5.국회의원현황_스크래핑_분석_시각화_저장.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 국회의원 현황 스크래핑\n",
    "1. 국회의원 이름,ID 추출\n",
    "2. 국회의원 상세페이지에 300번 요청을 보내서 상세정보 추출\n",
    "3. 상세정보들을 DataFrame 객체에 저장해서 표데이터 만들기\n",
    "4. 시각화(막대그래프, 히스토그램, 파이챠트, 히트맵 ) \n",
    "5. DB에 members 테이블에 저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "300\n",
      "300\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['9771230', '9771142', '9771174', '9771233', '9771283']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "\n",
    "url = 'https://www.assembly.go.kr/assm/memact/congressman/memCond/memCondListAjax.do'\n",
    "req_param = {\n",
    "    \"currentPage\":1,\n",
    "    \"rowPerPage\":500\n",
    "}\n",
    "res = requests.get(url, params=req_param)\n",
    "print(res.status_code)\n",
    "html = res.text\n",
    "soup = BeautifulSoup(html,'html.parser')\n",
    "print(len(soup.select('div.memberna_list dl dt a')))\n",
    "\n",
    "atag_list = soup.select('div.memberna_list dl dt a')\n",
    "\n",
    "member_id_list = []\n",
    "for idx,atag in enumerate(atag_list,1):\n",
    "    href = atag['href']\n",
    "    matched = re.search(r'(\\d+)',href)\n",
    "    member_id = matched.group(0)\n",
    "    member_id_list.append(member_id)\n",
    "    #print(idx, atag.text, member_id)\n",
    "\n",
    "print(len(member_id_list))    \n",
    "member_id_list[0:5]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----스크래핑 시작-----------\n",
      "--> 인덱스 1 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771230 200\n",
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     ]
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     "text": [
      "--> 인덱스 101 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770774 200\n",
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      "--> 인덱스 147 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771178 200\n",
      "--> 인덱스 148 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771287 200\n",
      "--> 인덱스 149 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771271 200\n",
      "--> 인덱스 150 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771155 200\n",
      "--> 인덱스 151 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771010 200\n",
      "--> 인덱스 152 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771202 200\n",
      "--> 인덱스 153 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771113 200\n",
      "--> 인덱스 154 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771172 200\n",
      "--> 인덱스 155 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771037 200\n",
      "--> 인덱스 156 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771261 200\n",
      "--> 인덱스 157 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770871 200\n",
      "--> 인덱스 158 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770872 200\n",
      "--> 인덱스 159 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771038 200\n",
      "--> 인덱스 160 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771126 200\n",
      "--> 인덱스 161 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771121 200\n",
      "--> 인덱스 162 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770966 200\n",
      "--> 인덱스 163 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771199 200\n",
      "--> 인덱스 164 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770912 200\n",
      "--> 인덱스 165 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771266 200\n",
      "--> 인덱스 166 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771118 200\n",
      "--> 인덱스 167 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770808 200\n",
      "--> 인덱스 168 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771227 200\n",
      "--> 인덱스 169 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771263 200\n",
      "--> 인덱스 170 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770598 200\n",
      "--> 인덱스 171 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771153 200\n",
      "--> 인덱스 172 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770811 200\n",
      "--> 인덱스 173 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771170 200\n",
      "--> 인덱스 174 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771221 200\n",
      "--> 인덱스 175 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770812 200\n",
      "--> 인덱스 176 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771239 200\n",
      "--> 인덱스 177 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771212 200\n",
      "--> 인덱스 178 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771240 200\n",
      "--> 인덱스 179 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771032 200\n",
      "--> 인덱스 180 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770876 200\n",
      "--> 인덱스 181 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770813 200\n",
      "--> 인덱스 182 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771123 200\n",
      "--> 인덱스 183 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770905 200\n",
      "--> 인덱스 184 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771198 200\n",
      "--> 인덱스 185 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771191 200\n",
      "--> 인덱스 186 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771106 200\n",
      "--> 인덱스 187 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771232 200\n",
      "--> 인덱스 188 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771260 200\n",
      "--> 인덱스 189 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771028 200\n",
      "--> 인덱스 190 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770602 200\n",
      "--> 인덱스 191 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771154 200\n",
      "--> 인덱스 192 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770414 200\n",
      "--> 인덱스 193 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771207 200\n",
      "--> 인덱스 194 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771098 200\n",
      "--> 인덱스 195 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771151 200\n",
      "--> 인덱스 196 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771185 200\n",
      "--> 인덱스 197 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771284 200\n",
      "--> 인덱스 198 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771269 200\n",
      "--> 인덱스 199 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771003 200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> 인덱스 200 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771251 200\n",
      "--> 인덱스 201 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771255 200\n",
      "--> 인덱스 202 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771158 200\n",
      "--> 인덱스 203 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771115 200\n",
      "--> 인덱스 204 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771184 200\n",
      "--> 인덱스 205 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771015 200\n",
      "--> 인덱스 206 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770826 200\n",
      "--> 인덱스 207 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771213 200\n",
      "--> 인덱스 208 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771278 200\n",
      "--> 인덱스 209 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770878 200\n",
      "--> 인덱스 210 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771201 200\n",
      "--> 인덱스 211 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771060 200\n",
      "--> 인덱스 212 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771205 200\n",
      "--> 인덱스 213 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770914 200\n",
      "--> 인덱스 214 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771242 200\n",
      "--> 인덱스 215 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771138 200\n",
      "--> 인덱스 216 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770835 200\n",
