가설4번_EDA(수정).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7788c550",
   "metadata": {},
   "source": [
    "# 가설4 시험폐지 후 창체활동이 늘었을까? \n",
    "- -> 창체 4가지 활동 중 동아리 활동 현황 분석\n",
    "## 창체활동-동아리 가입 증가수 데이터전처리\n",
    "### 변수설정\n",
    "- df_time :서울시 연도/학교유형별 창의적 체험활동(창체) 주당수업시수\n",
    "- df_time_total : 서울시 연도별 창체 주당수업시수\n",
    "- corr : 2012~2018년 창체 주당수업시수 -->전처리용\n",
    "- df_club# : 2012년부터 2018년까지 동아리가입 df-오름차순 (예. df_club1= 2012년도 동아리가입현황)\n",
    "- group : 전처리 후 연도별로 concat -->전처리용 \n",
    "- sex : 남녀/전체 동아리 가입비율 -->전처리용 \n",
    "- sex_total : 전체 합계 동아리 가입비율 \n",
    "- sex_male_female : 남/녀 동아리 가입비율 \n",
    "- raw : 연도별 창체 수업연수와 동아리가입비율 상관관계\n",
    "- school : 학교유형별 동아리가입비율 -->전처리용 \n",
    "- school_special : 고등학교유형별 동아리가입비율 \n",
    "- economic : 경제수준별 \n",
    "- region : 지역규모분류별 -->전처리용\n",
    "- region_city : 대도시와 중소도시 동아리가입비율 \n",
    "- region_rural : 대도시와 읍/면 동아리가입비율\n",
    "- familytype : 가정유형형태 -->전처리용 \n",
    "- familytype_single : 양부모가정과 한부모가정의 동아리가입비율\n",
    "- familytype_grandparents : 양부모가정과 조손가정 동아리가입비율"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d627fed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import koreanize_matplotlib\n",
    "import datetime as dt\n",
    "from dateutil.parser import parse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8602d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#컬럼 디스플레이 최대로 보이기\n",
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd7e0591",
   "metadata": {},
   "source": [
    "# 데이터가공 및 시각화\n",
    "\n",
    "## 창의적 체험활동 연도별 증가 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d6452b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>교원의 주당 창의적체험활동 수업시수</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1) 조사기준일 : 해당연도 4월 1일</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2) 교원(기간제 포함)은 수석교사, 보직교사, 교사, 특수교사, 실기교사이며, 휴...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>연도</td>\n",
       "      <td>시도</td>\n",
       "      <td>학교급</td>\n",
       "      <td>교원의 주당 창의적체험활동 수업시수</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>1.22773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>0.76502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>0.61068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>0.89541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>2.02041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>1.05075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>0.85654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.36703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>2.02675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>1.18103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.53852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.63813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>1.9117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>1.06988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.56201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.57402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>1.88886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>1.04801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.58189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.56607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>2.08489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>1.00925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2017</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.59483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.63918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>2.05222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>0.96235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.6102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2018</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.61744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>2.06003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>0.90062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.66069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2019</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.62121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>1.97544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>0.79296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.54737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.52156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2021</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>1.9322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2021</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>0.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2021</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.61854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2021</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.52227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2022</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>1.91702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2022</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>0.76655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2022</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.59203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2022</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.50811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  교원의 주당 창의적체험활동 수업시수 Unnamed: 1 Unnamed: 2  \\\n",
       "0                               1) 조사기준일 : 해당연도 4월 1일        NaN        NaN   \n",
       "1   2) 교원(기간제 포함)은 수석교사, 보직교사, 교사, 특수교사, 실기교사이며, 휴...        NaN        NaN   \n",
       "2                                                  연도         시도        학교급   \n",
       "3                                                2012         서울       초등학교   \n",
       "4                                                2012         서울        중학교   \n",
       "5                                                2012         서울       고등학교   \n",
       "6                                                2012         서울         전체   \n",
       "7                                                2013         서울       초등학교   \n",
       "8                                                2013         서울        중학교   \n",
       "9                                                2013         서울       고등학교   \n",
       "10                                               2013         서울         전체   \n",
       "11                                               2014         서울       초등학교   \n",
       "12                                               2014         서울        중학교   \n",
       "13                                               2014         서울       고등학교   \n",
       "14                                               2014         서울         전체   \n",
       "15                                               2015         서울       초등학교   \n",
       "16                                               2015         서울        중학교   \n",
       "17                                               2015         서울       고등학교   \n",
       "18                                               2015         서울         전체   \n",
       "19                                               2016         서울       초등학교   \n",
       "20                                               2016         서울        중학교   \n",
       "21                                               2016         서울       고등학교   \n",
       "22                                               2016         서울         전체   \n",
       "23                                               2017         서울       초등학교   \n",
       "24                                               2017         서울        중학교   \n",
       "25                                               2017         서울       고등학교   \n",
       "26                                               2017         서울         전체   \n",
       "27                                               2018         서울       초등학교   \n",
       "28                                               2018         서울        중학교   \n",
       "29                                               2018         서울       고등학교   \n",
       "30                                               2018         서울         전체   \n",
       "31                                               2019         서울       초등학교   \n",
       "32                                               2019         서울        중학교   \n",
       "33                                               2019         서울       고등학교   \n",
       "34                                               2019         서울         전체   \n",
       "35                                               2020         서울       초등학교   \n",
       "36                                               2020         서울        중학교   \n",
       "37                                               2020         서울       고등학교   \n",
       "38                                               2020         서울         전체   \n",
       "39                                               2021         서울       초등학교   \n",
       "40                                               2021         서울        중학교   \n",
       "41                                               2021         서울       고등학교   \n",
       "42                                               2021         서울         전체   \n",
       "43                                               2022         서울       초등학교   \n",
       "44                                               2022         서울        중학교   \n",
       "45                                               2022         서울       고등학교   \n",
       "46                                               2022         서울         전체   \n",
       "\n",
       "             Unnamed: 3  \n",
       "0                   NaN  \n",
       "1                   NaN  \n",
       "2   교원의 주당 창의적체험활동 수업시수  \n",
       "3               1.22773  \n",
       "4               0.76502  \n",
       "5               0.61068  \n",
       "6               0.89541  \n",
       "7               2.02041  \n",
       "8               1.05075  \n",
       "9               0.85654  \n",
       "10              1.36703  \n",
       "11              2.02675  \n",
       "12              1.18103  \n",
       "13              1.53852  \n",
       "14              1.63813  \n",
       "15               1.9117  \n",
       "16              1.06988  \n",
       "17              1.56201  \n",
