{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "80c54a4b-533f-4c20-8701-8243c69edd42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 패키지 로딩하기 : 패키지를 메모리(RAM)에 올리는 기능\n",
    "# import 패키지이름 as 별명(애칭)\n",
    "import pandas            as pd          # 데이터 읽어오기, 데이터 핸들링, 데이터 분석\n",
    "import matplotlib.pyplot as plt         # 데이터 시각화\n",
    "import seaborn           as sns         # 데이터 시각화\n",
    "import numpy             as np\n",
    "from matplotlib import font_manager, rc # 그래프의 한글 깨짐 처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "05273283-ed46-47a0-935c-e0036c89232f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 한글 폰트 설정\n",
    "font_path = 'C:/Windows/Fonts/malgun.ttf'  # 한글 폰트 경로\n",
    "font_name = font_manager.FontProperties(fname=font_path).get_name()\n",
    "rc('font', family=font_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3fa70fbb-2faa-4e7e-910d-8eac737e0166",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>시도교육청</th>\n",
       "      <th>지역교육청</th>\n",
       "      <th>지역</th>\n",
       "      <th>정보공시학교코드</th>\n",
       "      <th>학교명</th>\n",
       "      <th>학교급코드</th>\n",
       "      <th>설립구분</th>\n",
       "      <th>제외여부</th>\n",
       "      <th>제외사유</th>\n",
       "      <th>교과프로그램수</th>\n",
       "      <th>...</th>\n",
       "      <th>초등돌봄교실저녁돌봄참여학생수</th>\n",
       "      <th>초등돌봄교실방과후학교연계형돌봄운영교실수</th>\n",
       "      <th>초등돌봄교실방과후학교연계형돌봄참여학생수</th>\n",
       "      <th>초등돌봄참여학생중특수교육대상학생수</th>\n",
       "      <th>특수학교종일반학급수</th>\n",
       "      <th>특수학교종일반참여학생수</th>\n",
       "      <th>방과후학교수익자부담금액</th>\n",
       "      <th>방과후학교수익자부담외지원금액</th>\n",
       "      <th>초등돌봄교실수익자부담금액</th>\n",
       "      <th>초등돌봄교실수익자부담외지원금액</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울특별시교육청</td>\n",
       "      <td>교육부</td>\n",
       "      <td>서초구</td>\n",
       "      <td>S000003511</td>\n",
       "      <td>서울교육대학교부설초등학교</td>\n",
       "      <td>2</td>\n",
       "      <td>국립</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>256434000</td>\n",
       "      <td>13830000</td>\n",
       "      <td>14300000</td>\n",
       "      <td>101600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울특별시교육청</td>\n",
       "      <td>교육부</td>\n",
       "      <td>종로구</td>\n",
       "      <td>S000003563</td>\n",
       "      <td>서울대학교사범대학부설초등학교</td>\n",
       "      <td>2</td>\n",
       "      <td>국립</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>165002890</td>\n",
       "      <td>458640</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울특별시교육청</td>\n",
       "      <td>강남서초교육지원청</td>\n",
       "      <td>강남구</td>\n",
       "      <td>S010000738</td>\n",
       "      <td>서울개일초등학교</td>\n",
       "      <td>2</td>\n",
       "      <td>공립</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>222315270</td>\n",
       "      <td>8552780</td>\n",
       "      <td>0</td>\n",
       "      <td>336814450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울특별시교육청</td>\n",
       "      <td>강남서초교육지원청</td>\n",
       "      <td>강남구</td>\n",
       "      <td>S010000739</td>\n",
       "      <td>서울개포초등학교</td>\n",
       "      <td>2</td>\n",
       "      <td>공립</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울특별시교육청</td>\n",
       "      <td>강남서초교육지원청</td>\n",
       "      <td>강남구</td>\n",
       "      <td>S010000741</td>\n",
       "      <td>서울구룡초등학교</td>\n",
       "      <td>2</td>\n",
       "      <td>공립</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>서울특별시교육청</td>\n",
       "      <td>남부교육지원청</td>\n",
       "      <td>구로구</td>\n",
       "      <td>S010006431</td>\n",
       "      <td>서울하늘숲초등학교</td>\n",
       "      <td>2</td>\n",
       "      <td>공립</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
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       "      <td>131697800</td>\n",
       "      <td>34238020</td>\n",
       "      <td>7205000</td>\n",
       "      <td>155564150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>서울특별시교육청</td>\n",
       "      <td>남부교육지원청</td>\n",
       "      <td>구로구</td>\n",
       "      <td>S010006432</td>\n",
       "      <td>서울항동초등학교</td>\n",
       "      <td>2</td>\n",
       "      <td>공립</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236681233</td>\n",
       "      <td>36866310</td>\n",
       "      <td>51076300</td>\n",
       "      <td>75115000</td>\n",
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       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>서울특별시교육청</td>\n",
       "      <td>강동송파교육지원청</td>\n",
       "      <td>송파구</td>\n",
       "      <td>S010006433</td>\n",
       "      <td>서울해누리초등학교</td>\n",
       "      <td>2</td>\n",
       "      <td>공립</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>384202740</td>\n",
       "      <td>18358000</td>\n",
       "      <td>9738000</td>\n",
       "      <td>72961390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>서울특별시교육청</td>\n",
       "      <td>동부교육지원청</td>\n",
       "      <td>중랑구</td>\n",
       "      <td>S010006476</td>\n",
       "      <td>서울양원숲초등학교</td>\n",
       "      <td>2</td>\n",
       "      <td>공립</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163402670</td>\n",
       "      <td>11061040</td>\n",
       "      <td>15664000</td>\n",
       "      <td>209000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>서울특별시교육청</td>\n",
       "      <td>강동송파교육지원청</td>\n",
       "      <td>강동구</td>\n",
       "      <td>S010006543</td>\n",
       "      <td>서울강빛초등학교</td>\n",
       "      <td>2</td>\n",