      "--> 인덱스 217 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771002 200\n",
      "--> 인덱스 218 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771190 200\n",
      "--> 인덱스 219 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771280 200\n",
      "--> 인덱스 220 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770836 200\n",
      "--> 인덱스 221 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771129 200\n",
      "--> 인덱스 222 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770837 200\n",
      "--> 인덱스 223 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771157 200\n",
      "--> 인덱스 224 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770839 200\n",
      "--> 인덱스 225 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771177 200\n",
      "--> 인덱스 226 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771041 200\n",
      "--> 인덱스 227 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771000 200\n",
      "--> 인덱스 228 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771203 200\n",
      "--> 인덱스 229 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771110 200\n",
      "--> 인덱스 230 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770954 200\n",
      "--> 인덱스 231 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771160 200\n",
      "--> 인덱스 232 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771275 200\n",
      "--> 인덱스 233 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771139 200\n",
      "--> 인덱스 234 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771272 200\n",
      "--> 인덱스 235 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770951 200\n",
      "--> 인덱스 236 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771253 200\n",
      "--> 인덱스 237 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770846 200\n",
      "--> 인덱스 238 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770926 200\n",
      "--> 인덱스 239 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771245 200\n",
      "--> 인덱스 240 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771140 200\n",
      "--> 인덱스 241 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9770881 200\n",
      "--> 인덱스 242 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771012 200\n",
      "--> 인덱스 243 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771150 200\n",
      "--> 인덱스 244 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771103 200\n",
      "--> 인덱스 245 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771200 200\n",
      "--> 인덱스 246 https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd=9771006 200\n"
     ]
    },
    {
     "ename": "ConnectionError",
     "evalue": "HTTPSConnectionPool(host='www.assembly.go.kr', port=443): Max retries exceeded with url: /assm/memPop/memPopup.do?dept_cd=9771189 (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x00000134607A7E50>: Failed to establish a new connection: [WinError 10060] 연결된 구성원으로부터 응답이 없어 연결하지 못했거나, 호스트로부터 응답이 없어 연결이 끊어졌습니다'))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    158\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m             conn = connection.create_connection(\n\u001b[0m\u001b[0;32m    160\u001b[0m                 \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dns_host\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mextra_kw\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     83\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     73\u001b[0m                 \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m             \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     75\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTimeoutError\u001b[0m: [WinError 10060] 연결된 구성원으로부터 응답이 없어 연결하지 못했거나, 호스트로부터 응답이 없어 연결이 끊어졌습니다",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mNewConnectionError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    669\u001b[0m             \u001b[1;31m# Make the request on the httplib connection object.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 670\u001b[1;33m             httplib_response = self._make_request(\n\u001b[0m\u001b[0;32m    671\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    380\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 381\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    382\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m    977\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"sock\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# AppEngine might not have  `.sock`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 978\u001b[1;33m             \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    979\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    308\u001b[0m         \u001b[1;31m# Add certificate verification\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 309\u001b[1;33m         \u001b[0mconn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    310\u001b[0m         \u001b[0mhostname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    170\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mSocketError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 171\u001b[1;33m             raise NewConnectionError(\n\u001b[0m\u001b[0;32m    172\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Failed to establish a new connection: %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPSConnection object at 0x00000134607A7E50>: Failed to establish a new connection: [WinError 10060] 연결된 구성원으로부터 응답이 없어 연결하지 못했거나, 호스트로부터 응답이 없어 연결이 끊어졌습니다",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    438\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 439\u001b[1;33m                 resp = conn.urlopen(\n\u001b[0m\u001b[0;32m    440\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    725\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 726\u001b[1;33m             retries = retries.increment(\n\u001b[0m\u001b[0;32m    727\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    445\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 446\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='www.assembly.go.kr', port=443): Max retries exceeded with url: /assm/memPop/memPopup.do?dept_cd=9771189 (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x00000134607A7E50>: Failed to establish a new connection: [WinError 10060] 연결된 구성원으로부터 응답이 없어 연결하지 못했거나, 호스트로부터 응답이 없어 연결이 끊어졌습니다'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-6ac4a9413247>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmember_id\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmember_id_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mdetail_url\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mf'https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd={member_id}'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdetail_url\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'--> 인덱스'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdetail_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0mhtml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'get'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    528\u001b[0m         }\n\u001b[0;32m    529\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    531\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    532\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    641\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    642\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 643\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mSSLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mClosedPoolError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionError\u001b[0m: HTTPSConnectionPool(host='www.assembly.go.kr', port=443): Max retries exceeded with url: /assm/memPop/memPopup.do?dept_cd=9771189 (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x00000134607A7E50>: Failed to establish a new connection: [WinError 10060] 연결된 구성원으로부터 응답이 없어 연결하지 못했거나, 호스트로부터 응답이 없어 연결이 끊어졌습니다'))"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "from urllib.parse import urljoin\n",