       "18              1.57402  \n",
       "19              1.88886  \n",
       "20              1.04801  \n",
       "21              1.58189  \n",
       "22              1.56607  \n",
       "23              2.08489  \n",
       "24              1.00925  \n",
       "25              1.59483  \n",
       "26              1.63918  \n",
       "27              2.05222  \n",
       "28              0.96235  \n",
       "29               1.6102  \n",
       "30              1.61744  \n",
       "31              2.06003  \n",
       "32              0.90062  \n",
       "33              1.66069  \n",
       "34              1.62121  \n",
       "35              1.97544  \n",
       "36              0.79296  \n",
       "37              1.54737  \n",
       "38              1.52156  \n",
       "39               1.9322  \n",
       "40                0.766  \n",
       "41              1.61854  \n",
       "42              1.52227  \n",
       "43              1.91702  \n",
       "44              0.76655  \n",
       "45              1.59203  \n",
       "46              1.50811  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#데이터불러오기\n",
    "df_time = pd.read_csv(\"미니&미드플젝 data/2012-2022 교원의 주당 창의적체험활동시수 -service.csv\")\n",
    "df_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f336f016",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_time.columns = ['연도','시도','학교급','교원의 주당 창의적체험활동 수업시수']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "912db2c6",
   "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>연도</th>\n",
       "      <th>시도</th>\n",
       "      <th>학교급</th>\n",
       "      <th>교원의 주당 창의적체험활동 수업시수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>1.22773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>0.76502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>0.61068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>0.89541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>2.02041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>1.05075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>0.85654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.36703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>2.02675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>1.18103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.53852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.63813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>1.9117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>1.06988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.56201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.57402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>1.88886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>1.04801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.58189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.56607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2017</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>2.08489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2017</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>1.00925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.59483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.63918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>2.05222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>0.96235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.6102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.61744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>2.06003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>0.90062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.66069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.62121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>1.97544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>0.79296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.54737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.52156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2021</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>1.9322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2021</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>0.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2021</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.61854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2021</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.52227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2022</td>\n",
       "      <td>서울</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>1.91702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2022</td>\n",
       "      <td>서울</td>\n",
       "      <td>중학교</td>\n",
       "      <td>0.76655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2022</td>\n",
       "      <td>서울</td>\n",
       "      <td>고등학교</td>\n",
       "      <td>1.59203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2022</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.50811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      연도  시도   학교급 교원의 주당 창의적체험활동 수업시수\n",
       "0   2012  서울  초등학교             1.22773\n",
       "1   2012  서울   중학교             0.76502\n",
       "2   2012  서울  고등학교             0.61068\n",
       "3   2012  서울    전체             0.89541\n",
       "4   2013  서울  초등학교             2.02041\n",
       "5   2013  서울   중학교             1.05075\n",
       "6   2013  서울  고등학교             0.85654\n",
       "7   2013  서울    전체             1.36703\n",
       "8   2014  서울  초등학교             2.02675\n",
       "9   2014  서울   중학교             1.18103\n",
       "10  2014  서울  고등학교             1.53852\n",
       "11  2014  서울    전체             1.63813\n",
       "12  2015  서울  초등학교              1.9117\n",
       "13  2015  서울   중학교             1.06988\n",
       "14  2015  서울  고등학교             1.56201\n",
       "15  2015  서울    전체             1.57402\n",
       "16  2016  서울  초등학교             1.88886\n",
       "17  2016  서울   중학교             1.04801\n",
       "18  2016  서울  고등학교             1.58189\n",
       "19  2016  서울    전체             1.56607\n",
       "20  2017  서울  초등학교             2.08489\n",
       "21  2017  서울   중학교             1.00925\n",
       "22  2017  서울  고등학교             1.59483\n",
       "23  2017  서울    전체             1.63918\n",
       "24  2018  서울  초등학교             2.05222\n",
       "25  2018  서울   중학교             0.96235\n",
       "26  2018  서울  고등학교              1.6102\n",
       "27  2018  서울    전체             1.61744\n",
       "28  2019  서울  초등학교             2.06003\n",
       "29  2019  서울   중학교             0.90062\n",
       "30  2019  서울  고등학교             1.66069\n",
       "31  2019  서울    전체             1.62121\n",
       "32  2020  서울  초등학교             1.97544\n",
       "33  2020  서울   중학교             0.79296\n",
       "34  2020  서울  고등학교             1.54737\n",
       "35  2020  서울    전체             1.52156\n",
       "36  2021  서울  초등학교              1.9322\n",
       "37  2021  서울   중학교               0.766\n",
       "38  2021  서울  고등학교             1.61854\n",
       "39  2021  서울    전체             1.52227\n",
       "40  2022  서울  초등학교             1.91702\n",
       "41  2022  서울   중학교             0.76655\n",
       "42  2022  서울  고등학교             1.59203\n",
       "43  2022  서울    전체             1.50811"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_time=df_time.iloc[3:]\n",
    "df_time=df_time.reset_index(drop=True)\n",
    "df_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "55c80d71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 44 entries, 0 to 43\n",
      "Data columns (total 4 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   연도                   44 non-null     object\n",
      " 1   시도                   44 non-null     object\n",
      " 2   학교급                  44 non-null     object\n",
      " 3   교원의 주당 창의적체험활동 수업시수  44 non-null     object\n",
      "dtypes: object(4)\n",
      "memory usage: 1.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_time.info()\n",
    "df_time[\"교원의 주당 창의적체험활동 수업시수\"] = pd.to_numeric(df_time[\"교원의 주당 창의적체험활동 수업시수\"], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a65ea44b",
   "metadata": {},
   "source": [
    "### 서울시 연도별 창체 시간 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dbea4b16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#서울시 연도별 창체시간 df_time_total 변수지정\n",
    "df_time_total=df_time[df_time[\"학교급\"]==\"전체\"]\n",
    "sns.barplot(data=df_time, x=\"연도\", y=\"교원의 주당 창의적체험활동 수업시수\", palette = sns.color_palette(\"colorblind\"), ci=None)\n",
    "plt.title(\"서울시 연도별 교원의 주당 창의적체험활동시수\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "461c6c90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#서울시 연도/학교유형별 창체시간\n",
    "sns.barplot(data=df_time, x=\"연도\", y=\"교원의 주당 창의적체험활동 수업시수\", hue=\"학교급\", palette = sns.color_palette(\"colorblind\"), ci=None)\n",
    "plt.title(\"서울시 학교유형별 교원의 주당 창의적체험활동시수\")\n",
    "plt.legend(bbox_to_anchor = (1,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b2630be",
   "metadata": {},
   "source": [
    "## 창의적 재량활동 중 동아리 가입비율 데이터 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d417094f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#데이터불러오기 2012~2018 년까지\n",
    "df_club1 = pd.read_csv(\"미니&미드플젝 data/2012.csv\",encoding=\"cp949\")\n",
    "df_club2 = pd.read_csv(\"미니&미드플젝 data/2013.csv\",encoding=\"cp949\")\n",
    "df_club3 = pd.read_csv(\"미니&미드플젝 data/2014.csv\",encoding=\"cp949\")\n",
    "df_club4 = pd.read_csv(\"미니&미드플젝 data/2015.csv\",encoding=\"cp949\")\n",
    "df_club5 = pd.read_csv(\"미니&미드플젝 data/2016.csv\",encoding=\"cp949\")\n",
    "df_club6 = pd.read_csv(\"미니&미드플젝 data/2017_2018.csv\",encoding=\"cp949\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee1c7ebc",
   "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>시점</th>\n",
       "      <th>응답자유형별(1)</th>\n",
       "      <th>응답자유형별(2)</th>\n",
       "      <th>전체(N)</th>\n",
       "      <th>전체(N).1</th>\n",
       "      <th>있다</th>\n",
       "      <th>있다.1</th>\n",
       "      <th>없다</th>\n",
       "      <th>없다.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>시점</td>\n",
       "      <td>응답자유형별(1)</td>\n",
       "      <td>응답자유형별(2)</td>\n",
       "      <td>응답자수 (명)</td>\n",
       "      <td>응답률 (%)</td>\n",
       "      <td>응답자수 (명)</td>\n",
       "      <td>응답률 (%)</td>\n",
       "      <td>응답자수 (명)</td>\n",