       "      <td>공립</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>229302230</td>\n",
       "      <td>36128440</td>\n",
       "      <td>28665000</td>\n",
       "      <td>10036000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>604 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        시도교육청      지역교육청   지역    정보공시학교코드              학교명  학교급코드 설립구분 제외여부  \\\n",
       "0    서울특별시교육청        교육부  서초구  S000003511    서울교육대학교부설초등학교      2   국립    N   \n",
       "1    서울특별시교육청        교육부  종로구  S000003563  서울대학교사범대학부설초등학교      2   국립    N   \n",
       "2    서울특별시교육청  강남서초교육지원청  강남구  S010000738         서울개일초등학교      2   공립    N   \n",
       "3    서울특별시교육청  강남서초교육지원청  강남구  S010000739         서울개포초등학교      2   공립    N   \n",
       "4    서울특별시교육청  강남서초교육지원청  강남구  S010000741         서울구룡초등학교      2   공립    N   \n",
       "..        ...        ...  ...         ...              ...    ...  ...  ...   \n",
       "599  서울특별시교육청    남부교육지원청  구로구  S010006431        서울하늘숲초등학교      2   공립    N   \n",
       "600  서울특별시교육청    남부교육지원청  구로구  S010006432         서울항동초등학교      2   공립    N   \n",
       "601  서울특별시교육청  강동송파교육지원청  송파구  S010006433        서울해누리초등학교      2   공립    N   \n",
       "602  서울특별시교육청    동부교육지원청  중랑구  S010006476        서울양원숲초등학교      2   공립    N   \n",
       "603  서울특별시교육청  강동송파교육지원청  강동구  S010006543         서울강빛초등학교      2   공립    N   \n",
       "\n",
       "     제외사유  교과프로그램수  ...  초등돌봄교실저녁돌봄참여학생수  초등돌봄교실방과후학교연계형돌봄운영교실수  \\\n",
       "0     NaN        3  ...                9                      0   \n",
       "1     NaN       20  ...                0                      0   \n",
       "2     NaN       10  ...                1                      0   \n",
       "3     NaN        0  ...                0                      0   \n",
       "4     NaN       14  ...                0                      1   \n",
       "..    ...      ...  ...              ...                    ...   \n",
       "599   NaN       26  ...                6                      1   \n",
       "600   NaN       23  ...                5                      3   \n",
       "601   NaN       27  ...                2                      0   \n",
       "602   NaN       15  ...                4                      0   \n",
       "603   NaN       11  ...                3                      0   \n",
       "\n",
       "     초등돌봄교실방과후학교연계형돌봄참여학생수  초등돌봄참여학생중특수교육대상학생수  특수학교종일반학급수  특수학교종일반참여학생수  \\\n",
       "0                        0                   0           0             0   \n",
       "1                        0                   0           0             0   \n",
       "2                        0                   0           0             0   \n",
       "3                        0                   0           0             0   \n",
       "4                       38                   0           0             0   \n",
       "..                     ...                 ...         ...           ...   \n",
       "599                      3                   0           0             0   \n",
       "600                     66                   0           0             0   \n",
       "601                      0                   0           0             0   \n",
       "602                      0                   0           0             0   \n",
       "603                      0                   1           0             0   \n",
       "\n",
       "     방과후학교수익자부담금액  방과후학교수익자부담외지원금액  초등돌봄교실수익자부담금액  초등돌봄교실수익자부담외지원금액  \n",
       "0       256434000         13830000       14300000         101600000  \n",
       "1       165002890           458640              0                 0  \n",
       "2       222315270          8552780              0         336814450  \n",
       "3               0                0              0                 0  \n",
       "4       238279970         23456050              0         170907550  \n",
       "..            ...              ...            ...               ...  \n",
       "599     131697800         34238020        7205000         155564150  \n",
       "600     236681233         36866310       51076300          75115000  \n",
       "601     384202740         18358000        9738000          72961390  \n",
       "602     163402670         11061040       15664000            209000  \n",
       "603     229302230         36128440       28665000          10036000  \n",
       "\n",
       "[604 rows x 29 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 엑셀 데이터 읽어오기\n",
    "\n",
    "# 엑셀 파일 경로\n",
    "file_path = r'D:/SEN/SEN2023.xlsx'\n",
    "\n",
    "sen2023 = pd.read_excel(file_path)\n",
    "\n",
    "# 데이터프레임 출력\n",
    "sen2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8413d55-6eff-453d-83fa-c274e7870b81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "행의 개수: 604\n",
      "열의 개수: 29\n"
     ]
    }
   ],
   "source": [
    "# 데이터프레임의 행과 열의 개수 출력\n",
    "num_rows, num_cols = sen2023.shape\n",
    "print(\"행의 개수:\", num_rows)\n",
    "print(\"열의 개수:\", num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a6cb3960-92a5-4137-9cd0-29683f4d03ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "열의 이름: Index(['시도교육청', '지역교육청', '지역', '정보공시학교코드', '학교명', '학교급코드', '설립구분', '제외여부',\n",
      "       '제외사유', '교과프로그램수', '교과수강학생수', '특기적성프로그램수', '특기적성수강학생수', '프로그램수합계',\n",
      "       '수강학생수합계', '방과후학교참여학생수', '초등돌봄교실오후돌봄운영교실수', '초등돌봄교실오후돌봄참여학생수',\n",
      "       '초등돌봄교실저녁돌봄운영교실수', '초등돌봄교실저녁돌봄참여학생수', '초등돌봄교실방과후학교연계형돌봄운영교실수',\n",
      "       '초등돌봄교실방과후학교연계형돌봄참여학생수', '초등돌봄참여학생중특수교육대상학생수', '특수학교종일반학급수',\n",