    "\n",
    "print('-----스크래핑 시작-----------')\n",
    "\n",
    "# 국회의원 300명의 정보를 저장할 list 선언\n",
    "member_detail_list = []\n",
    "for idx,member_id in enumerate(member_id_list,1):\n",
    "    detail_url = f'https://www.assembly.go.kr/assm/memPop/memPopup.do?dept_cd={member_id}'\n",
    "    res = requests.get(detail_url)\n",
    "    print('--> 인덱스',idx, detail_url, res.status_code)\n",
    "    html = res.text\n",
    "    soup = BeautifulSoup(html,'html.parser')\n",
    "    \n",
    "    #국회의원 한 명의 정보를 저장 dict 선언\n",
    "    member_dict = {}\n",
    "\n",
    "    dt_list = []\n",
    "    for dt_tag in soup.select('div.info_mna dl.pro_detail dt'):\n",
    "        dt_list.append(dt_tag.text)\n",
    "    #print(dt_list)    \n",
    "    \n",
    "    dd_list = []\n",
    "    for dd_tag in soup.select('div.info_mna dl.pro_detail dd'):\n",
    "        # <dd> tag의 텍스트 추출해서 공백을 제거\n",
    "        dd_text = dd_tag.text.strip()\n",
    "        # 정규표현식 Pattern 객체 생성\n",
    "        regex = re.compile(r'[\\n\\r\\t]')\n",
    "        # 매칭 정규표현식 문자열을 empty string으로 변환\n",
    "        result = regex.sub('',dd_text).replace(\" \",\"\")\n",
    "        dd_list.append(result)\n",
    "    #print(dd_list)    \n",
    "    \n",
    "    member_dict = dict(zip(dt_list,dd_list))\n",
    "    \n",
    "    for div_tag in soup.select('.info_mna .profile'):\n",
    "        #print(div_tag)\n",
    "        member_dict['이름'] = div_tag.find('h4').text\n",
    "        img_src = div_tag.select('img')[0]['src']\n",
    "        #img_src = div_tag.find('img').attrs['src']\n",
    "        member_dict['이미지'] = urljoin(detail_url, img_src)    \n",
    "        member_dict['생년월일'] = div_tag.select_one('li:nth-of-type(4)').text\n",
    "        \n",
    "    #print(member_dict)\n",
    "    member_detail_list.append(member_dict)\n",
    "\n",
    "print(len(member_detail_list))\n",
    "print('-----스크래핑 끝-----------')\n",
    "member_detail_list[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('data/member.json','w') as file:\n",
    "    json.dump(member_detail_list, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(246, 15)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "member_df = pd.read_json('data/member.json')\n",
    "print(member_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>정당</th>\n",
       "      <th>선거구</th>\n",
       "      <th>소속위원회</th>\n",
       "      <th>당선횟수</th>\n",
       "      <th>사무실전화</th>\n",
       "      <th>홈페이지</th>\n",
       "      <th>이메일</th>\n",
       "      <th>보좌관</th>\n",
       "      <th>비서관</th>\n",
       "      <th>비서</th>\n",
       "      <th>취미, 특기</th>\n",
       "      <th>의원실안내</th>\n",
       "      <th>이름</th>\n",
       "      <th>이미지</th>\n",
       "      <th>생년월일</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>국민의힘</td>\n",
       "      <td>경남창원시성산구</td>\n",
       "      <td>보건복지위원회</td>\n",
       "      <td>재선(19대,21대)</td>\n",
       "      <td>02-784-1751</td>\n",
       "      <td>http://blog.naver.com/ggotop</td>\n",
       "      <td>ggotop@naver.com</td>\n",
       "      <td>김홍광,한영애</td>\n",
       "      <td>박응서,최광림</td>\n",
       "      <td>김영록,안효상,이유진,홍지형,김지훈</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>강기윤</td>\n",
       "      <td>https://www.assembly.go.kr/photo/9771230.jpg</td>\n",
       "      <td>1960-06-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>국민의힘</td>\n",
       "      <td>대구동구을</td>\n",
       "      <td>국방위원회</td>\n",
       "      <td>초선(21대)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>박홍규,정운태</td>\n",
       "      <td>유진영</td>\n",
       "      <td>박종원,박순권,김광연,김현정,송민욱</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>강대식</td>\n",
       "      <td>https://www.assembly.go.kr/photo/9771142.jpg</td>\n",
       "      <td>1959-11-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     정당       선거구    소속위원회         당선횟수        사무실전화  \\\n",
       "0  국민의힘  경남창원시성산구  보건복지위원회  재선(19대,21대)  02-784-1751   \n",
       "1  국민의힘     대구동구을    국방위원회      초선(21대)                \n",
       "\n",
       "                           홈페이지               이메일      보좌관      비서관  \\\n",
       "0  http://blog.naver.com/ggotop  ggotop@naver.com  김홍광,한영애  박응서,최광림   \n",
       "1                                                  박홍규,정운태      유진영   \n",
       "\n",
       "                    비서 취미, 특기 의원실안내   이름  \\\n",
       "0  김영록,안효상,이유진,홍지형,김지훈               강기윤   \n",
       "1  박종원,박순권,김광연,김현정,송민욱               강대식   \n",
       "\n",
       "                                            이미지        생년월일  \n",
       "0  https://www.assembly.go.kr/photo/9771230.jpg  1960-06-04  \n",
       "1  https://www.assembly.go.kr/photo/9771142.jpg  1959-11-02  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['정당', '선거구', '소속위원회', '당선횟수', '사무실전화', '홈페이지', '이메일', '보좌관', '비서관',\n",
      "       '비서', '취미, 특기', '의원실안내', '이름', '이미지', '생년월일'],\n",
      "      dtype='object')\n",
      "RangeIndex(start=0, stop=246, step=1)\n"
     ]
    }
   ],
   "source": [
    "print(member_df.columns)\n",
    "print(member_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 246 entries, 0 to 245\n",
      "Data columns (total 15 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   정당      246 non-null    object\n",
      " 1   선거구     246 non-null    object\n",
      " 2   소속위원회   246 non-null    object\n",
      " 3   당선횟수    246 non-null    object\n",
      " 4   사무실전화   246 non-null    object\n",
      " 5   홈페이지    246 non-null    object\n",
      " 6   이메일     246 non-null    object\n",
      " 7   보좌관     246 non-null    object\n",
      " 8   비서관     246 non-null    object\n",
      " 9   비서      246 non-null    object\n",
      " 10  취미, 특기  246 non-null    object\n",
      " 11  의원실안내   246 non-null    object\n",
      " 12  이름      246 non-null    object\n",
      " 13  이미지     246 non-null    object\n",
      " 14  생년월일    246 non-null    object\n",
      "dtypes: object(15)\n",
      "memory usage: 29.0+ KB\n"
     ]
    }
   ],
   "source": [
    "member_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['국민의힘', '더불어민주당', '열린민주당', '정의당', '국민의당', '무소속', '기본소득당'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['정당'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "더불어민주당    146\n",
       "국민의힘       80\n",
       "무소속         9\n",
       "정의당         6\n",
       "국민의당        2\n",
       "열린민주당       2\n",
       "기본소득당       1\n",
       "Name: 정당, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['정당'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['재선(19대,21대)', '초선(21대)', '재선(20대,21대)', '4선(18대,19대,20대,21대)',\n",
       "       '4선(16대,17대,18대,21대)', '3선(19대,20대,21대)', '재선(17대,21대)',\n",
       "       '4선(17대,18대,19대,21대)', '3선(15대,16대,21대)', '4선(17대,19대,20대,21대)',\n",
       "       '재선(18대,21대)', '5선(17대,18대,19대,20대,21대)', '3선(18대,19대,21대)',\n",
       "       '6선(16대,17대,18대,19대,20대,21대)', '5선(16대,17대,18대,19대,21대)',\n",
       "       '5선(15대,16대,19대,20대,21대)', '5선(16대,17대,18대,20대,21대)',\n",
       "       '3선(17대,19대,21대)', '3선(17대,18대,21대)', '3선(18대,20대,21대)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['당선횟수'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "초선(21대)                        118\n",
       "재선(20대,21대)                     55\n",
       "3선(19대,20대,21대)                 29\n",
       "4선(17대,19대,20대,21대)              9\n",
       "재선(19대,21대)                      8\n",
       "4선(18대,19대,20대,21대)              5\n",
       "5선(17대,18대,19대,20대,21대)          4\n",
       "재선(18대,21대)                      2\n",
       "3선(18대,20대,21대)                  2\n",
       "5선(16대,17대,18대,20대,21대)          2\n",
       "4선(16대,17대,18대,21대)              2\n",