       "      <td>응답률 (%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>전체</td>\n",
       "      <td>소계</td>\n",
       "      <td>9075</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4829</td>\n",
       "      <td>53.2</td>\n",
       "      <td>4246</td>\n",
       "      <td>46.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>성별</td>\n",
       "      <td>남자</td>\n",
       "      <td>4766</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2486</td>\n",
       "      <td>52.2</td>\n",
       "      <td>2280</td>\n",
       "      <td>47.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>성별</td>\n",
       "      <td>여자</td>\n",
       "      <td>4309</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2343</td>\n",
       "      <td>54.4</td>\n",
       "      <td>1966</td>\n",
       "      <td>45.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>2812</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1339</td>\n",
       "      <td>47.6</td>\n",
       "      <td>1472</td>\n",
       "      <td>52.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>3106</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1801</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1305</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반계고</td>\n",
       "      <td>2429</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1344</td>\n",
       "      <td>55.3</td>\n",
       "      <td>1085</td>\n",
       "      <td>44.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>전문계고</td>\n",
       "      <td>728</td>\n",
       "      <td>100.0</td>\n",
       "      <td>344</td>\n",
       "      <td>47.3</td>\n",
       "      <td>384</td>\n",
       "      <td>52.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>대도시</td>\n",
       "      <td>3949</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2118</td>\n",
       "      <td>53.6</td>\n",
       "      <td>1831</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>중소도시</td>\n",
       "      <td>3839</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>52.5</td>\n",
       "      <td>1822</td>\n",
       "      <td>47.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>읍/면</td>\n",
       "      <td>1287</td>\n",
       "      <td>100.0</td>\n",
       "      <td>694</td>\n",
       "      <td>54.0</td>\n",
       "      <td>593</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>양부모가정</td>\n",
       "      <td>7995</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4323</td>\n",
       "      <td>54.1</td>\n",
       "      <td>3671</td>\n",
       "      <td>45.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>한부모가정</td>\n",
       "      <td>773</td>\n",
       "      <td>100.0</td>\n",
       "      <td>367</td>\n",
       "      <td>47.4</td>\n",
       "      <td>406</td>\n",
       "      <td>52.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>조손가정</td>\n",
       "      <td>108</td>\n",
       "      <td>100.0</td>\n",
       "      <td>56</td>\n",
       "      <td>51.7</td>\n",
       "      <td>52</td>\n",
       "      <td>48.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>기타</td>\n",
       "      <td>113</td>\n",
       "      <td>100.0</td>\n",
       "      <td>48</td>\n",
       "      <td>42.8</td>\n",
       "      <td>65</td>\n",
       "      <td>57.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>1271</td>\n",
       "      <td>100.0</td>\n",
       "      <td>724</td>\n",
       "      <td>57.0</td>\n",
       "      <td>547</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2012</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>7262</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3830</td>\n",
       "      <td>52.7</td>\n",
       "      <td>3432</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2012</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>490</td>\n",
       "      <td>100.0</td>\n",
       "      <td>255</td>\n",
       "      <td>51.9</td>\n",
       "      <td>236</td>\n",
       "      <td>48.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점  응답자유형별(1)  응답자유형별(2)     전체(N)  전체(N).1        있다     있다.1  \\\n",
       "0     시점  응답자유형별(1)  응답자유형별(2)  응답자수 (명)  응답률 (%)  응답자수 (명)  응답률 (%)   \n",
       "1   2012         전체         소계      9075    100.0      4829     53.2   \n",
       "2   2012         성별         남자      4766    100.0      2486     52.2   \n",
       "3   2012         성별         여자      4309    100.0      2343     54.4   \n",
       "4   2012      학교유형별       초등학교      2812    100.0      1339     47.6   \n",
       "5   2012      학교유형별        중학교      3106    100.0      1801     58.0   \n",
       "6   2012      학교유형별       일반계고      2429    100.0      1344     55.3   \n",
       "7   2012      학교유형별       전문계고       728    100.0       344     47.3   \n",
       "8   2012      지역규모별        대도시      3949    100.0      2118     53.6   \n",
       "9   2012      지역규모별       중소도시      3839    100.0      2017     52.5   \n",
       "10  2012      지역규모별        읍/면      1287    100.0       694     54.0   \n",
       "11  2012      가족유형별      양부모가정      7995    100.0      4323     54.1   \n",
       "12  2012      가족유형별      한부모가정       773    100.0       367     47.4   \n",
       "13  2012      가족유형별       조손가정       108    100.0        56     51.7   \n",
       "14  2012      가족유형별         기타       113    100.0        48     42.8   \n",
       "15  2012     경제적수준별          상      1271    100.0       724     57.0   \n",
       "16  2012     경제적수준별          중      7262    100.0      3830     52.7   \n",
       "17  2012     경제적수준별          하       490    100.0       255     51.9   \n",
       "\n",
       "          없다     없다.1  \n",
       "0   응답자수 (명)  응답률 (%)  \n",
       "1       4246     46.8  \n",
       "2       2280     47.8  \n",
       "3       1966     45.6  \n",
       "4       1472     52.4  \n",
       "5       1305     42.0  \n",
       "6       1085     44.7  \n",
       "7        384     52.7  \n",
       "8       1831     46.4  \n",
       "9       1822     47.5  \n",
       "10       593     46.0  \n",
       "11      3671     45.9  \n",
       "12       406     52.6  \n",
       "13        52     48.3  \n",
       "14        65     57.2  \n",
       "15       547     43.0  \n",
       "16      3432     47.3  \n",
       "17       236     48.1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_club1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0ed168cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['시점', '응답자유형별(1)', '응답자유형별(2)', '전체(N)', '전체(N).1', '있다', '있다.1', '없다',\n",
       "       '없다.1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_club1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6aa61a5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>시점</th>\n",
       "      <th>응답자유형별(1)</th>\n",
       "      <th>응답자유형별(2)</th>\n",
       "      <th>비율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>전체</td>\n",
       "      <td>소계</td>\n",
       "      <td>53.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>성별</td>\n",
       "      <td>남자</td>\n",
       "      <td>52.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>성별</td>\n",
       "      <td>여자</td>\n",
       "      <td>54.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반계고</td>\n",
       "      <td>55.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>전문계고</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>대도시</td>\n",
       "      <td>53.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>중소도시</td>\n",
       "      <td>52.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>읍/면</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>양부모가정</td>\n",
       "      <td>54.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>한부모가정</td>\n",
       "      <td>47.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>조손가정</td>\n",
       "      <td>51.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>기타</td>\n",
       "      <td>42.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>52.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2012</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>51.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1) 응답자유형별(2)    비율\n",
       "0   2012        전체        소계  53.2\n",
       "1   2012        성별        남자  52.2\n",
       "2   2012        성별        여자  54.4\n",
       "3   2012     학교유형별      초등학교  47.6\n",
       "4   2012     학교유형별       중학교  58.0\n",
       "5   2012     학교유형별      일반계고  55.3\n",
       "6   2012     학교유형별      전문계고  47.3\n",
       "7   2012     지역규모별       대도시  53.6\n",
       "8   2012     지역규모별      중소도시  52.5\n",
       "9   2012     지역규모별       읍/면  54.0\n",
       "10  2012     가족유형별     양부모가정  54.1\n",
       "11  2012     가족유형별     한부모가정  47.4\n",
       "12  2012     가족유형별      조손가정  51.7\n",
       "13  2012     가족유형별        기타  42.8\n",
       "14  2012    경제적수준별         상  57.0\n",
       "15  2012    경제적수준별         중  52.7\n",
       "16  2012    경제적수준별         하  51.9"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 있다 응답률 만 빼고컬럼 삭제\n",
    "df_club1= df_club1.drop(columns=['전체(N)','전체(N).1','있다','없다','없다.1'])\n",
    "#컬럼명 재정의\n",
    "df_club1.columns=['시점','응답자유형별(1)', '응답자유형별(2)', '비율']\n",
    "#중복컬럼이름 제거\n",
    "df_club1 =df_club1.loc[1:]\n",
    "#인덱스 재정렬\n",
    "df_club1=df_club1.reset_index(drop=True)\n",
    "df_club1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "105ba4d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>시점</th>\n",
       "      <th>응답자유형별(1)</th>\n",
       "      <th>응답자유형별(2)</th>\n",
       "      <th>있다</th>\n",
       "      <th>없다</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>전체</td>\n",
       "      <td>소계</td>\n",
       "      <td>70.9</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>성별</td>\n",
       "      <td>남자</td>\n",
       "      <td>69.7</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>성별</td>\n",
       "      <td>여자</td>\n",
       "      <td>72.2</td>\n",
       "      <td>27.8</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>67.5</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>73.1</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>73.1</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>64.4</td>\n",
       "      <td>35.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>대도시</td>\n",
       "      <td>72.7</td>\n",
       "      <td>27.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>중소도시</td>\n",