      "       '특수학교종일반참여학생수', '방과후학교수익자부담금액', '방과후학교수익자부담외지원금액', '초등돌봄교실수익자부담금액',\n",
      "       '초등돌봄교실수익자부담외지원금액'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# 데이터의 열의 이름\n",
    "column_names = sen2023.columns\n",
    "print(\"열의 이름:\", column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c31a8e0-ad14-461e-9c85-08cf47a0d9e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "설립구분 열의 빈도:\n",
      "설립구분\n",
      "공립    564\n",
      "사립     38\n",
      "국립      2\n",
      "Name: count, dtype: int64\n",
      "\n",
      "설립구분 열의 백분율:\n",
      "설립구분\n",
      "공립    93.377483\n",
      "사립     6.291391\n",
      "국립     0.331126\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 설립구분의 빈도와 백분율\n",
    "\n",
    "# 빈도\n",
    "establishment_counts = sen2023['설립구분'].value_counts()\n",
    "\n",
    "# 백분율\n",
    "establishment_percentage = sen2023['설립구분'].value_counts(normalize=True) * 100\n",
    "\n",
    "# 결과 출력\n",
    "print(\"설립구분 열의 빈도:\")\n",
    "print(establishment_counts)\n",
    "print(\"\\n설립구분 열의 백분율:\")\n",
    "print(establishment_percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e57f17cc-7764-4a84-95b1-67de6d005b56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 막대그래프 작성\n",
    "plt.figure(figsize=(8, 6))\n",
    "establishment_counts.plot(kind = 'bar', color = 'skyblue')\n",
    "plt.title('설립구분의 현황')\n",
    "plt.xlabel('설립구분')\n",
    "plt.ylabel('빈도')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.grid(axis = 'y', linestyle = '--', alpha = 0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d926b7f5-9c2c-4c96-9427-271dc74f987e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\buillee\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 히스토그램 그리기\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(data  = sen2023,\n",
    "             x     = \"교과프로그램수\",\n",
    "             color = 'purple', \n",
    "             bins  = 20)\n",
    "plt.title('교과프로그램수 히스토그램')\n",
    "plt.xlabel('교과프로그램수')\n",
    "plt.ylabel('빈도')\n",
    "plt.grid(axis = 'y', linestyle = '--', alpha = 0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "51d1045b-3775-41e4-922b-9831d409fa9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 상자그림(Boxplot) 그리기\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(data  = sen2023,\n",
    "            y     = \"교과프로그램수\",\n",
    "            color = 'purple')\n",
    "plt.title('교과프로그램수 상자그림')\n",
    "plt.ylabel('교과프로그램수')\n",
    "plt.grid(axis = 'y', linestyle = '--', alpha = 0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ce47b19c-e343-4824-a10a-aee94c18ee92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 바이올린 그래프(Violin plot) 그리기\n",
    "plt.figure(figsize = (8, 6))\n",
    "sns.violinplot(data  = sen2023,\n",
    "               y     = \"교과프로그램수\", \n",
    "               color = 'purple')\n",
    "plt.title('교과프로그램수 바이올린 그래프')\n",
    "plt.ylabel('교과프로그램수')\n",
    "plt.grid(axis = 'y', linestyle = '--', alpha = 0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0b8acf6f-3f7f-42bb-8d52-2a1a2a2b471a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "평균: 13.89569536423841\n",
      "중앙값: 11.0\n",
      "표준편차: 14.433704852696241\n",
      "최솟값: 0\n",
      "최댓값: 209\n"
     ]
    }
   ],
   "source": [
    "mean          = np.mean(sen2023[\"교과프로그램수\"])   # 평균\n",
    "median        = np.median(sen2023[\"교과프로그램수\"]) # 중위수 = 중앙값\n",
    "std_deviation = np.std(sen2023[\"교과프로그램수\"])    # 표준편차\n",
    "min_value     = np.min(sen2023[\"교과프로그램수\"])    # 최소값\n",
    "max_value     = np.max(sen2023[\"교과프로그램수\"])    # 최대값\n",
    "\n",
    "# 결과 출력\n",
    "print(\"평균:\", mean)\n",
    "print(\"중앙값:\", median)\n",
    "print(\"표준편차:\", std_deviation)\n",
    "print(\"최솟값:\", min_value)\n",
    "print(\"최댓값:\", max_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "006d1858-8ce0-4219-9479-84cbb3633b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    604.000000\n",
       "mean      13.895695\n",
       "std       14.445668\n",
       "min        0.000000\n",
       "25%        6.000000\n",
       "50%       11.000000\n",
       "75%       17.000000\n",
       "max      209.000000\n",
       "Name: 교과프로그램수, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sen2023[\"교과프로그램수\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0ebaf341-247e-4fb0-b8e8-8b7642901cf0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\buillee\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\buillee\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\buillee\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 범주별로 데이터 분할\n",
    "np_public   = sen2023[sen2023['설립구분'] == '공립']['교과프로그램수']\n",
    "np_national = sen2023[sen2023['설립구분'] == '국립']['교과프로그램수']\n",
    "np_private  = sen2023[sen2023['설립구분'] == '사립']['교과프로그램수']\n",
    "\n",
    "# 히스토그램 그리기\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(np_public, color   = 'skyblue', label='공립', kde = True)\n",
    "sns.histplot(np_national, color = 'orange',  label='국립', kde = True)\n",
    "sns.histplot(np_private, color  = 'green',   label='사립', kde = True)\n",
    "plt.title('설립구분별 교과프로그램수 히스토그램')\n",
    "plt.xlabel('교과프로그램수')\n",
    "plt.ylabel('빈도')\n",
    "plt.legend()\n",
    "plt.grid(axis = 'y', linestyle = '--', alpha = 0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f195655e-7360-4e70-9906-fbb0168d02da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\buillee\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\buillee\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\buillee\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