       "5선(16대,17대,18대,19대,21대)          2\n",
       "6선(16대,17대,18대,19대,20대,21대)      1\n",
       "3선(18대,19대,21대)                  1\n",
       "5선(15대,16대,19대,20대,21대)          1\n",
       "4선(17대,18대,19대,21대)              1\n",
       "3선(17대,19대,21대)                  1\n",
       "3선(15대,16대,21대)                  1\n",
       "3선(17대,18대,21대)                  1\n",
       "재선(17대,21대)                      1\n",
       "Name: 당선횟수, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['당선횟수'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "비례대표              47\n",
       "전남담양군함평군영광군장성군     1\n",
       "울산동구               1\n",
       "경기화성시갑             1\n",
       "경기성남시수정구           1\n",
       "                  ..\n",
       "대구동구을              1\n",
       "광주서구을              1\n",
       "서울송파구갑             1\n",
       "전남목포시              1\n",
       "부산남구을              1\n",
       "Name: 선거구, Length: 254, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['선거구'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.strings.StringMethods'>\n"
     ]
    },
    {
     "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>당선횟수</th>\n",
       "      <th>당선횟수2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>재선(19대,21대)</td>\n",
       "      <td>재선</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>초선(21대)</td>\n",
       "      <td>초선</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>초선(21대)</td>\n",
       "      <td>초선</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>초선(21대)</td>\n",
       "      <td>초선</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>초선(21대)</td>\n",
       "      <td>초선</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          당선횟수 당선횟수2\n",
       "0  재선(19대,21대)    재선\n",
       "1      초선(21대)    초선\n",
       "2      초선(21대)    초선\n",
       "3      초선(21대)    초선\n",
       "4      초선(21대)    초선"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 당선횟수2 라는 컬럼 추가\n",
    "# 재선(20대,21대) -> 재선\n",
    "print(type(member_df['당선횟수']))\n",
    "print(type(member_df['당선횟수'].str))\n",
    "\n",
    "#Series 객체를 StringMethods 객체로 변환(문자열 슬라이싱을 하기 위해서)\n",
    "temp_str = member_df['당선횟수'].str\n",
    "#2글자를 슬라이싱해서 당선횟수2 라는 컬럼을 새로 생성\n",
    "member_df['당선횟수2'] = temp_str[:2]\n",
    "\n",
    "member_df.loc[:,['당선횟수','당선횟수2']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "초선    151\n",
       "재선     74\n",
       "3선     42\n",
       "4선     19\n",
       "5선     13\n",
       "6선      1\n",
       "Name: 당선횟수2, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['당선횟수2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>정당</th>\n",
       "      <th>선거구</th>\n",
       "      <th>소속위원회</th>\n",
       "      <th>당선횟수</th>\n",
       "      <th>사무실전화</th>\n",
       "      <th>홈페이지</th>\n",
       "      <th>이메일</th>\n",
       "      <th>보좌관</th>\n",
       "      <th>비서관</th>\n",
       "      <th>비서</th>\n",
       "      <th>취미, 특기</th>\n",
       "      <th>의원실안내</th>\n",
       "      <th>이름</th>\n",
       "      <th>이미지</th>\n",
       "      <th>생년월일</th>\n",
       "      <th>당선횟수2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>무소속</td>\n",
       "      <td>대전서구갑</td>\n",
       "      <td></td>\n",
       "      <td>6선(16대,17대,18대,19대,20대,21대)</td>\n",
       "      <td>02-788-2306</td>\n",
       "      <td></td>\n",
       "      <td>pbs2100@gmail.com</td>\n",
       "      <td>강창희,송석근</td>\n",
       "      <td>예석해,최명식</td>\n",
       "      <td>김혜리,김민숙,유민선,심용진,최지연</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>박병석</td>\n",
       "      <td>https://www.assembly.go.kr/photo/9770090.jpg</td>\n",
       "      <td>1952-01-25</td>\n",
       "      <td>6선</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     정당    선거구 소속위원회                         당선횟수        사무실전화 홈페이지  \\\n",
       "92  무소속  대전서구갑        6선(16대,17대,18대,19대,20대,21대)  02-788-2306        \n",
       "\n",
       "                  이메일      보좌관      비서관                   비서 취미, 특기 의원실안내  \\\n",
       "92  pbs2100@gmail.com  강창희,송석근  예석해,최명식  김혜리,김민숙,유민선,심용진,최지연                \n",
       "\n",
       "     이름                                           이미지        생년월일 당선횟수2  \n",
       "92  박병석  https://www.assembly.go.kr/photo/9770090.jpg  1952-01-25    6선  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#당선횟수2가 6선은 누구?\n",
    "member_df.loc[member_df['당선횟수2'] == '6선']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>선거구</th>\n",
       "      <th>선거구2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>경남창원시성산구</td>\n",
       "      <td>경남</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>대구동구을</td>\n",
       "      <td>대구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>경기안양시만안구</td>\n",
       "      <td>경기</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>경남진주시을</td>\n",
       "      <td>경남</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>비례대표</td>\n",
       "      <td>비례</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        선거구 선거구2\n",
       "0  경남창원시성산구   경남\n",
       "1     대구동구을   대구\n",
       "2  경기안양시만안구   경기\n",
       "3    경남진주시을   경남\n",
       "4      비례대표   비례"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#선거구2 라는 컬럼을 추가\n",
    "#전남여수시갑 -> 전남\n",
    "\n",
    "temp_str = member_df['선거구'].str\n",
    "member_df['선거구2'] = temp_str[:2]\n",
    "\n",
    "member_df.loc[:,['선거구','선거구2']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "경기    59\n",
       "서울    49\n",
       "비례    47\n",
       "부산    18\n",
       "경남    16\n",
       "경북    13\n",
       "인천    13\n",
       "대구    12\n",
       "충남    11\n",
       "전북    10\n",
       "전남    10\n",
       "강원     8\n",
       "충북     8\n",
       "광주     8\n",
       "대전     7\n",
       "울산     6\n",
       "제주     3\n",
       "세종     2\n",
       "Name: 선거구2, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['선거구2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300\n",
      "0.19666666666666666\n"
     ]
    }
   ],
   "source": [
    "#print(member_df['선거구'].sum())\n",
    "print(member_df['선거구'].count())\n",
    "print(59/300) # 경기도 59 / 전체 300 => 비율"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'member_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-bf93afd6eb83>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#value_counts(normalize=True)는 전체 건수에서 비율을 표시해준다.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmember_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'선거구2'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# => 전체 건수의 비율이 나온다.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'member_df' is not defined"
     ]
    }
   ],
   "source": [
    "#value_counts(normalize=True)는 전체 건수에서 비율을 표시해준다.\n",
    "member_df['선거구2'].value_counts(normalize=True) # => 전체 건수의 비율이 나온다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1960-06-04\n",
       "1    1959-11-02\n",
       "2    1963-05-27\n",
       "3    1971-03-03\n",
       "4    1961-04-26\n",
       "Name: 생년월일, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(member_df['생년월일'].dtypes)\n",
    "#print(pd.DatetimeIndex(member_df['생년월일']))\n",
    "member_df['생년월일'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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>생년월일</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960-06-04</td>\n",
       "      <td>1960</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959-11-02</td>\n",
       "      <td>1959</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1963-05-27</td>\n",
       "      <td>1963</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1971-03-03</td>\n",
       "      <td>1971</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1961-04-26</td>\n",
       "      <td>1961</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         생년월일  year  month  day\n",
       "0  1960-06-04  1960      6    4\n",