       "      <td>67.4</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>읍면지역</td>\n",
       "      <td>77.5</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>양부모가정</td>\n",
       "      <td>71.2</td>\n",
       "      <td>28.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>한부모가정</td>\n",
       "      <td>68.3</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>조손가정</td>\n",
       "      <td>75.8</td>\n",
       "      <td>24.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>기타</td>\n",
       "      <td>61.7</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013</td>\n",
       "      <td>학업성적별</td>\n",
       "      <td>상</td>\n",
       "      <td>73.8</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>학업성적별</td>\n",
       "      <td>중</td>\n",
       "      <td>69.9</td>\n",
       "      <td>30.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>학업성적별</td>\n",
       "      <td>하</td>\n",
       "      <td>69.3</td>\n",
       "      <td>30.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>72.7</td>\n",
       "      <td>27.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>68.9</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>70.5</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1)  응답자유형별(2)    있다    없다\n",
       "0   2013        전체         소계  70.9  29.1\n",
       "1   2013        성별         남자  69.7  30.3\n",
       "2   2013        성별         여자  72.2  27.8\n",
       "3   2013     학교유형별       초등학교  67.5  32.5\n",
       "4   2013     학교유형별        중학교  73.1  26.9\n",
       "5   2013     학교유형별  일반/특목/자율고  73.1  26.9\n",
       "6   2013     학교유형별       특성화고  64.4  35.6\n",
       "7   2013     지역규모별        대도시  72.7  27.3\n",
       "8   2013     지역규모별       중소도시  67.4  32.6\n",
       "9   2013     지역규모별       읍면지역  77.5  22.5\n",
       "10  2013     가족유형별      양부모가정  71.2  28.8\n",
       "11  2013     가족유형별      한부모가정  68.3  31.7\n",
       "12  2013     가족유형별       조손가정  75.8  24.2\n",
       "13  2013     가족유형별         기타  61.7  38.3\n",
       "14  2013     학업성적별          상  73.8  26.2\n",
       "15  2013     학업성적별          중  69.9  30.1\n",
       "16  2013     학업성적별          하  69.3  30.7\n",
       "17  2013    경제적수준별          상  72.7  27.3\n",
       "18  2013    경제적수준별          중  68.9  31.1\n",
       "19  2013    경제적수준별          하  70.5  29.5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_club2 = pd.read_csv(\"미니&미드플젝 data/2013.csv\",encoding=\"cp949\")\n",
    "df_club2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a3ca4fc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['시점', '응답자유형별(1)', '응답자유형별(2)', '있다', '없다'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_club2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d60372f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>시점</th>\n",
       "      <th>응답자유형별(1)</th>\n",
       "      <th>응답자유형별(2)</th>\n",
       "      <th>비율</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
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       "      <td>소계</td>\n",
       "      <td>70.9</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>성별</td>\n",
       "      <td>남자</td>\n",
       "      <td>69.7</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>64.4</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>대도시</td>\n",
       "      <td>72.7</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>중소도시</td>\n",
       "      <td>67.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>읍면지역</td>\n",
       "      <td>77.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>양부모가정</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>한부모가정</td>\n",
       "      <td>68.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>조손가정</td>\n",
       "      <td>75.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>기타</td>\n",
       "      <td>61.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>72.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>68.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1)  응답자유형별(2)    비율\n",
       "0   2013        전체         소계  70.9\n",
       "1   2013        성별         남자  69.7\n",
       "2   2013        성별         여자  72.2\n",
       "3   2013     학교유형별       초등학교  67.5\n",
       "4   2013     학교유형별        중학교  73.1\n",
       "5   2013     학교유형별  일반/특목/자율고  73.1\n",
       "6   2013     학교유형별       특성화고  64.4\n",
       "7   2013     지역규모별        대도시  72.7\n",
       "8   2013     지역규모별       중소도시  67.4\n",
       "9   2013     지역규모별       읍면지역  77.5\n",
       "10  2013     가족유형별      양부모가정  71.2\n",
       "11  2013     가족유형별      한부모가정  68.3\n",
       "12  2013     가족유형별       조손가정  75.8\n",
       "13  2013     가족유형별         기타  61.7\n",
       "14  2013    경제적수준별          상  72.7\n",
       "15  2013    경제적수준별          중  68.9\n",
       "16  2013    경제적수준별          하  70.5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_club2= df_club2.drop(columns=['없다'])\n",
    "df_club2.columns = (['시점', '응답자유형별(1)', '응답자유형별(2)', '비율'])\n",
    "df_club2= df_club2.drop([14,15,16])\n",
    "df_club2=df_club2.reset_index(drop=True)\n",
    "df_club2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ed356a6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>6</th>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>읍면지역</td>\n",
       "      <td>75.7</td>\n",
       "      <td>24.3</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>양부모가정</td>\n",
       "      <td>75.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>한부모가정</td>\n",
       "      <td>68.5</td>\n",
       "      <td>31.5</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>조손가정</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>기타</td>\n",
       "      <td>69.3</td>\n",
       "      <td>30.7</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>학업성적별</td>\n",
       "      <td>상</td>\n",
       "      <td>79.7</td>\n",
       "      <td>20.3</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014</td>\n",
       "      <td>학업성적별</td>\n",
       "      <td>중</td>\n",
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       "      <td>25.7</td>\n",
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       "      <th>16</th>\n",
       "      <td>2014</td>\n",
       "      <td>학업성적별</td>\n",
       "      <td>하</td>\n",
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       "      <th>17</th>\n",
       "      <td>2014</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>73.1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>72.3</td>\n",
       "      <td>27.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1)  응답자유형별(2)    있다    없다\n",
       "0   2014        전체         소계  74.6  25.4\n",
       "1   2014        성별         남자  74.4  25.6\n",
       "2   2014        성별         여자  74.9  25.1\n",
       "3   2014     학교유형별       초등학교  74.6  25.4\n",
       "4   2014     학교유형별        중학교  75.5  24.5\n",
       "5   2014     학교유형별  일반/특목/자율고  73.7  26.3\n",
       "6   2014     학교유형별       특성화고  74.3  25.7\n",
       "7   2014     지역규모별        대도시  76.8  23.2\n",
       "8   2014     지역규모별       중소도시  72.3  27.7\n",
       "9   2014     지역규모별       읍면지역  75.7  24.3\n",
       "10  2014     가족유형별      양부모가정  75.0  25.0\n",
       "11  2014     가족유형별      한부모가정  68.5  31.5\n",
       "12  2014     가족유형별       조손가정  76.4  23.6\n",
       "13  2014     가족유형별         기타  69.3  30.7\n",
       "14  2014     학업성적별          상  79.7  20.3\n",
       "15  2014     학업성적별          중  74.3  25.7\n",
       "16  2014     학업성적별          하  69.5  30.5\n",
       "17  2014    경제적수준별          상  76.9  23.1\n",
       "18  2014    경제적수준별          중  73.1  26.9\n",
       "19  2014    경제적수준별          하  72.3  27.7"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_club3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "403128ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>시점</th>\n",
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       "      <td>2014</td>\n",
       "      <td>학교유형별</td>\n",
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       "      <td>75.5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
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       "      <th>6</th>\n",
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       "      <td>74.3</td>\n",
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       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>지역규모별</td>\n",
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       "      <td>76.8</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014</td>\n",
       "      <td>지역규모별</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>읍면지역</td>\n",
       "      <td>75.7</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>양부모가정</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>한부모가정</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>조손가정</td>\n",
       "      <td>76.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>기타</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>76.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>73.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>72.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1)  응답자유형별(2)    비율\n",
       "0   2014        전체         소계  74.6\n",
       "1   2014        성별         남자  74.4\n",
       "2   2014        성별         여자  74.9\n",
       "3   2014     학교유형별       초등학교  74.6\n",
       "4   2014     학교유형별        중학교  75.5\n",
       "5   2014     학교유형별  일반/특목/자율고  73.7\n",
       "6   2014     학교유형별       특성화고  74.3\n",
       "7   2014     지역규모별        대도시  76.8\n",
       "8   2014     지역규모별       중소도시  72.3\n",
       "9   2014     지역규모별       읍면지역  75.7\n",
       "10  2014     가족유형별      양부모가정  75.0\n",
       "11  2014     가족유형별      한부모가정  68.5\n",
       "12  2014     가족유형별       조손가정  76.4\n",
       "13  2014     가족유형별         기타  69.3\n",
       "17  2014    경제적수준별          상  76.9\n",
       "18  2014    경제적수준별          중  73.1\n",
       "19  2014    경제적수준별          하  72.3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_club3= df_club3.drop(columns=['없다'])\n",
    "df_club3.columns = (['시점', '응답자유형별(1)', '응답자유형별(2)', '비율'])\n",
    "df_club3= df_club3.drop([14,15,16])\n",
    "df_club3.reset_index(drop=True)\n",
    "df_club3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4665dc7c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>75.0</td>\n",
