C</th>\n",
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       "       math  english\n",
       "class               \n",
       "A       185    94.75\n",
       "B       310    84.25\n",
       "C        70    95.50"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 수학의 합, 영어의 평균을 출력\n",
    "df_class.agg({'math':'sum', 'english':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "882719ac-a038-4a87-938e-fc6920306460",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "      math     english\n",
       "       sum max    mean\n",
       "class                 \n",
       "A      185  60   94.75\n",
       "B      310  90   84.25\n",
       "C       70  50   95.50"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 수학의 합과 최댓값을 둘 다 구하고 영어의 평균을 구함\n",
    "df_class.agg({'math': ['sum', 'max'], 'english':'mean'})"
   ]
  }
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