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AWrgGnVNqwYIFDbk7AABiRuVE5/UNpegpBQAAAPg0aCg1fPhwSWKuKQBAs9PQPaWSWmfISwdjAAAAtGAhDd/7yU9+UuuVhn7zm9/o6quvliQNHDhQ+/fvr3/rAACIEZWhVH3nlIqzSDJNWSwWlTfsd0MAAABAkxJSKHXffffVuk2PHj38PzO3FACguXGbDTN8zzAMWUyPvIZNTkIpAAAAtGAhhVI//elP67TT2npVAQDQ1LgaaPieJFm8HnktNjlNa733BQAAADRVIV997+OPPw66vE+fPurQoUODNQgAgFhUOdF5fYfv+fbhkSR6SgEAAKBFCzmUevDBByVJW7duVadOnZSWlqa9e/fq3nvv1e9+97tGayAAALGgoSY6lySr1y2JUAoAAAAtW8ih1AcffCDJN6H5tddeq1GjRulPf/pTozUMAIBY4jYbdvieJIbvAQAAoEXjK1oAAELA8D0AAACgYYXcU6omhw4d0q9+9SsZhiHTNJWXl9cQuwUAIGY05PA9S+XwPZNQCgAAAC1XnSc6P3z4sDZv3ixJ2r17tzp16qTWrVvrvvvua5QGAgAQC46FUma993WspxTD9wAAANBy1Xmic0l6++239fbbb0uSLrnkEtntdv30pz9t+NYBABAjjg3f89R7X1Yvw/cAAACAOk90DgBAS+P2mvI2Qk8ptyxye03ZLEa99wkAAAA0NXxFCwBALco9x4IoQ/WfU8owvXI5yyRJxe767w8AAABoigilAACohdPrC6WcxQ41RJ8mQ5LjyGFJUrGLUAoAAAAtE6EUAAC1cFb0lCorLmqwfRZVhFIOQikAAAC0UIRSAADUwunxBUfOBgylHEdyJDF8DwAAAC0XoRQAALUorxi+V+YobLB9FvmH79V/4nQAAACgKSKUAgCgFpXD95yOhuwpVRFK0VMKAAAALRShFAAAtSj3zynlaLB9FuUy0TkAAABaNkIpAABq4e8p1QgTndNTCgAAAC0VoRQAALVwehs+lHJw9T0AAAC0cIRSAADUwj98rwHnlCqqvPqeyyvTZLJzAAAAtDyEUgAA1KIxhu85KkIpt3ns6n4AAABASxLzodTUqVPVt29f//1NmzZp+PDhyszMVL9+/bRs2bIotg4A0BJUDt8rcxQ22D5dZSWyyTd0r9hFKAUAAICWJ6ZDqb179+o///mP/35RUZEmTJigmTNnas+ePXruued06aWX6uDBg1FsJQCguWuMq+9Jkr0ylGKycwAAALRAMR1K3X777br66qv9919//XUNGTJE48aNkySNGjVKo0eP1vz586PVRABAC+D0+EIjZwPOKSVJdnkk+eaVAgAAAFqamA2lFi1apLy8PE2cONG/bN26dRo5cmTAdkOHDtXmzZsj3DoAQEvSGHNKSZLd8IVRDnpKAQAAoAWyRbsBwezfv1833XSTli1bpgMHDgQsHzt2bMC27du31/r166vdl9PplNPp9N8vLGy4+UAAALGjMet95UTkZQ0cSsX755QilAKAUPDZHgCal5jrKeX1enX55Zfr7rvvVp8+fQLWeTyeKpfN9ng8Mgyj2v098sgjSktL89+6devWKO0GAERXY9b7Ru8pRSgFACHhsz0ANC8xF0rNmDFDqampuummm6qsS09PV25ubsCynJwcdezYsdr9TZ8+XQUFBf5bVlZWg7cZABB9jVXv3V5TFZmUyooa9hv5xIo5pYoIpQAgJHy2B4DmJeaG7/3zn/9UcXGx2rRpI0lyu90qLS1V69atNX36dK1du1Z33HGHf/s1a9boV7/6VbX7s9vtstvtjd5uAEB0NVa9rxy6J0nOkoa9+l5CRShVWE4oBQCh4LM9ADQvMddT6sCBAyosLNTRo0d19OhRLVq0SCeffLKOHj2qK664QitXrtSqVaskSUuWLNHWrVs1adKkKLcaANBcVQ7ds8or09uw4VGiURlKVR2eDgAAADR3MddTqiZdu3bVvHnzdOONNyovL0+9e/fWu+++q+Tk5Gg3DQDQTFWGUjY1fGhU2VPKbUplHlOJturnSAQAAACam5gPpcaMGaOtW7f6759zzjkB9wEAaEzljRhKWQ0p2Wao2G2qoNyrRFvMdWAGAAAAGg2ffgEAqIGzYsieTY0z71OreKsk3xA+AAAAoCUhlAIAoAb+nlJG48z51Cre91ZcyBX4AAAA0MLE/PA9AACiqXJOqbjG6ikV5wuldmUfkv1gkSQpPT1dXbp0aZTjAQAAALGCnlIAANSg3Nt4c0pJklnqkCS9s3SFBg0apEGDBqlv377Kzs5ulOMBAAAAsYJQCgCAGjTm1fckyVvi6x014Mej9dLKTzXj+VfkcDiUl5fXKMcDAAAAYgXD9wAAqMGxUKpxhu8lGr4Jzm1JqerVuXOjHAMAAACIRfSUAgCgBs5Gnug8SW5Jktdik8dsnGMAAAAAsYhQCgCAGjT2nFJxMlVaVCBJcroJpQAAANByMHwPAIBqZGdnK69QkuzKPdDwE49v375dknSkLE1d+52uUg+hFAAAAFoOekoBABBEdna2+vbtqy+/+U6S9OjMhyVJJY6Seu87P+ewZBiaOHGiJk6cqCNZP0iSyjyNM28VAAAAEIvoKQUAQBB5eXlyOBzq0vNkSdJ5l16ubWtWqMxZVu99O4oKJdPUXY8/q1JHobZXhlIM3wMAAEALQk8pAABqYI23S5LaZKQ3+L47ZfZQh67dlLdvtySpjOF7AAAAaEEIpQAAqIHXqHir9Lgb7RiVw/eYUwoAAAAtCaEUAADVsNriJH8o5Wm041SGUk6P2UjX+AMAAABiD6EUAADVsCenHLvTiD2linIPSV5f6OWxxDXacQAAAIBYQigFAEA17MmpkiSLIamR+zAZLt8E6m4roRQAAABaBkIpAACqkZDSSpJkNRr/WEZ5ZSgV3/gHAwAAAGIAoRQAANWwp/h6SlmNxk+ljPJSSZLbQigFAACAloFQCgCAaiRUhFK2CLxbGs4SSfSUAgAAQMtBKAUAQDWODd+LRE+pY8P3LFZrox8PAAAAiDZCKQAAqlEZStkiMKeU3E7fhOqGofSu3SNwQAAAACC6CKUAAKiGv6eUJQI9pSQlVcyo3qHXqY1+PAAAACDaCKUAAKhGQmoEe0pJSqqYvKpj776ROSAAAAAQRYRSAABUI5I9pSQpKc73tty576CIHA8AAACIJkIpAACqcWxOqciEUilxvuMQSgEAAKAlIJQCAKAax66+F5njJdsskmmqdccucpq8RQMAAKB54xMvAADVSEhJlSTZIjR8z2YxZPW6JEkFZlxEjgkAAABEC6EUAADViHRPKUmKczslSQUilAIAAEDzRigFAEA1/HNKRainlCTFeSpCKXpKAQAAoJkjlAIAoBpR6SlFKAUAAIAWglAKAIAgTFOyJ6dIimxPKVvF8L0yWVXi8kbsuAAAAECkEUoBABCEW8eCqEj2lLLIVM7u7yVJB0rckTswAAAAEGGEUgAABOGqfIs0vbIYEUylJO375gtJhFIAAABo3gilAAAIorKnlMWM/BC6rK99odT+ElfEjw0AAABECqEUAABBVPaUMqIRSm35XJJ0oNgt0zQjfnwAAAAgEgilAAAIwm1Gr6fUge3fyCJTpR5TR8uZ7BwAAADNE6EUAABBRLOnlMftUiv5hu7tL2YIHwAAAJonQikAAIKonFMqGqGUJLU2KkIpJjsHAABAM0UoBQBAEJU9paIxfE+Syg7ukSTtzClUdnZ2VNoAAAAANCZCKQAAgqicUyrSPaXycw5LhqE7pvxWkpRbbqr/wEEEUwAAAGh2CKUAAAiicvhepHtKOYoKJdPUNbf9XobXI1u8Xa26dFdeXl5E2wEAAAA0NkIpAACCiOZE55LUKbOHWifGSZK6DRgclTYAAAAAjYlQCgCAIKI90bkkpcT53qa7DTgjam0AAAAAGguhFAAAQbjM6E50LkmplaHUQHpKAQAAoPkhlAIAIIhY6ClVGUpldOuh8oqJ1wEAAIDmglAKAIAg3KrsKeWJWhtsFkNWT7kk6agZH7V2AAAAAI2BUAoAgCBcMdBTSpLi3E5J0lEzLqrtAAAAABoaoRQAACcwTVNWmfK4XFGdU0qS4jxlkqSjIpQCAABA80IoBQDACQzD0M9sh3XfsM5R7ykV7+8pFS/TNKPaFgAAAKAhxWwotWrVKo0cOVK9e/dWr169NHv2bP+6TZs2afjw4crMzFS/fv20bNmyKLYUANCcRXt6cZvHKZezTC5ZlO+MbkAGAAAANCRbtBtQnddff10vvPCCTj31VO3atUtnn322Tj75ZI0cOVITJkzQnDlzNG7cOH388ce64IILtHXrVnXs2DHazQYAoEEZkvZv/UqZpw3V/hKXSo8cVF5enn99enq6unTpEr0GAgAAAGGK2VDq+eef9//cs2dPXXbZZVq1apX27t2rIUOGaNy4cZKkUaNGafTo0Zo/f76mTZsWreYCANBo9n2zSZmnDdWOwwX67ZC+cjgc/nUpKSnaunUrwRQAAACanJgdvneinJwcpaWlad26dRo5cmTAuqFDh2rz5s3RaRgAAI0sa8vnkqSDpR45HA7NeP4VvbTyU814/hU5HI6AnlMAAABAU9EkQqkNGzZo0aJFuvzyy7V//3516NAhYH379u115MiRoI91Op0qLCwMuAEAmp/mXO/3fv2FJKlQcbLF29WtZ2/1OnWAuvXsHeWWAUBkNedaDwAtUcyHUgsWLNCFF16ouXPnqkePHvJ4PFWuPuTxeGQYwaeifeSRR5SWlua/devWLRLNBgBEWHOu9/nZexQvj0wZ6tRnQLSbAwBR05xrPQC0RDEbSnk8Ht1000168MEHtWzZMk2YMEGSb0LX3NzcgG1zcnKqneR8+vTpKigo8N+ysrIave0AgMhr7vW+teGSJJ008MwotwQAoqe513oAaGlidqLzadOmaefOndqwYYNSUlL8ywcPHqy1a9fqjjvu8C9bs2aNfvWrXwXdj91ul91ub/T2AgCiq7nX+9aGS4fNBJ00iFAKQMvV3Gs9ALQ0MdlTqrS0VM8995xefvnlgEBKkq644gqtXLlSq1atkiQtWbJEW7du1aRJk6LRVAAAIiJd5ZKkzNOGyqxlWwAAAKApiMmeUj/88IO8Xq+GDRsWsLxXr15auXKl5s2bpxtvvFF5eXnq3bu33n33XSUnJ0eptQAANL40wyVDptI6dJb36J5oNwcAAACot5gMpfr16yev11vt+nPOOUdbt26NYIsAAIgum2GqlVwqULzKbQnRbg4AAABQbzE5fA8AAFTVpmKycxehFAAAAJoBQikAAJqINoZvXil6SgEAAKA5iMnhewAAoKrKUMptjZfHy3TnANBQ9u7dq9zc3Gg3o0Zt27bVSSedFO1mAECDIpQCAKCJSDS8yj+QpTaduqnIVf3ciwCA0O3du1ennnqqSkpKot2UGiUlJem7774jmALQrBBKAQDQhOz98jNCKQBoQLm5uSopKdF9z/xbmb37RLs5Qe35fptm3nyNcnNzCaUANCuEUgAANCF7vvxMp42/RIXlXjGzFAA0nMzefdRn0OnRbgYAtChMdA4AQBOy58sNkqQil