       "1  1959-11-02  1959     11    2\n",
       "2  1963-05-27  1963      5   27\n",
       "3  1971-03-03  1971      3    3\n",
       "4  1961-04-26  1961      4   26"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DatetimeIndex 객체를 사용해서 생년월일 컬럼의 값들을 year,month,day로 각각 추출한다 \n",
    "#year, month, day 3개의 컬럼을 추가\n",
    "member_df['year'] = pd.DatetimeIndex(member_df['생년월일']).year\n",
    "member_df['month'] = pd.DatetimeIndex(member_df['생년월일']).month\n",
    "member_df['day'] = pd.DatetimeIndex(member_df['생년월일']).day\n",
    "\n",
    "member_df.loc[:,['생년월일','year','month','day']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(member_df['생년월일']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>이름</th>\n",
       "      <th>정당</th>\n",
       "      <th>year</th>\n",
       "      <th>선거구</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>류호정</td>\n",
       "      <td>정의당</td>\n",
       "      <td>1992</td>\n",
       "      <td>비례대표</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     이름   정당  year   선거구\n",
       "80  류호정  정의당  1992  비례대표"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#가장 나이가 어린, 많은 사람은?\n",
    "member_df.loc[member_df['year'] == member_df['year'].max(),['이름','정당','year','선거구']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>이름</th>\n",
       "      <th>정당</th>\n",
       "      <th>year</th>\n",
       "      <th>선거구</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>김진표</td>\n",
       "      <td>더불어민주당</td>\n",
       "      <td>1947</td>\n",
       "      <td>경기수원시무</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>홍문표</td>\n",
       "      <td>국민의힘</td>\n",
       "      <td>1947</td>\n",
       "      <td>충남홍성군예산군</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      이름      정당  year       선거구\n",
       "65   김진표  더불어민주당  1947    경기수원시무\n",
       "290  홍문표    국민의힘  1947  충남홍성군예산군"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.loc[member_df['year'] == member_df['year'].min(),['이름','정당','year','선거구']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 나이 계산하는 함수 선언\n",
    "from datetime import date\n",
    "\n",
    "def calc_age(birth):\n",
    "    #현재날짜\n",
    "    today = date.today()\n",
    "    age = (today.year - birth.year) - ((today.month, today.day) < (birth.month, birth.day))\n",
    "    return age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_age(date(2005,1,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[60, 60, 57, 49, 59]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#행별로 년,월,일 컬럼의 값으로 나이를 계산하고\n",
    "#계산된 나이값을 age_list에 저장하기\n",
    "age_list = []\n",
    "for idx,row in member_df.iterrows(): # 행의 갯수만큼 반복\n",
    "    age = calc_age(date(row['year'],row['month'],row['day']))\n",
    "    age_list.append(age)\n",
    "\n",
    "print(len(age_list))    \n",
    "age_list[0:5]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>생년월일</th>\n",
       "      <th>나이</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>1978-11-24</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>1954-12-05</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>1976-08-05</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1962-09-10</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1967-07-28</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           생년월일  나이\n",
       "295  1978-11-24  41\n",
       "296  1954-12-05  65\n",
       "297  1976-08-05  44\n",
       "298  1962-09-10  58\n",
       "299  1967-07-28  53"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#나이 컬럼 추가\n",
    "member_df['나이'] = age_list\n",
    "member_df.loc[:,['생년월일','나이']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    300.000000\n",
       "mean      55.506667\n",
       "std        7.384560\n",
       "min       28.000000\n",
       "25%       52.000000\n",
       "50%       56.000000\n",
       "75%       60.000000\n",
       "max       73.000000\n",
       "Name: 나이, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#나이 최대,평균,최소,표준편차\n",
    "member_df['나이'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['정당', '선거구', '소속위원회', '당선횟수', '사무실전화', '홈페이지', '이메일', '보좌관', '비서관',\n",
       "       '비서', '취미, 특기', '의원실안내', '이름', '이미지', '생년월일', '당선횟수2', '선거구2', 'year',\n",
       "       'month', 'day', '나이'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    https://www.assembly.go.kr/photo/9771230.jpg\n",
       "1    https://www.assembly.go.kr/photo/9771142.jpg\n",
       "2    https://www.assembly.go.kr/photo/9771174.jpg\n",
       "3    https://www.assembly.go.kr/photo/9771233.jpg\n",
       "4    https://www.assembly.go.kr/photo/9771283.jpg\n",
       "Name: 이미지, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['이미지'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 시각화\n",
    "* 이미지 출력 - IPython에서 제공하는 Image 객체, display() 함수 사용\n",
    "* seaborn - count plot(막대그래프), distplot(히스토그램,분포도)\n",
    "* matplotlib - histogram, pie chart\n",
    "* 한글폰트 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.assembly.go.kr/photo/9771224.jpg\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.assembly.go.kr/photo/9771224.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.assembly.go.kr/photo/9770934.jpg\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.assembly.go.kr/photo/9770934.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.assembly.go.kr/photo/9771207.jpg\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.assembly.go.kr/photo/9771207.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "\n",
    "for image_url in member_df['이미지'].sample(3):\n",
    "    print(image_url)\n",
    "    #display(Image(url=image_url))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "import seaborn as sns\n",
    "\n",
    "#sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#한글폰트 설정\n",
    "font_path = 'c:/windows/fonts/malgun.ttf'\n",
    "font_prop = fm.FontProperties(fname=font_path).get_name()\n",
    "#matplotlib의 rc(run command) 함수를 사용해서 한글폰트 설정\n",
    "matplotlib.rc('font', family=font_prop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### seaborn의 막대그래프\n",
    "* barplot - x축, y축을 둘 다 설정할 수 있음\n",
    "* countplot - x축 이나 y축 중에서 하나만 설정할 수 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "더불어민주당    174\n",
       "국민의힘      103\n",
       "무소속         9\n",
       "정의당         6\n",
       "열린민주당       3\n",
       "국민의당        3\n",
       "기본소득당       1\n",
       "시대전환        1\n",
       "Name: 정당, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['정당'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['더불어민주당', '국민의힘', '무소속', '정의당', '열린민주당', '국민의당', '기본소득당', '시대전환'], dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['정당'].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28853fee2b0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#figure와 axes 객체 생성\n",
    "figure,(axes1,axes2) = plt.subplots(nrows=2, ncols=1)\n",
    "figure.set_size_inches(18,12)\n",
    "\n",
    "#정당과 당선횟수2 컬럼을 row count 것을 시각화\n",
    "sns.countplot(data=member_df, x='정당', ax=axes1, order=member_df['정당'].value_counts().index)\n",
    "sns.countplot(data=member_df, x='당선횟수2', ax=axes2, \\\n",
    "              order=member_df['당선횟수2'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28854524df0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#선거구2 컬럼의 값을 countplot으로 그리기\n",
    "#figure에 axes 객체를 1개로 설정\n",
    "\n",
    "figure, axes1 = plt.subplots(nrows=1, ncols=1)\n",
    "figure.set_size_inches(18,12)\n",
    "sns.countplot(data=member_df, y='선거구2', ax=axes1, \\\n",
    "              order=member_df['선거구2'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28854173ca0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#산점도 seaborn의 scatterplot 를 사용 x y만나는 부분에 점을 찍는거야\n",
    "#선거구2 와 나이 분포도 를 확인\n",
    "figure, (axes1,axes2) = plt.subplots(nrows=2, ncols=1)\n",
    "figure.set_size_inches(18,12)\n",
    "sns.scatterplot(data=member_df, x='선거구2', y='나이', ax=axes1)\n",