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       "      <th>7</th>\n",
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       "      <th>8</th>\n",
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       "      <td>67.3</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>읍면지역</td>\n",
       "      <td>72.4</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>양부모가정</td>\n",
       "      <td>72.9</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>한부모가정</td>\n",
       "      <td>69.2</td>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>조손가정</td>\n",
       "      <td>73.9</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>기타</td>\n",
       "      <td>61.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>학업성적별</td>\n",
       "      <td>상</td>\n",
       "      <td>77.4</td>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>학업성적별</td>\n",
       "      <td>중</td>\n",
       "      <td>71.5</td>\n",
       "      <td>28.5</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015</td>\n",
       "      <td>학업성적별</td>\n",
       "      <td>하</td>\n",
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       "      <th>17</th>\n",
       "      <td>2015</td>\n",
       "      <td>경제적수준별</td>\n",
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       "      <th>18</th>\n",
       "      <td>2015</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>70.2</td>\n",
       "      <td>29.8</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
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       "      <td>29.6</td>\n",
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      "text/plain": [
       "      시점 응답자유형별(1)  응답자유형별(2)    있다    없다\n",
       "0   2015        전체         소계  72.6  27.4\n",
       "1   2015        성별         남자  72.0  28.0\n",
       "2   2015        성별         여자  73.2  26.8\n",
       "3   2015     학교유형별       초등학교  71.3  28.7\n",
       "4   2015     학교유형별        중학교  72.3  27.7\n",
       "5   2015     학교유형별  일반/특목/자율고  73.5  26.5\n",
       "6   2015     학교유형별       특성화고  75.0  25.0\n",
       "7   2015     지역규모별        대도시  77.5  22.5\n",
       "8   2015     지역규모별       중소도시  67.3  32.7\n",
       "9   2015     지역규모별       읍면지역  72.4  27.6\n",
       "10  2015     가족유형별      양부모가정  72.9  27.1\n",
       "11  2015     가족유형별      한부모가정  69.2  30.8\n",
       "12  2015     가족유형별       조손가정  73.9  26.1\n",
       "13  2015     가족유형별         기타  61.0  39.0\n",
       "14  2015     학업성적별          상  77.4  22.6\n",
       "15  2015     학업성적별          중  71.5  28.5\n",
       "16  2015     학업성적별          하  67.9  32.1\n",
       "17  2015    경제적수준별          상  74.7  25.3\n",
       "18  2015    경제적수준별          중  70.2  29.8\n",
       "19  2015    경제적수준별          하  70.4  29.6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_club4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a24ccae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>시점</th>\n",
       "      <th>응답자유형별(1)</th>\n",
       "      <th>응답자유형별(2)</th>\n",
       "      <th>비율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>전체</td>\n",
       "      <td>소계</td>\n",
       "      <td>72.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>성별</td>\n",
       "      <td>남자</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>성별</td>\n",
       "      <td>여자</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>71.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>72.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>대도시</td>\n",
       "      <td>77.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>중소도시</td>\n",
       "      <td>67.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015</td>\n",
       "      <td>지역규모별</td>\n",
       "      <td>읍면지역</td>\n",
       "      <td>72.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>양부모가정</td>\n",
       "      <td>72.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>한부모가정</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>조손가정</td>\n",
       "      <td>73.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>기타</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>74.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>70.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>70.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1)  응답자유형별(2)    비율\n",
       "0   2015        전체         소계  72.6\n",
       "1   2015        성별         남자  72.0\n",
       "2   2015        성별         여자  73.2\n",
       "3   2015     학교유형별       초등학교  71.3\n",
       "4   2015     학교유형별        중학교  72.3\n",
       "5   2015     학교유형별  일반/특목/자율고  73.5\n",
       "6   2015     학교유형별       특성화고  75.0\n",
       "7   2015     지역규모별        대도시  77.5\n",
       "8   2015     지역규모별       중소도시  67.3\n",
       "9   2015     지역규모별       읍면지역  72.4\n",
       "10  2015     가족유형별      양부모가정  72.9\n",
       "11  2015     가족유형별      한부모가정  69.2\n",
       "12  2015     가족유형별       조손가정  73.9\n",
       "13  2015     가족유형별         기타  61.0\n",
       "14  2015    경제적수준별          상  74.7\n",
       "15  2015    경제적수준별          중  70.2\n",
       "16  2015    경제적수준별          하  70.4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_club4= df_club4.drop(columns=['없다'])\n",
    "df_club4.columns = (['시점', '응답자유형별(1)', '응답자유형별(2)', '비율'])\n",
    "df_club4= df_club4.drop([14,15,16])\n",
    "df_club4=df_club4.reset_index(drop=True)\n",
    "df_club4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "58c92f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>응답자유형별(1)</th>\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>학교유형별</td>\n",
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       "      <td>68.6</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>지역규모별</td>\n",
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       "      <th>8</th>\n",
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       "      <td>지역규모별</td>\n",
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       "      <th>9</th>\n",
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       "      <td>70.7</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>양부모가정</td>\n",
       "      <td>73.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>한부모가정</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>조손가정</td>\n",
       "      <td>79.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>기타</td>\n",
       "      <td>67.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>76.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>70.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>71.6</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1) 응답자유형별(2)    비율\n",
       "0   2016        전체        소계  73.6\n",
       "1   2016        성별        남자  72.5\n",
       "2   2016        성별        여자  74.8\n",
       "3   2016     학교유형별      초등학교  76.0\n",
       "4   2016     학교유형별       중학교  75.4\n",
       "5   2016     학교유형별      일반계고  70.6\n",
       "6   2016     학교유형별     특성화계고  68.6\n",
       "7   2016     지역규모별       대도시  78.4\n",
       "8   2016     지역규모별      중소도시  70.3\n",
       "9   2016     지역규모별      읍면지역  70.7\n",
       "10  2016     가족유형별     양부모가정  73.7\n",
       "11  2016     가족유형별     한부모가정  73.0\n",
       "12  2016     가족유형별      조손가정  79.1\n",
       "13  2016     가족유형별        기타  67.5\n",
       "14  2016    경제적수준별         상  76.1\n",
       "15  2016    경제적수준별         중  70.6\n",
       "16  2016    경제적수준별         하  71.6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_club5= df_club5.drop(columns=['없다'])\n",
    "df_club5.columns = (['시점', '응답자유형별(1)', '응답자유형별(2)', '비율'])\n",
    "df_club5= df_club5.drop([14,15,16])\n",
    "df_club5=df_club5.reset_index(drop=True)\n",
    "df_club5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ce865678",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>학교급</td>\n",
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       "      <th>4</th>\n",
       "      <td>2017</td>\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
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       "      <th>7</th>\n",
       "      <td>2017</td>\n",
       "      <td>지역규모</td>\n",
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       "      <th>8</th>\n",
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       "      <th>10</th>\n",
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       "      <td>78.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017</td>\n",
       "      <td>가족유형</td>\n",
       "      <td>한부모가정</td>\n",
       "      <td>76.4</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017</td>\n",
       "      <td>가족유형</td>\n",
       "      <td>조손가정</td>\n",
       "      <td>75.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017</td>\n",
       "      <td>가족유형</td>\n",
       "      <td>기타</td>\n",
       "      <td>59.6</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017</td>\n",
       "      <td>경제적 수준</td>\n",
       "      <td>상</td>\n",
       "      <td>78.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>경제적 수준</td>\n",
       "      <td>중</td>\n",
       "      <td>76.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017</td>\n",
       "      <td>경제적 수준</td>\n",
       "      <td>하</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018</td>\n",
       "      <td>전체</td>\n",
       "      <td>소계</td>\n",
       "      <td>75.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>성별</td>\n",
       "      <td>남자</td>\n",
       "      <td>74.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018</td>\n",
       "      <td>성별</td>\n",
       "      <td>여자</td>\n",
       "      <td>77.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018</td>\n",
       "      <td>학교급</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018</td>\n",
       "      <td>학교급</td>\n",
       "      <td>중학교</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018</td>\n",
       "      <td>고교유형</td>\n",
       "      <td>일반계고</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018</td>\n",
       "      <td>고교유형</td>\n",
       "      <td>특성화계고</td>\n",
       "      <td>77.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>지역규모</td>\n",
       "      <td>대도시</td>\n",
       "      <td>79.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018</td>\n",
       "      <td>지역규모</td>\n",
       "      <td>중소도시</td>\n",