1fMKgUAAICmjFAKAIAm5OCOb2SYXnlMyW2Jj3ZzAAAAgLAxfA8AgCZg+/btkiSvx6M4d5nK45LksjGADwAAAE0XoRQAADEsP+ewZBiaOHHisYUlDiktSeWEUgAAAGjCGL4HAEAMcxQVSqapux5/Vjc9+IgkyVucL0n0lAIAAECTRk8pAACagE6ZPVRckCdJMsqKJUkea5xS0ttFs1kAAABA2OgpBQBAE2N4PUqyGZKkk04bEuXWAAAAAOEhlAIAoAlKjfO9hWcSSgEAAKCJIpQCAKAJahXvews/adDQKLcEAAAACA9zSgEA0ARV9pTq2u80ecy8KLcGAFAT0zRV6jFV6jbl9Jhyerwq90oerym3KXlNU5JkGJIhQzZDirMYslkM2a2GyuOT1faknvJG+XkAQEMjlAIAoAlKsBqyeD2yxdtVoLhoNwcAcByP11RBuVcF5V45XF453F55zVAfHWTD1l115zvrtdI0tWVrvjom2dQpKU5dk23KSLDKMIyGbD4ARAyhFAAATZBhGIpzl8oZn6J8Mz7azQGAFs/pMXWkzKMjTo+Kyr1VoiWLpESboQSrIbvVIrvVkNWQbBZDlopMyTR9kZTba8rlNeXySk6PV0cdJSp1exSfmKxDpR4dKvXoyyNOSVKyzVBmarwyU+OUmRKn1nZrJJ82ANQLoRQAAE1UvNtJKAUAUeT2msop8yi31KNCV+DgOrvVUOt4i1rFW5RssyjJZoTdo2lb1nf6/fiz9Mnnm5XRs68Olbi1v9it7GKXit2mvs136tt8X0jVNsGqU9LidXLreHVMtNGLCkBMI5QCAKCJinOXSpLyzHiZpskfHgAQIQ6XVwdL3Mop8wQMy0uNs6htglVt7BYlWMMPoaqTKI/6trarb2u7JF8oll3s0p4il/Y4XNpf7FZumUe5ZaVae6hUqXEWnZwWr75t7OqWTEAFIPYQSgEA0ETFeZxylZVKCYnKc3qUkcDbOgA0FtM0le/0KrvYHdArKtFqqEOSVW0TrLJbI3txc5ulcuier8dsmdurnYXl2l5Qrl2F5SpyefVFbpm+yC1TapxFp7axq18buzokMg8VgNjAp1cAAJooQ1LW11+o55kjtc/hJpQCgEZgmqZyyzzKKnar1F1xlTxJGQlWdUyyqlWcJWYCngSbRf3TE9Q/PUFur6ndRS5tO+rU9qO+gGrD4VJtOFyqdLtV/SoCqvQE5qACED18egUAoAnbvXm9ep45UlnFLp3WNiHazQGAZsM0TeU5vdrrcKmkIoyyGqq48p1NdmtsBFHVsVkM9U6LV++0eJ3TzdTOwnJ9m+/UzoJy5Tk9+uRgiT45WKKOSTb1a2PXqa3jlRpPQAUgsgilAABowvZsXi9J2udwRbklANA8mKapo+Ve7SnyTSIu+cKoLsm+MMpmiV4Y9d1339Xr8ZmSusjQYSXpgJKUpwQdLHHrYIlbq/Y51EZOdVKxOqhUcfLWur/jtW3bVieddFK92geg5SGUAgCgCdvz1WeSfH9AFbk8So3jW24ACFexy6sfilwqKPcFMhZD6pxkU5fk6IZRRw4flAxDkydPbtD9JrfO0MCfXaBB4y9Rjx8NV74SlK8EfeUq1/a1q/TV+2/p24+WylVWUuu+kpKS9N133xFMAagTQikAAJowp6NIreRWoeKU7XCrbxtCKQCoq5T0dipK7aDNR5ySfHNGdUqyqWuKTXFRDKMqOQoKJNPUzQ//VacNGdYox/Dk7pQzoZWc9lQpLkH9Ro9Xv9HjJdMru9Oh+LJCxZcXK9jZ2PP9Ns28+Rrl5uYSSgGoE0IpAACauDZGuQrNOGUVu9S3jT3azQGAJsPtNfWDUnXnO+vlTEyV5JvAvHuKTQm2yF5JLxRdevRSn0GnN/pxStxe5ZR6lFvmUZnH4gurElrJZvjOT0aCVWnxFlliZIJ3AE0XoRQAAE1culGuPWYy80oBQB19sL9YO4w2SkiRbK5SndohTa2Y7FtJNosyUy06KcUmh8tUTplbuWUeubzSoVKPDpV6ZDWkNnarMuwWeQmnAIQp9uJ/AABQJ+lGuSTpcKlHTk/dJqYFgJZsaPtEJZku/ff+G5WWv5dA6gSGYSg13qKereI1pF2C+reJV8dEq+IskseUcss82lbgUl7b3rry6deUpRQVlHui3WwATQg9pQAAaOISDK/S4i0qKPdqn8OtXmnx0W4SADQJafFWjdQBTVu8QMYt06LdnJhmGIZa261qbbeqp2nK4TJ1xOnRkYohfn3P/pm+k/TdN/lKNl1qq1JlqExtVKZYifq4QiAQewilAABoBrqnxunLI079UFROKAUAdcDAs7rz9aDy9aLKTLFp7SertXTpMp0y8qc6aeCZKrbFqVhx2qNWKi8t0a6Na7R97SrtWLdKuXt3Ra3dXCEQiD2EUgAANAM9UuP15RGndhcxrxQAIHIMw1Dx4QP68MWnNKBHN7XrmiFXfJLK45Plik9WfGKS+p79M/U9+2eSJIvHpThXieLKfTer1x2RdnKFQCA2EUoBANAMZKbGSfLN71Hk8ig1LlYGSwAAWoouPXrp1IGD/PdN01SJ21R+uUdHnV4VlnvltcbJaU2TMyFNkmS3GkqLt1TcrLJb6bsGtCSEUgAANAOJNos6Jtl0sMSt3YUuDcwglAIARJdhGEqOM5QcZ1HXZMljmioq96qg4uZweeX0mDpc6tHhUo8klxIrQyq7VWnxFsVZCKmA5oxQCgCAZqJnapwOlri1o6BcAzMSot0cAAACWI+bLF2SPF5Tha6KkMrpkcNtqtRjqrTUo4Olvqv4Jdl8IVXreKtaxVtkI6QCmhVCKQAAmrjt27dLkiymTVI77SosV7nHVDxDIAAAMcxqMdTGblUbu1VKjZPba1b0ovKooNyrErdZcfPoQIkvpEqJM5QW7+tF1SrOIishFdCkEUoBANBE5ecclgxDEydO9C/7/bsb1aZLpnYWluvUNvYotg4AgLqxWQxlJFiVkeDrSeWqDKmcHh0t96rMY8rhMuVwuZVd7LtyYmpcxXxUdotS4yyyGIRUQFNCKAUAQBPlKCqUTFN3Pf6s+p8xWFm7vteaZQs15upbtfWok1AKANCkxVkMtU2wqm1FSOX0mP5eVAVOr5wVw/8KXV5lFUsWSanxFrWumDQ9Jc6QQUgFxDRCKQAAmrhOmT3U69QBkqQtf31MY66+Vd8XlKvY5VVynCUibcjOzlZeXp7/fnp6urp06RKRYwMAWga71VD7RJvaJ/qu7Of0mDrqnzjdI5dX/knUJbeshvxX9XNb46PdfABBEEoBANCM7N/6ldJUrgIzXl8eKdOPOyY1+jGzs7PVt29fORwO/7KUlBRt3bqVYAoA0CgMw1CCzVBHm0Udk3whVanHVIHTq6MVvak8ppTn9CrP6ZUyemj60i3aolTZjpSpe2qcWsVzpVog2gilAABoZrpbSvSlN16fHS7V4HYJslsbt7dUXl6eHA6HZjz/irr17K2sXd/rgWsnKy8vj1AKABARhmEoyWYoyWZRp2SbTNOUw23656MqcLrVql1HHZB0YK/vS5Q2dou6p8YrMzVOmSlxSrRFpncxgGMIpQAAaGY6G6XKsmcoz+nR2oOl+kmX5JAeV9MQvFCG53Xr2ds/jBAAgGgyDEOpcYZS4yzqKmnrV1v16P1/0J+fmyNncoYOlLiV7/Qq31mmTbllkqQOiVZ1T41X99Q4dU2JUxxX9gMaHaEUAADNjMWQftIlSW/uKtL6w6XKTI1Tz1Y1z6VR0xA8SQzPAwA0aYZM7frsE52sAp3Rp5fKPF5lOVzaXeTSniKXcss8OlTq0aHSUq0/XCqrIXVOtvlDqk5JNq7sBzQCQikAAJqhk9PsGpRRrq+OOPXWrkL9rGuKBmbYq/1AXWUIXlaWFr71llbnuFUkm659eanade0ui9Uqr8etnH17tDTH1KnWEnlNPk4AAJqWBKtFJ6fZdXKa70q1DpdXe4rK/SFVocurLIdbWQ63Vh+Q4i2GuqUcC6naJli5sh/QAPgUCQBAM7N9+3ZJUhdT2m+mKVdJei/LoY/25qurtUwdjTKlyK2MjMAheK3adVT6qWeoJK2D4lr31MRBY7Tb9K1r3+MUSZIpybBY1b7HKTpsSocPlEhqpxvmLJHTlhjhZwoAQMNIibOof3qC+qcnyDRN5Tu92uM4FlKVeUztLHRpZ6FLkpRoM9Q5yaauyXHqnGxTp6Q4xVsJqYC6IpQCAKCZyM85LBmGJk6c6F9msVo18vLrNeZ3t0lpbbTdG6ftSlVR7mEd+OhjTfjZWNkTk7Tb3VbTl25RkSS5vJJhaO+WjRo9qK9aGW7dPOUq3fP4bJ3Uq7f27vxef/nDbXryXy+qPKWddhU6ddKgIcqXtONouSQ+lAMAmi7DMJSeYFV6QqJ+1DZRpmnqUKnH35Mqy+FSqTswpDIktU+0qktynLok29Q5OU6t4y30pgJqQSgFAEAz4SgqlExTdz3+rPqfMVgbP/5Azz40XcMG9Vf+J//Texs3acJNv1d8Rieltm2v1LN+pm2lkkqdkuLk9Xpl95arS5tUFWft0PQrz9UNX30lSdq1cY3ivOVKslkU5y3Xzg0fq4elRAN7tdLGr77RQ/9ZqJG/vlaHyzyypXZRm84nRfVcAADQUAzDUMckmzom2TSsg+TxmjpU6lZ2sVvZxS5lF7tV5PJWzEnl0Re5vsfZrYY6JNrUIdGqjkk2dUi0KT3BytxUwHEIpQAAaGY6ZfZQr1MHKGvX9/77xQV52rjwVf36iit0Rv+e2r7je738z3/o9t/fo04dO8hxKFu/HDtCz7y5WF26DtBO0yPp2FDAYCrX7dmxXYueuFcTJvxCRWld5LLZdeN/lirPbPznCgBAXXz33XcNti+rpJMqbmWy6qjsOqp4HZVdRYqX0yPtdbi01+HyP8ZiepUil5LlqvjXrRS5lCi3DElt27bVSSfxxQ5aDkIpAABaGKthKN7j1NrX/6Xnpt+sgR17aEtOmYqPHvFvE2woYImjpNp1kuQ+mqfTevbU5gOFSmnTVp96TLU5UqZBGQmReWIAAFTjyOGDkmFo8uTJETme1Ran9j37qHPfgercd5A69x2kTqf0lz0pWYWyq1D2gO1dzjLl7P5eeSs26VcXnKfMdm3U2m5V63iLUuIYBojmq0mGUqWlpZo2bZqWLl0qj8ejX//61/rLX/4ii8US7aYBANAsHD8UsNRRqGcfmq4yZ1mVdccPEyxzlslutSijKFsrNn6tgT+7QEv2OrSnyKWfdklWUhzv0wCA6HAUFEimqZsf/qtOGzIsKm0wHfvkKY2Tx2aXxxovj80utzVeHlu84uwJ6txngDr3GaCvS6Wv9zr8j7MaUlq8Va3tFrWOtyqtIqhKibMoNc6qlDgLk6yjyWqSodSdd94pr9ernTt3qri4WOPGjdMzzzyjW2+9NWpt2rt3r3Jzc+v0GKfTKbvdXvuGJ6BLJwCgoVQOwatumF7l0L/q1h0/TLCSIVOv3zNFl/x8p3aYqfom36mt+aXqYpSqo1Gm3hkpOqlrl6D7BACgMXXp0Ut9Bp0e7WYEME1TTo+pHd/v1PyX/60pt96puFZtdNTpUUG5Vx5TynN6lOf0SHIF3YfdalSEVL6wKslmUaLV8P1rM5Rosyip4t8Eq8G8VjEgnAwhXKYkrySvDHllyKy4KI1ZcfN9eqv6c/s2rdWvR7dGbVuTC6UcDodefvll7d27VzabTWlpafq///s/zZgxI2qh1LxvDuqj9V/JNCyyWq0yrFZZrDZZKv612o79bLH6TrlpemWacZJpyvR6Zco89rPpeymYXq+8Xo88rnJ53G553S553G4VHtqgv0w+j2AKABC26obgVQ7Rqy/TNHWK1aFeqXa9tG6rOvUdpL1msvaayVqzr0y/cO3XGT06N8ixAABoygzDUILNUHx5sT5++RnNuvVqndG7uyTJa5oqLPfqaLkvoDrq9Kiw3Ksil1cOl1dFLo9cXsnpMeX0eHSkzBPSMROtvmParRbZLYbirYbslbfj7sdX/GwzDNkshuIshmyGZLMYFTcpzjBkMcQQw2p4TVNur+QxTblNUx6vlJW9Xxf9cpLcXlM2u11x8Qmy2e2yxdtlsycoLt4ua3y8bPEJiqtcHp8gW8UyW3y8rPHxiotP8G/nW2c/7ha4PBxb//e+Zlw8qlGzhyYXSn3++efq0aOHMjIy/MuGDh2qr7/+Wm63WzZb5J/SYaepzB8Nj9jxDu3cqtzcXEIpAEDYahqC15CsRUf0t8t/qofnLVFqZh+VWOyKsyfIW3RIEqEUAAA1sRiGb24pu7XabZyeyoDK96/D5VWJ21Sp26sSt1elblOlHt8yp8fXAaLUY6rUU9l/pv4MSTaLL6yyyhdS+W6GrIZkGL45Lf3LFbi8Ms8yKm6SL+Q69vMJ643Knw3/ekOVPX0k05Sv40flMvP4XkG+9Qq4bx77+bj1XlPyyvT9a/oCJq/pC5h8644tO3595XKPt7oznKhrX1xct5PckMzjzoZZ2TfKv1IyJY/HLUd+XqNnD00ulNq/f786dOgQsKx9+/Zyu90qLCxUenp6wDqn0ymn0+m/X1BQIEkqLCxssDZ1dWTpvgf/qAsn/04ZHTr6fi3M4zq+Hf9zhW++2KBFr76sX157s3r1PVXHfvWOY1R2qfP9tpoydOTwQS361zO6+LGHGvQ5AECsSE1NDeubtoau9w6Hby6HHd98pdLiYmXt3CFJ2r3tWyUm2APulxUXVbuutvt1eWxjHMdZVqrS4mKVV4RRDXHcfbt3SpI2b97sP5+lh/cpI8muw7t36h+PztToN+bxPga0YLFS66Vj9X77ls0qLS4Oez+Nac9O3xDrH777RsmJiVFuTVWx3j6pabQxa5fv/fTzzz/3vy7DZZOUWnE7nleSWxaVyyqXLPLIkEcWuWWRW4bcFfc9MiqW+ZZXDvnyyJDXMOQxDXkN5oqsK8M0ZZEp0+NWfl6uUlu1UpwtTobpCwgr8wPDNCt+9sqoSNWMiuWqWK6KbQzT9P98bJn3uGXyP87/bwhtzdq1Q28+NE23jv2oXjW21npvNjH/+c9/zJ/85CcBy0pLS01JZl5eXpXtH3zwwcoIkBs3bty4NYFbQUFBWO8P1Htu3Lhxazo3aj03bty4tYxbbfXeMM3KjmlNw5IlS3TPPffoq6++8i/LyspSnz595HA4qlyB78RvU7xer/Ly8pSRkRH2mNfCwkJ169ZNWVlZatWqVXhPpAXivIWH8xYezlt4YuG8NdS35/Wt97FwLpoizlv4OHfh4byFJ9rnLVZqvRT9c9FUcd7Cw3kLD+ctfNE+d7XV+yY3fO+MM87Qtm3blJ+frzZt2kiS1qxZo6FDh1YJpCTJbrdXucJd69atG6QtrVq14hciDJy38HDewsN5C09TPG+NVe+b4rmIBZy38HHuwsN5C09TO298to89nLfwcN7Cw3kLX6yeuyY3CLRjx44aP368/u///k9ut1u5ubn685//rNtuuy3aTQMAAAAAAECImlwoJUn//ve/tX//fnXq1ElnnnmmrrvuOl100UXRbhYAAAAAAABC1OSG70lS27ZttXDhwqgd326368EHH6zSdRg147yFh/MWHs5beDhvx3AuwsN5Cx/nLjyct/Bw3o7hXISH8xYezlt4OG/hi/Vz1+QmOgcAAAAAAEDT1ySH7wEAAAAAAKBpI5QCAAAAAABAxBFK1UFpaamuu+46ZWZmqmvXrrr77rvl9Xqj3ayYM2vWLKWlpal79+7+286dOyVJmzZt0vDhw5WZmal+/fpp2bJlUW5t9Jmmqblz52r48OEBy2s7V0899ZR69+6tLl266KKLLlJubm4kmx111Z230047TV26dPG/9i6++OKA9S39vK1atUojR45U79691atXL82ePdu/jtfcMdT70FDv64Z6Hx7qfd1R60NDrQ8Ntb5uqPXhodaHp1nUexMhu+GGG8xrrrnGdLlc5tGjR80zzzzTfPrpp6PdrJhz//33mw888ECV5YWFhWaXLl3M5cuXm6Zpmh999JGZlpZmHjhwINJNjBnvvfeeOWDAALNnz55mnz59/MtrO1fz5883f/SjH5lHjhwx3W63OXXqVPPiiy+OynOIhurOm2maZrdu3cxdu3YFfVxLP2+maZpTpkwxv/32W9M0TXPnzp1m586dzffee4/X3Amo96Gh3oeOeh8e6n14qPWhodaHhlofOmp9eKj14WsO9Z5QKkRFRUVmUlKSmZub61/21ltvmaeffnoUWxWbbrrpJvPJJ5+ssvyf//ynedFFFwUsu+CCC8ynnnoqUk2LOQsWLDD/97//mR988EFAAa7tXI0YMcJ85513/OtycnLMuLg488iRI5FpeJRVd95M0zSTk5PNvLy8oI9r6ectmNtvv928++67ec0dh3ofOup96Kj34aHeNwxqfVXU+tBR60NHrQ8Ptb7hNMV6z/C9EH3++efq0aOHMjIy/MuGDh2qr7/+Wm63O4otiz35+flq3bp1leXr1q3TyJEjA5YNHTpUmzdvjkzDYtDEiRM1YcKEKstrOldut1sbN24MWN+2bVtlZmZqy5Ytjd7mWFDdeXO5XCopKVFaWlqVdZy34HJycpSWlsZr7jjU+9BR70NHvQ8P9b5hUOurotaHjlofOmp9eKj1Dacp1ntCqRDt379fHTp0CFjWvn17ud1uFRYWRqlVsSk/P1/33nuvunXrprFjx2rlypWSqj+HR44ciUYzY1pN5yonJ0cej0dt27YNur4ly8vLk2EY6tWrl0455RRNmTJFBw8elCTOWxAbNmzQokWLdPnll/OaOw71PnTU+/rjdy881PvQUeuDo9aHjlpff/zuhYdaXzdNtd4TSoXI4/HINM0qyyTJMIxoNClmLVq0SPv379cPP/ygW265RZdccom++OKLas8h56+qms5V5euOc1lVhw4d5Ha79cMPP2jdunWyWq2aMGGCTNPkvJ1gwYIFuvDCCzV37lz16NGD19xxqPeho97XH7974aHeh4ZaXz1qfeio9fXH7154qPWha8r13hbxIzZR6enpVWajz8nJUWJiYtDuhC2ZxeLLOm02my6++GItXbpU77zzTrXnsGPHjtFoZkyr6Vy1adNGpmkqPz9f6enpVda3dJWFNCMjQ3//+9+VlpamH374QR06dOC8yfdmc+utt+qDDz7QsmXLNHDgQEm85o5HvQ8d9b7++N0LH/W+etT62lHrQ0etrz9+98JHra9Zc6j39JQK0RlnnKFt27YpPz/fv2zNmjUaOnSov1AjOI/Ho/j4eA0ePFhr164NWLdmzRqNGDEiSi2LXTWdq+TkZPXp0ydg/YEDB3To0CGddtppkW5qTDNNU16vV/Hx8Zy3CtOmTdPOnTu1YcMG/5uWxGvueNT78FH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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# FacetGrid를 사용하여 범주별 히스토그램 그리기\n",
    "g = sns.FacetGrid(sen2023, col = '설립구분', col_wrap = 3, height = 4)\n",
    "g.map(sns.histplot, '교과프로그램수', kde = True, color = 'skyblue')\n",
    "g.set_titles('학교 {col_name}의 교과프로그램수 히스토그램')\n",
    "g.set_axis_labels('교과프로그램수', '빈도')\n",
    "plt.subplots_adjust(top = 0.9)\n",
    "plt.suptitle('범주별 교과프로그램수 히스토그램', fontsize = 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "af206d8e-73d2-42ce-af5f-ccd2a1f0676a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 상자그림 그리기\n",
    "plt.figure(figsize = (8, 6))\n",
    "sns.boxplot(data = sen2023, x = '설립구분', y = '교과프로그램수', palette = 'Set3')\n",
    "plt.title('범주별 교과프로그램수 상자그림')\n",
    "plt.xlabel('학교')\n",
    "plt.ylabel('교과프로그램수')\n",
    "plt.grid(axis = 'y', linestyle = '--', alpha = 0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "17456781-13bf-42f2-97e7-3b6c55a0e6b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\buillee\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 바이올린 그리기\n",
    "plt.figure(figsize = (8, 6))\n",
    "sns.violinplot(data = sen2023, x = '설립구분', y = '교과프로그램수', palette = 'Set3')\n",
    "plt.title('범주별 교과프로그램수 바이올린')\n",
    "plt.xlabel('학교')\n",
    "plt.ylabel('교과프로그램수')\n",
    "plt.grid(axis = 'y', linestyle = '--', alpha = 0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7ab58036-28d0-4902-83c0-0fdee4170e2b",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>설립구분</th>\n",
       "      <th></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>공립</th>\n",
       "      <td>564.0</td>\n",
       "      <td>12.299645</td>\n",
       "      <td>9.180484</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>국립</th>\n",