    "sns.scatterplot(data=member_df, x='정당', y='나이', ax=axes2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'나이'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '나이'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-7414a894f4ca>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mncols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_size_inches\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmember_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'나이'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhist\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxes1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# false하면 막대없어짐\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;31m#sns.distplot(member_df['나이'], hist=True)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '나이'"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 나이 값의 분포를 볼 수 있는 히스토그램 그릭\n",
    "# seaborn의 distplot() 함수 사용\n",
    "figure, axes1 = plt.subplots(nrows=1, ncols=1)\n",
    "figure.set_size_inches(18,12)\n",
    "sns.distplot(member_df['나이'], hist=True, ax=axes1) # false하면 막대없어짐\n",
    "#sns.distplot(member_df['나이'], hist=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'나이'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '나이'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-460340044b3f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mage_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmember_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmember_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'나이'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m35\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mmember_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'나이'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m65\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mage_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mncols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_size_inches\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mage_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'나이'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhist\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxes1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '나이'"
     ]
    }
   ],
   "source": [
    "age_df = member_df.loc[(member_df['나이'] > 35) & (member_df['나이'] < 65)]\n",
    "len(age_df)\n",
    "figure, axes1 = plt.subplots(nrows=1, ncols=1)\n",
    "figure.set_size_inches(18,12)\n",
    "sns.distplot(age_df['나이'], hist=True, ax=axes1)\n",
    "#sns.distplot(age_df['나이'], hist=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'나이'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '나이'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-ae04331ef66c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Matplotlib 를 사용하여 Histogram 그리기\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpatches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmember_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'나이'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpatches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2900\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2902\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2903\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '나이'"
     ]
    }
   ],
   "source": [
    "#Matplotlib 를 사용하여 Histogram 그리기\n",
    "arrays,bins,patches = plt.hist(member_df['나이'], bins=10)\n",
    "print(arrays)\n",
    "print(bins)\n",
    "print(patches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['경기', '서울', '비례', '부산', '경남', '경북', '인천', '대구', '충남', '전북', '전남', '강원',\n",
      "       '충북', '광주', '대전', '울산', '제주', '세종'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "경기    0.196667\n",
       "서울    0.163333\n",
       "비례    0.156667\n",
       "부산    0.060000\n",
       "경남    0.053333\n",
       "경북    0.043333\n",
       "인천    0.043333\n",
       "대구    0.040000\n",
       "충남    0.036667\n",
       "전북    0.033333\n",
       "전남    0.033333\n",
       "강원    0.026667\n",
       "충북    0.026667\n",
       "광주    0.026667\n",
       "대전    0.023333\n",
       "울산    0.020000\n",
       "제주    0.010000\n",
       "세종    0.006667\n",
       "Name: 선거구2, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# row count를 퍼센티지(%) 비율로 나타내려면 value_counts(normalize=True) 로 설정\n",
    "cdf = member_df['선거구2'].value_counts(normalize=True)\n",
    "print(cdf.index)\n",
    "cdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '선거구 분포값')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Matplotlib의 pie plot 그리기\n",
    "\n",
    "#figure size 조정\n",
    "figure = plt.figure(figsize=(20,12))\n",
    "#autopct는 값의 퍼센티지 포맷지정\n",
    "#startangle은 첫번째 pie의 시작각도 지정\n",
    "plt.pie(cdf, labels=cdf.index, autopct='%1.1f%%', startangle=140, shadow=True)\n",
    "#pie plot을 그릴때 원의 형태를 유지하도록 하는 설정\n",
    "plt.axis('equal')\n",
    "plt.title('선거구 분포값')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 피벗 테이블 - 표의 데이터를 요약하는 통계표\n",
    "# pivot_table(index, colums,aggfunc)\n",
    "# index - 행의 위치에 들어갈 열(컬럼명), colums - 열의 위치에 들어갈 컬럼명\n",
    "# aggfunc - 집계 함수\n",
    "member_df.pivot_table(index = '나이', columns='정당', aggfunc='size').fillna(0).astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>정당</th>\n",
       "      <th>국민의당</th>\n",
       "      <th>국민의힘</th>\n",
       "      <th>기본소득당</th>\n",
       "      <th>더불어민주당</th>\n",
       "      <th>무소속</th>\n",
       "      <th>시대전환</th>\n",
       "      <th>열린민주당</th>\n",
       "      <th>정의당</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>나이</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "정당  국민의당  국민의힘  기본소득당  더불어민주당  무소속  시대전환  열린민주당  정의당\n",
       "나이                                                  \n",
       "28     0     0      0       0    0     0      0    1\n",
       "29     0     0      0       1    0     0      0    0\n",
       "30     0     0      1       0    0     0      0    0\n",
       "32     0     0      0       1    0     0      0    0\n",
       "33     0     0      0       0    0     0      0    1"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pivot_table 함수 사용\n",
    "age_pivot_df=member_df.pivot_table(index='나이',columns='정당',aggfunc='size').fillna(0).astype(int)\n",
    "#.fillna(0).astype(int)\n",
    "age_pivot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28855052100>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#seaborn의 heatmap 그리기\n",
    "sns.heatmap(age_pivot_df, linewidths=1, annot=True, fmt='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#나이구간 컬럼을 추가\n",
    "#print(member_df['나이'].value_counts())\n",
    "member_df.loc[member_df['나이'] < 30,'나이구간'] = 20\n",
    "member_df.loc[(member_df['나이'] >= 30) & (member_df['나이'] < 40),'나이구간'] = 30\n",
    "member_df.loc[(member_df['나이'] >= 40) & (member_df['나이'] < 50),'나이구간'] = 40\n",
    "member_df.loc[(member_df['나이'] >= 50) & (member_df['나이'] < 60),'나이구간'] = 50\n",
    "member_df.loc[(member_df['나이'] >= 60) & (member_df['나이'] < 70),'나이구간'] = 60\n",
    "member_df.loc[member_df['나이'] >= 70,'나이구간'] = 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50    169\n",
       "60     80\n",
       "40     35\n",
       "30     11\n",
       "70      3\n",
       "20      2\n",
       "Name: 나이구간, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['나이구간'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 나이구간 컬럼의 타입을 변경 float -> int \n",
    "member_df = member_df.astype({\"나이구간\":int})\n",
    "member_df['나이구간'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>정당</th>\n",
       "      <th>국민의당</th>\n",
       "      <th>국민의힘</th>\n",
       "      <th>기본소득당</th>\n",
       "      <th>더불어민주당</th>\n",
       "      <th>무소속</th>\n",
       "      <th>시대전환</th>\n",
       "      <th>열린민주당</th>\n",