       "      <td>72.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018</td>\n",
       "      <td>지역규모</td>\n",
       "      <td>읍면지역</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018</td>\n",
       "      <td>가족유형</td>\n",
       "      <td>양부모가정</td>\n",
       "      <td>76.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018</td>\n",
       "      <td>가족유형</td>\n",
       "      <td>한부모가정</td>\n",
       "      <td>71.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018</td>\n",
       "      <td>가족유형</td>\n",
       "      <td>조손가정</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2018</td>\n",
       "      <td>가족유형</td>\n",
       "      <td>기타</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2018</td>\n",
       "      <td>경제적 수준</td>\n",
       "      <td>상</td>\n",
       "      <td>77.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2018</td>\n",
       "      <td>경제적 수준</td>\n",
       "      <td>중</td>\n",
       "      <td>74.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2018</td>\n",
       "      <td>경제적 수준</td>\n",
       "      <td>하</td>\n",
       "      <td>73.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1) 응답자유형별(2)    비율\n",
       "0   2017        전체        소계  77.6\n",
       "1   2017        성별        남자  76.3\n",
       "2   2017        성별        여자  79.1\n",
       "3   2017       학교급      초등학교  80.3\n",
       "4   2017       학교급       중학교  76.2\n",
       "5   2017      고교유형      일반계고  77.0\n",
       "6   2017      고교유형     특성화계고  75.6\n",
       "7   2017      지역규모       대도시  80.6\n",
       "8   2017      지역규모      중소도시  74.8\n",
       "9   2017      지역규모      읍면지역  77.4\n",
       "10  2017      가족유형     양부모가정  78.0\n",
       "11  2017      가족유형     한부모가정  76.4\n",
       "12  2017      가족유형      조손가정  75.1\n",
       "13  2017      가족유형        기타  59.6\n",
       "14  2017    경제적 수준         상  78.5\n",
       "15  2017    경제적 수준         중  76.8\n",
       "16  2017    경제적 수준         하  75.6\n",
       "17  2018        전체        소계  75.9\n",
       "18  2018        성별        남자  74.7\n",
       "19  2018        성별        여자  77.2\n",
       "20  2018       학교급      초등학교  76.5\n",
       "21  2018       학교급       중학교  75.0\n",
       "22  2018      고교유형      일반계고  76.0\n",
       "23  2018      고교유형     특성화계고  77.1\n",
       "24  2018      지역규모       대도시  79.8\n",
       "25  2018      지역규모      중소도시  72.5\n",
       "26  2018      지역규모      읍면지역  76.5\n",
       "27  2018      가족유형     양부모가정  76.3\n",
       "28  2018      가족유형     한부모가정  71.4\n",
       "29  2018      가족유형      조손가정  80.0\n",
       "30  2018      가족유형        기타  69.2\n",
       "31  2018    경제적 수준         상  77.2\n",
       "32  2018    경제적 수준         중  74.4\n",
       "33  2018    경제적 수준         하  73.9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_club6= df_club6.drop(columns=['없다'])\n",
    "df_club6.columns=['시점','응답자유형별(1)', '응답자유형별(2)', '비율']\n",
    "df_club6= df_club6.drop([15,16,17,36,37,38])\n",
    "df_club6= df_club6.drop([5,26])\n",
    "df_club6=df_club6.reset_index(drop=True)\n",
    "df_club6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6d373942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>시점</th>\n",
       "      <th>응답자유형별(1)</th>\n",
       "      <th>응답자유형별(2)</th>\n",
       "      <th>비율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>전체</td>\n",
       "      <td>소계</td>\n",
       "      <td>53.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>성별</td>\n",
       "      <td>남자</td>\n",
       "      <td>52.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>성별</td>\n",
       "      <td>여자</td>\n",
       "      <td>54.4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
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       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018</td>\n",
       "      <td>가족유형</td>\n",
       "      <td>조손가정</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2018</td>\n",
       "      <td>가족유형</td>\n",
       "      <td>기타</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2018</td>\n",
       "      <td>경제적 수준</td>\n",
       "      <td>상</td>\n",
       "      <td>77.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2018</td>\n",
       "      <td>경제적 수준</td>\n",
       "      <td>중</td>\n",
       "      <td>74.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2018</td>\n",
       "      <td>경제적 수준</td>\n",
       "      <td>하</td>\n",
       "      <td>73.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1) 응답자유형별(2)    비율\n",
       "0   2012        전체        소계  53.2\n",
       "1   2012        성별        남자  52.2\n",
       "2   2012        성별        여자  54.4\n",
       "3   2012     학교유형별      초등학교  47.6\n",
       "4   2012     학교유형별       중학교  58.0\n",
       "..   ...       ...       ...   ...\n",
       "29  2018      가족유형      조손가정  80.0\n",
       "30  2018      가족유형        기타  69.2\n",
       "31  2018    경제적 수준         상  77.2\n",
       "32  2018    경제적 수준         중  74.4\n",
       "33  2018    경제적 수준         하  73.9\n",
       "\n",
       "[119 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#전처리된 2012년도~18년 데이터 합치기\n",
    "group=pd.concat([df_club1,df_club2,df_club3,df_club4,df_club5,df_club6], axis=0)\n",
    "group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3cd23996",
   "metadata": {},
   "outputs": [],
   "source": [
    "#각 연도별 데이터명 상이한 부분 하나로 맞추기\n",
    "group=group.replace('지역규모','지역규모별')\n",
    "group=group.replace('가족유형','가족유형별')\n",
    "group=group.replace('경제적 수준','경제적수준별')\n",
    "group=group.replace('학교급','학교유형별')\n",
    "group=group.replace('고교유형','학교유형별')\n",
    "group=group.replace('읍면지역','읍/면')\n",
    "group=group.replace('가족유형','가족유형별')\n",
    "group=group.replace('일반계고','일반/특목/자율고')\n",
    "group=group.replace('전문계고','특성화고')\n",
    "group=group.replace('특성화계고','특성화고')\n",
    "group=group.replace('전체','성별')\n",
    "group=group.replace('소계','합계')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8f8a9ead",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>시점</th>\n",
       "      <th>응답자유형별(1)</th>\n",
       "      <th>응답자유형별(2)</th>\n",
       "      <th>비율</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>성별</td>\n",
       "      <td>남자</td>\n",
       "      <td>52.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>성별</td>\n",
       "      <td>여자</td>\n",
       "      <td>54.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>58.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>조손가정</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2018</td>\n",
       "      <td>가족유형별</td>\n",
       "      <td>기타</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2018</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>77.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2018</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>74.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2018</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>73.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1) 응답자유형별(2)    비율\n",
       "0   2012        성별        합계  53.2\n",
       "1   2012        성별        남자  52.2\n",
       "2   2012        성별        여자  54.4\n",
       "3   2012     학교유형별      초등학교  47.6\n",
       "4   2012     학교유형별       중학교  58.0\n",
       "..   ...       ...       ...   ...\n",
       "29  2018     가족유형별      조손가정  80.0\n",
       "30  2018     가족유형별        기타  69.2\n",
       "31  2018    경제적수준별         상  77.2\n",
       "32  2018    경제적수준별         중  74.4\n",
       "33  2018    경제적수준별         하  73.9\n",
       "\n",
       "[119 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a60ad90",
   "metadata": {},
   "outputs": [],
   "source": [
    "#비율 데이터 타입변경\n",
    "group[\"비율\"] = pd.to_numeric(group[\"비율\"], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d480aa90",
   "metadata": {},
   "source": [
    "### 동아리 가입비율 데이터 시각화하기\n",
    "\n",
    "#### 전체, 성별 가입자 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8212739e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sex= group[group['응답자유형별(1)'] == \"성별\"]\n",
    "sex=sex.drop(columns=[\"응답자유형별(1)\"])\n",
    "sex.columns=(['시점', '성별', '비율'])\n",
    "sex=sex.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e56b0c3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>시점</th>\n",
       "      <th>성별</th>\n",
       "      <th>비율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>합계</td>\n",
       "      <td>53.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>남자</td>\n",
       "      <td>52.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>여자</td>\n",
       "      <td>54.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>합계</td>\n",
       "      <td>70.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>남자</td>\n",
       "      <td>69.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>여자</td>\n",
       "      <td>72.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014</td>\n",
       "      <td>합계</td>\n",
       "      <td>74.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>남자</td>\n",
       "      <td>74.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014</td>\n",
       "      <td>여자</td>\n",
       "      <td>74.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015</td>\n",
       "      <td>합계</td>\n",
       "      <td>72.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015</td>\n",
       "      <td>남자</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015</td>\n",
       "      <td>여자</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016</td>\n",
       "      <td>합계</td>\n",
       "      <td>73.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016</td>\n",
       "      <td>남자</td>\n",
       "      <td>72.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>여자</td>\n",
       "      <td>74.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>합계</td>\n",
       "      <td>77.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017</td>\n",
       "      <td>남자</td>\n",
       "      <td>76.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>여자</td>\n",
       "      <td>79.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>합계</td>\n",
       "      <td>75.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018</td>\n",
       "      <td>남자</td>\n",
       "      <td>74.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018</td>\n",