       "      <td>2.0</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>12.020815</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.25</td>\n",
       "      <td>11.5</td>\n",
       "      <td>15.75</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사립</th>\n",
       "      <td>38.0</td>\n",
       "      <td>37.710526</td>\n",
       "      <td>38.638245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.25</td>\n",
       "      <td>28.5</td>\n",
       "      <td>57.50</td>\n",
       "      <td>209.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count       mean        std  min    25%   50%    75%    max\n",
       "설립구분                                                             \n",
       "공립    564.0  12.299645   9.180484  0.0   6.00  10.0  16.00   55.0\n",
       "국립      2.0  11.500000  12.020815  3.0   7.25  11.5  15.75   20.0\n",
       "사립     38.0  37.710526  38.638245  0.0  13.25  28.5  57.50  209.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sen2023.groupby('설립구분')['교과프로그램수'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d3c2bda3-63b5-41b9-8014-14299e8ff2c4",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지역교육청</th>\n",
       "      <th></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>강남서초교육지원청</th>\n",
       "      <td>54.0</td>\n",
       "      <td>14.907407</td>\n",
       "      <td>12.346061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강동송파교육지원청</th>\n",
       "      <td>68.0</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>9.006424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.25</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강서양천교육지원청</th>\n",
       "      <td>65.0</td>\n",
       "      <td>13.276923</td>\n",
       "      <td>9.333253</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>교육부</th>\n",
       "      <td>2.0</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>12.020815</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.25</td>\n",
       "      <td>11.5</td>\n",
       "      <td>15.75</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>남부교육지원청</th>\n",
       "      <td>67.0</td>\n",
       "      <td>13.746269</td>\n",
       "      <td>26.168651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>209.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동부교육지원청</th>\n",
       "      <td>45.0</td>\n",
       "      <td>14.644444</td>\n",
       "      <td>12.564661</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동작관악교육지원청</th>\n",
       "      <td>43.0</td>\n",
       "      <td>13.325581</td>\n",
       "      <td>11.511321</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.50</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>북부교육지원청</th>\n",
       "      <td>65.0</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>12.694487</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서부교육지원청</th>\n",
       "      <td>71.0</td>\n",
       "      <td>15.042254</td>\n",
       "      <td>14.298288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.50</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>성동광진교육지원청</th>\n",
       "      <td>42.0</td>\n",
       "      <td>10.690476</td>\n",
       "      <td>10.358190</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11.75</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>성북강북교육지원청</th>\n",
       "      <td>43.0</td>\n",
       "      <td>18.627907</td>\n",
       "      <td>16.679076</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>중부교육지원청</th>\n",
       "      <td>39.0</td>\n",
       "      <td>14.692308</td>\n",
       "      <td>12.380277</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.50</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count       mean        std  min   25%   50%    75%    max\n",
       "지역교육청                                                                \n",
       "강남서초교육지원청   54.0  14.907407  12.346061  0.0  6.25  12.0  20.00   64.0\n",
       "강동송파교육지원청   68.0  14.250000   9.006424  0.0  8.00  12.0  18.25   49.0\n",
       "강서양천교육지원청   65.0  13.276923   9.333253  3.0  7.00  11.0  16.00   49.0\n",
       "교육부          2.0  11.500000  12.020815  3.0  7.25  11.5  15.75   20.0\n",
       "남부교육지원청     67.0  13.746269  26.168651  0.0  5.00   8.0  14.00  209.0\n",
       "동부교육지원청     45.0  14.644444  12.564661  2.0  7.00  11.0  17.00   64.0\n",
       "동작관악교육지원청   43.0  13.325581  11.511321  0.0  7.50  12.0  15.00   73.0\n",
       "북부교육지원청     65.0  10.600000  12.694487  0.0  4.00   7.0  15.00   71.0\n",
       "서부교육지원청     71.0  15.042254  14.298288  0.0  7.50  11.0  18.00   84.0\n",
       "성동광진교육지원청   42.0  10.690476  10.358190  0.0  5.00   7.5  11.75   55.0\n",
       "성북강북교육지원청   43.0  18.627907  16.679076  2.0  8.50  15.0  23.00  102.0\n",
       "중부교육지원청     39.0  14.692308  12.380277  2.0  6.50  11.0  20.00   58.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sen2023.groupby('지역교육청')['교과프로그램수'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "af5306cf-5b11-4d23-ac8e-8a608a5cf543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "지역교육청      설립구분\n",
       "강남서초교육지원청  공립       13.981132\n",
       "           사립       64.000000\n",
       "강동송파교육지원청  공립       14.250000\n",
       "강서양천교육지원청  공립       13.031250\n",
       "           사립       29.000000\n",
       "교육부        국립       11.500000\n",
       "남부교육지원청    공립       10.787879\n",
       "           사립      209.000000\n",
       "동부교육지원청    공립       12.170732\n",
       "           사립       40.000000\n",
       "동작관악교육지원청  공립       11.904762\n",
       "           사립       73.000000\n",
       "북부교육지원청    공립        8.389831\n",