       "      <th>정의당</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>나이구간</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "정당    국민의당  국민의힘  기본소득당  더불어민주당  무소속  시대전환  열린민주당  정의당\n",
       "나이구간                                                  \n",
       "20       0     0      0       1    0     0      0    1\n",
       "30       0     3      1       6    0     0      0    1\n",
       "40       1    12      0      21    0     1      0    0\n",
       "50       1    48      0     110    5     0      2    3\n",
       "60       1    39      0      34    4     0      1    1\n",
       "70       0     1      0       2    0     0      0    0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_pivot_df=member_df.pivot_table(index='나이구간',columns='정당',aggfunc='size')\\\n",
    ".fillna(0).astype(int)\n",
    "age_pivot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2885596f190>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(age_pivot_df, linewidths=1, annot=True, fmt='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>선거구2</th>\n",
       "      <th>강원</th>\n",
       "      <th>경기</th>\n",
       "      <th>경남</th>\n",
       "      <th>경북</th>\n",
       "      <th>광주</th>\n",
       "      <th>대구</th>\n",
       "      <th>대전</th>\n",
       "      <th>부산</th>\n",
       "      <th>비례</th>\n",
       "      <th>서울</th>\n",
       "      <th>세종</th>\n",
       "      <th>울산</th>\n",
       "      <th>인천</th>\n",
       "      <th>전남</th>\n",
       "      <th>전북</th>\n",
       "      <th>제주</th>\n",
       "      <th>충남</th>\n",
       "      <th>충북</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>나이구간</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "선거구2   강원    경기   경남   경북   광주   대구   대전    부산    비례    서울   세종   울산   인천  \\\n",
       "나이구간                                                                        \n",
       "20    NaN   NaN  NaN  NaN  NaN  NaN  NaN   NaN   2.0   NaN  NaN  NaN  NaN   \n",
       "30    NaN   3.0  NaN  NaN  NaN  NaN  1.0   NaN   5.0   2.0  NaN  NaN  NaN   \n",
       "40    NaN  11.0  1.0  3.0  NaN  NaN  NaN   3.0   9.0   6.0  NaN  NaN  NaN   \n",
       "50    5.0  33.0  9.0  7.0  7.0  6.0  4.0  13.0  18.0  29.0  2.0  2.0  9.0   \n",
       "60    3.0  11.0  6.0  3.0  1.0  6.0  2.0   2.0  13.0  12.0  NaN  4.0  4.0   \n",
       "70    NaN   1.0  NaN  NaN  NaN  NaN  NaN   NaN   NaN   NaN  NaN  NaN  NaN   \n",
       "\n",
       "선거구2   전남   전북   제주   충남   충북  \n",
       "나이구간                           \n",
       "20    NaN  NaN  NaN  NaN  NaN  \n",
       "30    NaN  NaN  NaN  NaN  NaN  \n",
       "40    NaN  NaN  NaN  2.0  NaN  \n",
       "50    5.0  9.0  3.0  6.0  2.0  \n",
       "60    5.0  1.0  NaN  2.0  5.0  \n",
       "70    NaN  NaN  NaN  1.0  1.0  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.pivot_table(index='나이구간',columns='선거구2',aggfunc='size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>나이구간</th>\n",
       "      <th>20</th>\n",
       "      <th>30</th>\n",
       "      <th>40</th>\n",
       "      <th>50</th>\n",
       "      <th>60</th>\n",
       "      <th>70</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>선거구2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강원</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경기</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경남</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경북</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대전</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>비례</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>세종</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>울산</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전남</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전북</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충남</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "나이구간   20   30    40    50    60   70\n",
       "선거구2                                 \n",
       "강원    NaN  NaN   NaN   5.0   3.0  NaN\n",
       "경기    NaN  3.0  11.0  33.0  11.0  1.0\n",
       "경남    NaN  NaN   1.0   9.0   6.0  NaN\n",
       "경북    NaN  NaN   3.0   7.0   3.0  NaN\n",
       "광주    NaN  NaN   NaN   7.0   1.0  NaN\n",
       "대구    NaN  NaN   NaN   6.0   6.0  NaN\n",
       "대전    NaN  1.0   NaN   4.0   2.0  NaN\n",
       "부산    NaN  NaN   3.0  13.0   2.0  NaN\n",
       "비례    2.0  5.0   9.0  18.0  13.0  NaN\n",
       "서울    NaN  2.0   6.0  29.0  12.0  NaN\n",
       "세종    NaN  NaN   NaN   2.0   NaN  NaN\n",
       "울산    NaN  NaN   NaN   2.0   4.0  NaN\n",
       "인천    NaN  NaN   NaN   9.0   4.0  NaN\n",
       "전남    NaN  NaN   NaN   5.0   5.0  NaN\n",
       "전북    NaN  NaN   NaN   9.0   1.0  NaN\n",
       "제주    NaN  NaN   NaN   3.0   NaN  NaN\n",
       "충남    NaN  NaN   2.0   6.0   2.0  1.0\n",
       "충북    NaN  NaN   NaN   2.0   5.0  1.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.pivot_table(index='선거구2',columns='나이구간',aggfunc='size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['경남', '대구', '경기', '비례', '서울', '세종', '충남', '경북', '울산', '강원', '인천',\n",
       "       '부산', '전북', '전남', '충북', '광주', '대전', '제주'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(member_df['선거구2'].unique()))\n",
    "member_df['선거구2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16     강원\n",
       "128    강원\n",
       "133    제주\n",
       "154    제주\n",
       "158    제주\n",
       "162    강원\n",
       "183    강원\n",
       "198    강원\n",
       "216    강원\n",
       "281    강원\n",
       "286    강원\n",
       "Name: 선거구2, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#서울, 경기, 인천 -> 수도권 (3)\n",
    "#충남,충북,대전,세종 -> 충청 (4)\n",
    "#경남,경북,대구,울산,부산 -> 경상 (5)\n",
    "#광주,전남,전북 -> 전라 (3)\n",
    "#비례 (1)\n",
    "#강원 (1)\n",
    "#제주 (1)\n",
    "\n",
    "#isin()\n",
    "#선거구2 값이 강원이거나 제주인 행을 선택하고, 선거구2 컬럼만 선택\n",
    "member_df.loc[member_df['선거구2'].isin(['강원','제주']),'선거구2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "member_df.loc[member_df['선거구2'].isin(['서울','경기','인천']),'선거구3'] = '수도권'\n",
    "member_df.loc[member_df['선거구2'].isin(['충남','충북','대전','세종']),\\\n",
    "              '선거구3'] = '충청권'\n",
    "member_df.loc[member_df['선거구2'].isin(['경남','경북','대구','울산','부산']),\\\n",
    "              '선거구3'] = '경상권'\n",
    "member_df.loc[member_df['선거구2'].isin(['광주','전남','전북']),\\\n",
    "              '선거구3'] = '전라권'\n",
    "member_df.loc[member_df['선거구2'] == '강원','선거구3'] = '강원'\n",
    "member_df.loc[member_df['선거구2'] == '제주','선거구3'] = '제주'\n",
    "member_df.loc[member_df['선거구2'] == '비례','선거구3'] = '비례'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['선거구3'].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>나이구간</th>\n",
       "      <th>20</th>\n",
       "      <th>30</th>\n",
       "      <th>40</th>\n",
       "      <th>50</th>\n",
       "      <th>60</th>\n",
       "      <th>70</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>선거구3</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강원</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경상권</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>37</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>비례</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>수도권</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>71</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전라권</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충청권</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "나이구간  20  30  40  50  60  70\n",
       "선거구3                        \n",
       "강원     0   0   0   5   3   0\n",
       "경상권    0   0   7  37  21   0\n",
       "비례     2   5   9  18  13   0\n",
       "수도권    0   5  17  71  27   1\n",
       "전라권    0   0   0  21   7   0\n",
       "제주     0   0   0   3   0   0\n",
       "충청권    0   1   2  14   9   2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_pivot_df2 = member_df.pivot_table(index='선거구3',columns='나이구간',aggfunc='size').fillna(0).astype(int)\n",