       "      <td>여자</td>\n",
       "      <td>77.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점  성별    비율\n",
       "0   2012  합계  53.2\n",
       "1   2012  남자  52.2\n",
       "2   2012  여자  54.4\n",
       "3   2013  합계  70.9\n",
       "4   2013  남자  69.7\n",
       "5   2013  여자  72.2\n",
       "6   2014  합계  74.6\n",
       "7   2014  남자  74.4\n",
       "8   2014  여자  74.9\n",
       "9   2015  합계  72.6\n",
       "10  2015  남자  72.0\n",
       "11  2015  여자  73.2\n",
       "12  2016  합계  73.6\n",
       "13  2016  남자  72.5\n",
       "14  2016  여자  74.8\n",
       "15  2017  합계  77.6\n",
       "16  2017  남자  76.3\n",
       "17  2017  여자  79.1\n",
       "18  2018  합계  75.9\n",
       "19  2018  남자  74.7\n",
       "20  2018  여자  77.2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bab508b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sex_total 합계 변수\n",
    "#sex_male_female 남녀변수\n",
    "sex_total=sex[sex[\"성별\"]== \"합계\"]\n",
    "sex_male_female=sex[sex[\"성별\"]!= \"합계\"]\n",
    "sex_total=sex_total.reset_index(drop=True)\n",
    "sex_male_female=sex_male_female.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8a9e6822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>연도</th>\n",
       "      <th>시도</th>\n",
       "      <th>학교급</th>\n",
       "      <th>교원의 주당 창의적체험활동 수업시수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>0.89541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.36703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.63813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.57402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.56607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.63918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.61744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도  시도 학교급  교원의 주당 창의적체험활동 수업시수\n",
       "0  2012  서울  전체              0.89541\n",
       "1  2013  서울  전체              1.36703\n",
       "2  2014  서울  전체              1.63813\n",
       "3  2015  서울  전체              1.57402\n",
       "4  2016  서울  전체              1.56607\n",
       "5  2017  서울  전체              1.63918\n",
       "6  2018  서울  전체              1.61744"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=df_time_total.reset_index(drop=True).drop([7,8,9,10])\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d2514f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>시점</th>\n",
       "      <th>성별</th>\n",
       "      <th>동아리가입비율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>합계</td>\n",
       "      <td>53.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>합계</td>\n",
       "      <td>70.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014</td>\n",
       "      <td>합계</td>\n",
       "      <td>74.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015</td>\n",
       "      <td>합계</td>\n",
       "      <td>72.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016</td>\n",
       "      <td>합계</td>\n",
       "      <td>73.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>합계</td>\n",
       "      <td>77.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>합계</td>\n",
       "      <td>75.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점  성별  동아리가입비율\n",
       "0   2012  합계     53.2\n",
       "3   2013  합계     70.9\n",
       "6   2014  합계     74.6\n",
       "9   2015  합계     72.6\n",
       "12  2016  합계     73.6\n",
       "15  2017  합계     77.6\n",
       "18  2018  합계     75.9"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_total=sex[sex[\"성별\"]== \"합계\"]\n",
    "sex_total.columns=['시점', '성별', '동아리가입비율']\n",
    "sex_total.reset_index(drop=True)\n",
    "sex_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f7523007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex_total=sex[sex[\"성별\"]== \"합계\"]\n",
    "sns.barplot(data=sex_total, x=\"시점\", y=\"비율\",  palette = sns.color_palette(\"colorblind\"), ci=None)\n",
    "plt.title(\"학생 동아리활동 가입 비율\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42d296c9",
   "metadata": {},
   "source": [
    "- 연도별 그래프를 봤을 때 2013년에 상대적으로 비율이 많이 증가함을 알 수있다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0ac13b25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=sex_male_female, x=\"시점\", y=\"비율\", hue=\"성별\", palette = sns.color_palette(\"colorblind\"), ci=None)\n",
    "plt.title(\"성별 동아리활동 가입 비율\")\n",
    "plt.legend(bbox_to_anchor = (1,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd85689",
   "metadata": {},
   "source": [
    "- 남녀별 큰 차이가 확인되지 않으나 여자 학생들의 동아리가입 비율이 높다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7767e8a4",
   "metadata": {},
   "source": [
    "#### 창체 수업연수와 동아리 가입비율 상관관계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "01f61885",
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>연도</th>\n",
       "      <th>시도</th>\n",
       "      <th>학교급</th>\n",
       "      <th>교원의 주당 창의적체험활동 수업시수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>0.89541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.36703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.63813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.57402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.56607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.63918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.61744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도  시도 학교급  교원의 주당 창의적체험활동 수업시수\n",
       "0  2012  서울  전체              0.89541\n",
       "1  2013  서울  전체              1.36703\n",
       "2  2014  서울  전체              1.63813\n",
       "3  2015  서울  전체              1.57402\n",
       "4  2016  서울  전체              1.56607\n",
       "5  2017  서울  전체              1.63918\n",
       "6  2018  서울  전체              1.61744"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1afd1256",
   "metadata": {},
   "outputs": [],
   "source": [
    "sex_total=sex_total.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4e42de9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.89541</td>\n",
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       "      <td>53.2</td>\n",
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       "      <td>74.6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>전체</td>\n",
       "      <td>1.57402</td>\n",
       "      <td>2015</td>\n",
       "      <td>합계</td>\n",
       "      <td>72.6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.56607</td>\n",
       "      <td>2016</td>\n",
       "      <td>합계</td>\n",
       "      <td>73.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.63918</td>\n",
       "      <td>2017</td>\n",
       "      <td>합계</td>\n",
       "      <td>77.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.61744</td>\n",
       "      <td>2018</td>\n",
       "      <td>합계</td>\n",
       "      <td>75.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도  시도 학교급  교원의 주당 창의적체험활동 수업시수    시점  성별    비율\n",
       "0  2012  서울  전체              0.89541  2012  합계  53.2\n",
       "1  2013  서울  전체              1.36703  2013  합계  70.9\n",
       "2  2014  서울  전체              1.63813  2014  합계  74.6\n",
       "3  2015  서울  전체              1.57402  2015  합계  72.6\n",
       "4  2016  서울  전체              1.56607  2016  합계  73.6\n",
       "5  2017  서울  전체              1.63918  2017  합계  77.6\n",
       "6  2018  서울  전체              1.61744  2018  합계  75.9"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#창체 수업연수와 동아리 가입비율 concat\n",
    "raw=pd.concat([corr,sex_total], axis=1)\n",
    "raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "174a31f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>1.61744</td>\n",
       "      <td>75.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도  시도 학교급  교원의 주당 창의적체험활동 수업시수    비율\n",
       "0  2012  서울  전체              0.89541  53.2\n",
       "1  2013  서울  전체              1.36703  70.9\n",
       "2  2014  서울  전체              1.63813  74.6\n",
       "3  2015  서울  전체              1.57402  72.6\n",
       "4  2016  서울  전체              1.56607  73.6\n",
       "5  2017  서울  전체              1.63918  77.6\n",
       "6  2018  서울  전체              1.61744  75.9"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw=raw.drop(columns=[\"시점\",\"성별\"])\n",
    "raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0b8f2fb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 창의적 체험활동 수업시수와 동아리 가입비율 상관관계 0.98\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.heatmap(data = raw.corr(), annot=True, fmt = '.2f' ,)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e78cc0c2",
   "metadata": {},
   "source": [
    "#### 학교유형별 동아리 가입비율 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c0b7e15b",
   "metadata": {},
   "outputs": [],
   "source": [
    "school= group[group['응답자유형별(1)'] == \"학교유형별\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "748a2c89",
   "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>시점</th>\n",
       "      <th>응답자유형별(1)</th>\n",
       "      <th>응답자유형별(2)</th>\n",
       "      <th>비율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>55.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>67.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>73.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>73.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>64.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>74.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>75.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>73.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>74.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>71.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>72.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>75.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>70.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>68.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>80.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>76.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>초등학교</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>중학교</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>77.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1)  응답자유형별(2)    비율\n",
       "3   2012     학교유형별       초등학교  47.6\n",
       "4   2012     학교유형별        중학교  58.0\n",
       "5   2012     학교유형별  일반/특목/자율고  55.3\n",
       "6   2012     학교유형별       특성화고  47.3\n",
       "3   2013     학교유형별       초등학교  67.5\n",