       "           사립       32.333333\n",
       "서부교육지원청    공립       13.000000\n",
       "           사립       31.125000\n",
       "성동광진교육지원청  공립       10.026316\n",
       "           사립       17.000000\n",
       "성북강북교육지원청  공립       16.216216\n",
       "           사립       33.500000\n",
       "중부교육지원청    공립       11.727273\n",
       "           사립       31.000000\n",
       "Name: 교과프로그램수, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 지역교육청과 설립구분별로 교과프로그램수의 평균의 현황\n",
    "# 범주형 데이터 : 2개 : 지역교육청, 설립구분\n",
    "# 수치형 데이터 : 1개 : 교과프로그램수\n",
    "\n",
    "sen2023.groupby([\"지역교육청\", \"설립구분\"])['교과프로그램수'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1ef8ae79-506c-4f40-bdfd-f3a809b7fcfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>설립구분</th>\n",
       "      <th>공립</th>\n",
       "      <th>국립</th>\n",
       "      <th>사립</th>\n",
       "      <th>합계</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지역교육청</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강남서초교육지원청</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강동송파교육지원청</th>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강서양천교육지원청</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>교육부</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>남부교육지원청</th>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동부교육지원청</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동작관악교육지원청</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>북부교육지원청</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서부교육지원청</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>성동광진교육지원청</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>성북강북교육지원청</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>중부교육지원청</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>합계</th>\n",
       "      <td>564</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "설립구분        공립  국립  사립   합계\n",
       "지역교육청                      \n",
       "강남서초교육지원청   53   0   1   54\n",
       "강동송파교육지원청   68   0   0   68\n",
       "강서양천교육지원청   64   0   1   65\n",
       "교육부          0   2   0    2\n",
       "남부교육지원청     66   0   1   67\n",
       "동부교육지원청     41   0   4   45\n",
       "동작관악교육지원청   42   0   1   43\n",
       "북부교육지원청     59   0   6   65\n",
       "서부교육지원청     63   0   8   71\n",
       "성동광진교육지원청   38   0   4   42\n",
       "성북강북교육지원청   37   0   6   43\n",
       "중부교육지원청     33   0   6   39\n",
       "합계         564   2  38  604"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index        = sen2023['지역교육청'], \n",
    "            columns      = sen2023['설립구분'], \n",
    "            margins      = True, \n",
    "            margins_name = '합계')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d87a7b1b-98ba-4c60-8df4-9d3ec278ee40",
   "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>설립구분</th>\n",
       "      <th>공립</th>\n",
       "      <th>국립</th>\n",
       "      <th>사립</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지역교육청</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강남서초교육지원청</th>\n",
       "      <td>98.148148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.851852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강동송파교육지원청</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강서양천교육지원청</th>\n",
       "      <td>98.461538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>교육부</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>남부교육지원청</th>\n",
       "      <td>98.507463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.492537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동부교육지원청</th>\n",
       "      <td>91.111111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동작관악교육지원청</th>\n",
       "      <td>97.674419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.325581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>북부교육지원청</th>\n",
       "      <td>90.769231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서부교육지원청</th>\n",
       "      <td>88.732394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.267606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>성동광진교육지원청</th>\n",
       "      <td>90.476190</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>성북강북교육지원청</th>\n",
       "      <td>86.046512</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.953488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>중부교육지원청</th>\n",
       "      <td>84.615385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.384615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "설립구분               공립     국립         사립\n",
       "지역교육청                                  \n",
       "강남서초교육지원청   98.148148    0.0   1.851852\n",
       "강동송파교육지원청  100.000000    0.0   0.000000\n",
       "강서양천교육지원청   98.461538    0.0   1.538462\n",
       "교육부          0.000000  100.0   0.000000\n",
       "남부교육지원청     98.507463    0.0   1.492537\n",
       "동부교육지원청     91.111111    0.0   8.888889\n",
       "동작관악교육지원청   97.674419    0.0   2.325581\n",
       "북부교육지원청     90.769231    0.0   9.230769\n",
       "서부교육지원청     88.732394    0.0  11.267606\n",
       "성동광진교육지원청   90.476190    0.0   9.523810\n",
       "성북강북교육지원청   86.046512    0.0  13.953488\n",
       "중부교육지원청     84.615385    0.0  15.384615"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index        = sen2023['지역교육청'], \n",
    "            columns      = sen2023['설립구분'], \n",
    "            normalize    = \"index\")*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3c845f7-b767-40c2-bfbd-7a159305fae4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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