    "age_pivot_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x288559d7a30>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(age_pivot_df2, annot=True, fmt='d', cmap=sns.light_palette('red'),\\\n",
    "            linewidths=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28855b0ea30>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(age_pivot_df2, annot=True, fmt='d',linewidths=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80    류호정\n",
       "Name: 이름, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.loc[member_df['취미, 특기'] == '게임','이름']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                               224\n",
       "독서                              17\n",
       "등산                              10\n",
       "독서,등산                            4\n",
       "등산,독서                            3\n",
       "음악감상                             2\n",
       "배드민턴                             2\n",
       "축구                               2\n",
       "탁구                               2\n",
       "바둑                               2\n",
       "독서,걷기                            1\n",
       "명상                               1\n",
       "독서노래기타                           1\n",
       "바둑,독서,산책                         1\n",
       "독서,기타연주                          1\n",
       "독서,탁구                            1\n",
       "원예,요리                            1\n",
       "독서,영화,등산                         1\n",
       "독서,음악감상,기타연주                     1\n",
       "궁도,배드민턴                          1\n",
       "게임                               1\n",
       "정치,정치                            1\n",
       "바둑,낚시                            1\n",
       "등산,족구,배드민턴,축구                    1\n",
       "등산,독서/운동                         1\n",
       "등산,축구                            1\n",
       "걷기,독서                            1\n",
       "캠핑,요리,독서                         1\n",
       "등산,역도                            1\n",
       "영화감상,여행                          1\n",
       "수영.검도                            1\n",
       "바둑(아마5단),등산,테니스,검도(공인2단),역사      1\n",
       "캠핑,등산/독서                         1\n",
       "독서,명상                            1\n",
       "독서,글쓰기                           1\n",
       "독서,운동                            1\n",
       "사진                               1\n",
       "노래,기타연주                          1\n",
       "여행                               1\n",
       "독서,배드민턴                          1\n",
       "등산,배드민턴                          1\n",
       "매력발산(a.k.a심블리,심크러쉬,심장군,심별명)      1\n",
       "Name: 취미, 특기, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['취미, 특기'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>이름</th>\n",
       "      <th>정당</th>\n",
       "      <th>나이</th>\n",
       "      <th>취미, 특기</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>한준호</td>\n",
       "      <td>더불어민주당</td>\n",
       "      <td>46</td>\n",
       "      <td>수영.검도</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      이름      정당  나이 취미, 특기\n",
       "285  한준호  더불어민주당  46  수영.검도"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#like search\n",
    "member_df.loc[member_df['취미, 특기'].str.contains('수영'),\\\n",
    "              ['이름','정당','나이','취미, 특기']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['정당', '선거구', '소속위원회', '당선횟수', '사무실전화', '홈페이지', '이메일', '보좌관', '비서관',\n",
       "       '비서', '취미, 특기', '의원실안내', '이름', '이미지', '생년월일', '당선횟수2', '선거구2', 'year',\n",
       "       'month', 'day', '나이', '나이구간', '선거구3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### DB에 저장\n",
    "member_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['이름', '나이', '이미지', '정당', '선거구2', '당선횟수2', '소속위원회', '사무실전화', '홈페이지',\n",
      "       '이메일', '보좌관', '비서관', '비서', '취미, 특기'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>이름</th>\n",
       "      <th>나이</th>\n",
       "      <th>이미지</th>\n",
       "      <th>정당</th>\n",
       "      <th>선거구2</th>\n",
       "      <th>당선횟수2</th>\n",
       "      <th>소속위원회</th>\n",
       "      <th>사무실전화</th>\n",
       "      <th>홈페이지</th>\n",
       "      <th>이메일</th>\n",
       "      <th>보좌관</th>\n",
       "      <th>비서관</th>\n",
       "      <th>비서</th>\n",
       "      <th>취미, 특기</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>강기윤</td>\n",
       "      <td>60</td>\n",
       "      <td>https://www.assembly.go.kr/photo/9771230.jpg</td>\n",
       "      <td>국민의힘</td>\n",
       "      <td>경남</td>\n",
       "      <td>재선</td>\n",
       "      <td>보건복지위원회</td>\n",
       "      <td>02-784-1751</td>\n",
       "      <td>http://blog.naver.com/ggotop</td>\n",
       "      <td>ggotop@naver.com</td>\n",
       "      <td>김홍광,한영애</td>\n",
       "      <td>박응서,최광림</td>\n",
       "      <td>김영록,안효상,이유진,홍지형,김지훈</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>강대식</td>\n",
       "      <td>60</td>\n",
       "      <td>https://www.assembly.go.kr/photo/9771142.jpg</td>\n",
       "      <td>국민의힘</td>\n",
       "      <td>대구</td>\n",
       "      <td>초선</td>\n",
       "      <td>국방위원회</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>박홍규,정운태</td>\n",
       "      <td>유진영</td>\n",
       "      <td>박종원,박순권,김광연,김현정,송민욱</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    이름  나이                                           이미지    정당 선거구2 당선횟수2  \\\n",
       "0  강기윤  60  https://www.assembly.go.kr/photo/9771230.jpg  국민의힘   경남    재선   \n",
       "1  강대식  60  https://www.assembly.go.kr/photo/9771142.jpg  국민의힘   대구    초선   \n",
       "\n",
       "     소속위원회        사무실전화                          홈페이지               이메일  \\\n",
       "0  보건복지위원회  02-784-1751  http://blog.naver.com/ggotop  ggotop@naver.com   \n",
       "1    국방위원회                                                                \n",
       "\n",
       "       보좌관      비서관                   비서 취미, 특기  \n",
       "0  김홍광,한영애  박응서,최광림  김영록,안효상,이유진,홍지형,김지훈         \n",
       "1  박홍규,정운태      유진영  박종원,박순권,김광연,김현정,송민욱         "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_df = member_df.loc[:,['이름','나이','이미지','정당','선거구2','당선횟수2','소속위원회','사무실전화', '홈페이지', '이메일', '보좌관', '비서관',\n",
    "       '비서', '취미, 특기']]\n",
    "print(table_df.columns)\n",
    "table_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['이름', '나이', '이미지', '정당', '선거구', '당선횟수', '소속위원회', '사무실전화', '홈페이지', '이메일',\n",
       "       '보좌관', '비서관', '비서', '취미, 특기'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#컬럼명 변경\n",
    "table_df = table_df.rename(columns={'선거구2':'선거구','당선횟수2':'당선횟수'})\n",
    "table_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "            ...\n",
       "            291, 292, 293, 294, 295, 296, 297, 298, 299, 300],\n",
       "           dtype='int64', length=300)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#인덱스를 1부터 시작하도록 변경\n",
    "import numpy as np\n",
    "\n",
    "table_df.index = np.arange(1, len(table_df)+1)\n",
    "table_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Engine(mysql+mysqldb://python:***@localhost:3306/python_db)\n",
      "<sqlalchemy.engine.base.Connection object at 0x0000028856B62E50>\n"
     ]
    }
   ],
   "source": [
    "import pymysql\n",
    "import sqlalchemy\n",
    "\n",
    "#pymysql과 sqlalchemy 를 연동\n",
    "pymysql.install_as_MySQLdb()\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "#'mysql+mysqldb://scott:tiger@localhost/foo'\n",
    "#Engine 객체 생성\n",
    "engine = create_engine('mysql+mysqldb://python:python@localhost:3306/python_db',\\\n",
    "                      encoding='utf-8')\n",
    "print(engine)\n",
    "#Engine을 사용해서 DB에 연결 \n",
    "con = engine.connect()\n",
    "print(con)\n",
    "\n",
    "#DataFrame의 to_sql() 함수로 dataframe 객체를 Table로 저정\n",
    "table_df.to_sql(name='member', con=engine, if_exists='replace',\\\n",
    "                index=True, index_label='id')\n",
    "\n",
    "con.close()"
   ]
  }
 ],
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