       "4   2013     학교유형별        중학교  73.1\n",
       "5   2013     학교유형별  일반/특목/자율고  73.1\n",
       "6   2013     학교유형별       특성화고  64.4\n",
       "3   2014     학교유형별       초등학교  74.6\n",
       "4   2014     학교유형별        중학교  75.5\n",
       "5   2014     학교유형별  일반/특목/자율고  73.7\n",
       "6   2014     학교유형별       특성화고  74.3\n",
       "3   2015     학교유형별       초등학교  71.3\n",
       "4   2015     학교유형별        중학교  72.3\n",
       "5   2015     학교유형별  일반/특목/자율고  73.5\n",
       "6   2015     학교유형별       특성화고  75.0\n",
       "3   2016     학교유형별       초등학교  76.0\n",
       "4   2016     학교유형별        중학교  75.4\n",
       "5   2016     학교유형별  일반/특목/자율고  70.6\n",
       "6   2016     학교유형별       특성화고  68.6\n",
       "3   2017     학교유형별       초등학교  80.3\n",
       "4   2017     학교유형별        중학교  76.2\n",
       "5   2017     학교유형별  일반/특목/자율고  77.0\n",
       "6   2017     학교유형별       특성화고  75.6\n",
       "20  2018     학교유형별       초등학교  76.5\n",
       "21  2018     학교유형별        중학교  75.0\n",
       "22  2018     학교유형별  일반/특목/자율고  76.0\n",
       "23  2018     학교유형별       특성화고  77.1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "50c3f833",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>시점</th>\n",
       "      <th>응답자유형별(1)</th>\n",
       "      <th>응답자유형별(2)</th>\n",
       "      <th>비율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>55.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>73.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>64.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>73.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>74.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>70.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>68.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>일반/특목/자율고</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018</td>\n",
       "      <td>학교유형별</td>\n",
       "      <td>특성화고</td>\n",
       "      <td>77.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1)  응답자유형별(2)    비율\n",
       "5   2012     학교유형별  일반/특목/자율고  55.3\n",
       "6   2012     학교유형별       특성화고  47.3\n",
       "5   2013     학교유형별  일반/특목/자율고  73.1\n",
       "6   2013     학교유형별       특성화고  64.4\n",
       "5   2014     학교유형별  일반/특목/자율고  73.7\n",
       "6   2014     학교유형별       특성화고  74.3\n",
       "5   2015     학교유형별  일반/특목/자율고  73.5\n",
       "6   2015     학교유형별       특성화고  75.0\n",
       "5   2016     학교유형별  일반/특목/자율고  70.6\n",
       "6   2016     학교유형별       특성화고  68.6\n",
       "5   2017     학교유형별  일반/특목/자율고  77.0\n",
       "6   2017     학교유형별       특성화고  75.6\n",
       "22  2018     학교유형별  일반/특목/자율고  76.0\n",
       "23  2018     학교유형별       특성화고  77.1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_special=school[(school['응답자유형별(2)'] == \"특성화고\") | (school['응답자유형별(2)'] == \"일반/특목/자율고\")]\n",
    "school_special"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a727c2b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '학교유형에 따른 동아리 가입 비율')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=school_special, x=\"비율\", y=\"시점\", hue=\"응답자유형별(2)\", palette = sns.color_palette(\"muted\"), ci=None)\n",
    "plt.legend(bbox_to_anchor = (1,1))\n",
    "plt.title(\"학교유형에 따른 동아리 가입 비율\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7366caf",
   "metadata": {},
   "source": [
    "- 일반/특목/자율고 와 특성화고 사이의 가입비율을 봤을때 2014년 이후 특성화고의 가입수 비율이 증가한 것으로 확인된다.\n",
    "- 2013년 이전에는 일반계와 전문계로 이분화 되었으나 이후 특성화고로 변경되면서 특성화고의 동아리 가입 비율 증가된 것으로 예상된다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25660580",
   "metadata": {},
   "source": [
    "#### 경제적 수준별 동아리 가입비율 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b17565c1",
   "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>시점</th>\n",
       "      <th>응답자유형별(1)</th>\n",
       "      <th>응답자유형별(2)</th>\n",
       "      <th>비율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>52.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2012</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>51.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>72.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>68.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>76.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>73.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>72.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>74.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>70.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>70.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>76.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>70.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>71.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>78.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>76.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2018</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>상</td>\n",
       "      <td>77.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2018</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>중</td>\n",
       "      <td>74.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2018</td>\n",
       "      <td>경제적수준별</td>\n",
       "      <td>하</td>\n",
       "      <td>73.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      시점 응답자유형별(1) 응답자유형별(2)    비율\n",
       "14  2012    경제적수준별         상  57.0\n",
       "15  2012    경제적수준별         중  52.7\n",
       "16  2012    경제적수준별         하  51.9\n",
       "14  2013    경제적수준별         상  72.7\n",
       "15  2013    경제적수준별         중  68.9\n",
       "16  2013    경제적수준별         하  70.5\n",
       "17  2014    경제적수준별         상  76.9\n",
       "18  2014    경제적수준별         중  73.1\n",
       "19  2014    경제적수준별         하  72.3\n",
       "14  2015    경제적수준별         상  74.7\n",
       "15  2015    경제적수준별         중  70.2\n",
       "16  2015    경제적수준별         하  70.4\n",
       "14  2016    경제적수준별         상  76.1\n",
       "15  2016    경제적수준별         중  70.6\n",
       "16  2016    경제적수준별         하  71.6\n",
       "14  2017    경제적수준별         상  78.5\n",
       "15  2017    경제적수준별         중  76.8\n",
       "16  2017    경제적수준별         하  75.6\n",
       "31  2018    경제적수준별         상  77.2\n",
       "32  2018    경제적수준별         중  74.4\n",
       "33  2018    경제적수준별         하  73.9"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "economic= group[group['응답자유형별(1)'] == \"경제적수준별\"]\n",
    "economic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d7eb694f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '경제적 수준별 동아리활동 참여 비율')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=economic, x=\"비율\", y=\"시점\", hue=\"응답자유형별(2)\", palette = sns.color_palette(\"muted\"), ci=None)\n",
    "plt.legend(bbox_to_anchor = (1,1))\n",
    "plt.title(\"경제적 수준별 동아리활동 참여 비율\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "68a99501",
   "metadata": {},
   "outputs": [],
   "source": [
    "region= group[group['응답자유형별(1)'] == \"지역규모별\"]\n",
    "region_city= region[(region['응답자유형별(2)'] == \"대도시\")|(region['응답자유형별(2)']=='중소도시')]\n",
    "region_rural= region[(region['응답자유형별(2)'] == \"대도시\")|(region['응답자유형별(2)']=='읍/면')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "31777185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=region_city, x=\"비율\", y=\"시점\", hue=\"응답자유형별(2)\", palette = sns.color_palette(\"muted\"), ci=None)\n",
    "plt.legend(bbox_to_anchor = (1,1))\n",
    "plt.title(\"대도시와 중소도시별 동아리활동 참여 비율\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2558f23d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=region_rural, x=\"비율\", y=\"시점\", hue=\"응답자유형별(2)\", palette = sns.color_palette(\"muted\"), ci=None)\n",
    "plt.legend(bbox_to_anchor = (1,1))\n",
    "plt.title(\"대도시와 읍/면별 동아리활동 참여 비율\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdea80d3",
   "metadata": {},
   "source": [
    "- 대도시와 중소도시를 비교했을 때 대도시가 항상 동아리가입자비율이 높지만, \n",
    "- 대도시와 읍면을 비교했을 때 2013년도 읍/면의 비율이 더 높아 확인해 볼 필요가 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3c80d8c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '가족형태별 동아리활동 참여 비율')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "familytype= group[group['응답자유형별(1)'] == \"가족유형별\"]\n",
    "\n",
    "sns.barplot(data=familytype, x=\"시점\", y=\"비율\", hue=\"응답자유형별(2)\", palette = sns.color_palette(\"muted\"), ci=None)\n",
    "plt.legend(bbox_to_anchor = (1,1))\n",
    "plt.title(\"가족형태별 동아리활동 참여 비율\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bdf7ea27",
   "metadata": {},
   "outputs": [],
   "source": [
    "familytype_single= familytype[(familytype['응답자유형별(2)'] == \"양부모가정\")|(familytype['응답자유형별(2)']==\"한부모가정\")]\n",
    "familytype_grandparents= familytype[(familytype['응답자유형별(2)'] == \"양부모가정\")|(familytype['응답자유형별(2)']==\"조손가정\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9a1df9a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=familytype_single, x=\"시점\", y=\"비율\", hue=\"응답자유형별(2)\", palette = sns.color_palette(\"muted\"), ci=None)\n",
    "plt.legend(bbox_to_anchor = (1,1))\n",
    "plt.title(\"양부모가정과 한부모가정 가족형태별 동아리활동 참여 비율\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3e7ee29d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=familytype_grandparents, x=\"시점\", y=\"비율\", hue=\"응답자유형별(2)\", palette = sns.color_palette(\"muted\"), ci=None)\n",
    "plt.legend(bbox_to_anchor = (1,1))\n",
    "plt.title(\"양부모가정과 조손가정 가족형태별 동아리활동 참여 비율\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cad0f88",
   "metadata": {},
   "source": [
    "- 2012년 2017년을 제외하고는 모두 조손가정의 동아리 활동 참여비율이 높게 나타난다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd9302ec",
   "metadata": {},
   "source": [
    "# 결론\n",
    "- 창체수업시간 증가에 따라 동아리 가입자 비율도 늘어나고 있다. \n",
    "    =>상관계수 0.98\n",
    "- 2013년도에 특히 증가세가 두드러지고 있다. \n",
    "     ==> \"창체활동 중 하나였던 스포츠클럽활동을 따로 분리하여 의무화 시킴\" / 2012년도 이후 주5일제 시행으로 주말 활동프로그램의 증가가 두드러짐 \n",
    "     >https://news.seoul.go.kr/welfare/archives/306939\n",
    "     >https://www.kice.re.kr/upload/brochureBoard/7/2021/05/1622172496674_56140382171416185.pdf\n",
    "- 동아리가입비율에서 특이한 점은 고등학교 유형별 특성화고에서 2014년도부터 일반/자율/특목고를 앞지르는데 이후 가입자 비율은 비슷해지고 있다.\n",
    "    >2012년도 전문계 고등학교에서 특성화고로 명칭변경되면서 세분화된 맞춤 전공 지원\n",
    "    ```\n",
    "    학년에 관계없이 방과 후 수업 시간에는 학생들이 적성에 따라 전공 관련 자격증이나 전문교과 등 부족한 부분을 보완할 수 있도록 돕고 있으며, 이외에도 학교에서 각종 공모전, 대회에 관한 소식을 빠짐없이 전해 학생들이 진학과 취업의 꿈에 한발 가까이 다가갈 수 있도록 하고 있습니다.```\n",
    "    \n",
    "    https://if-blog.tistory.com/1609"
   ]
  }
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