05.국회의원현황-개정판.ipynb

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   "cell_type": "markdown",
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   "source": [
    "### 국회의원 현황정보 수집/분석/시각화/저장\n",
    "* 이름과 ID 추출\n",
    "* 상세페이지 정보 추출 (json으로 저장)\n",
    "* 상세정보들을 DataFrame으로 변환\n",
    "* 시각화(막대그래프,히스토그램, 파이챠트, 히트맵)\n",
    "* 테이블로 저장"
   ]
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     "text": [
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=1\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=2\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=3\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=4\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=5\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=6\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=7\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=8\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=9\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=10\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=11\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=12\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=13\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=14\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=15\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=16\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=17\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=18\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=19\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=20\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=21\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=22\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=23\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=24\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=25\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=26\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=27\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=28\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=29\n",
      "https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex=30\n",
      "300\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "\n",
    "headers = {\n",
    "    'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36'\n",
    "}\n",
    "\n",
    "id_list_300 = []\n",
    "for index in range(1,31):\n",
    "    url = f'https://www.assembly.go.kr/portal/cnts/cntsNaas/findNaasThx01.json?pageIndex={index}'\n",
    "    print(url)\n",
    "    res = requests.get(url, headers=headers)\n",
    "    #print(res.status_code)\n",
    "    if res.ok:\n",
    "        #print(res.json()['resultList'])\n",
    "        id_list_10 = []\n",
    "        for member in res.json()['resultList']:\n",
    "            #print(member['openNaId'])\n",
    "            id_list_10.append(member['openNaId'])\n",
    "        \n",
    "        #print(id_list_10)\n",
    "        id_list_300.extend(id_list_10)\n",
    "    else:\n",
    "        print(res.status_code)\n",
    "print(len(id_list_300))\n",
    "#print(id_list_300[98:])"
   ]
  },
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    {
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     "output_type": "stream",
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      "===> 스크래핑 시작\n",
      "True 1 https://www.assembly.go.kr/members/22st/KANGKYUNGSOOK\n",
      "True 2 https://www.assembly.go.kr/members/22st/KANGDAESIK\n",
      "True 3 https://www.assembly.go.kr/members/22st/KANGDEUKGU\n",
      "True 4 https://www.assembly.go.kr/members/22st/KANGMYOUNGGU\n",
      "True 5 https://www.assembly.go.kr/members/22st/KANGMINKUK\n",
      "True 6 https://www.assembly.go.kr/members/22st/KANGSUNYOUNG\n",
      "True 7 https://www.assembly.go.kr/members/22st/KANGSUNWOO\n",
      "True 8 https://www.assembly.go.kr/members/22st/KangSeungKyoo\n",
      "True 9 https://www.assembly.go.kr/members/22st/KANGYUJUNG\n",
      "True 10 https://www.assembly.go.kr/members/22st/KANGJUNHYEON\n",
      "True 11 https://www.assembly.go.kr/members/22st/KANGHOONSIK\n",
      "True 12 https://www.assembly.go.kr/members/22st/KOHDONGJIN\n",
      "True 13 https://www.assembly.go.kr/members/22st/KOMINJUNG\n",
      "True 14 https://www.assembly.go.kr/members/22st/KWAKKYUTAEK\n",
      "True 15 https://www.assembly.go.kr/members/22st/KWAKSANGEON\n",
      "True 16 https://www.assembly.go.kr/members/22st/KUJAKEUN\n",
      "True 17 https://www.assembly.go.kr/members/22st/KWEONSEONGDONG\n",
      "True 18 https://www.assembly.go.kr/members/22st/KWONYOUNGSE\n",
      "True 19 https://www.assembly.go.kr/members/22st/KwonYoungJin\n",
      "True 20 https://www.assembly.go.kr/members/22st/KWONCHILSEUNG\n",
      "True 21 https://www.assembly.go.kr/members/22st/KWONHYANGYUP\n",
      "True 22 https://www.assembly.go.kr/members/22st/KIMGUNN\n",
      "True 23 https://www.assembly.go.kr/members/22st/KIMKYOHEUNG\n",
      "True 24 https://www.assembly.go.kr/members/22st/KIMKIWOONG\n",
      "True 25 https://www.assembly.go.kr/members/22st/KIMGIPYO\n",
      "True 26 https://www.assembly.go.kr/members/22st/KIMGIHYEON\n",
      "True 27 https://www.assembly.go.kr/members/22st/KIMNAMGEUN\n",
      "True 28 https://www.assembly.go.kr/members/22st/KIMNAMHEE\n",
      "True 29 https://www.assembly.go.kr/members/22st/KIMDAISIK\n",
      "True 30 https://www.assembly.go.kr/members/22st/KIMDOEUP\n",
      "True 31 https://www.assembly.go.kr/members/22st/KIMDONGAH\n",
      "True 32 https://www.assembly.go.kr/members/22st/KIMMOONSOO\n",
      "True 33 https://www.assembly.go.kr/members/22st/KIMMIAE\n",
      "True 34 https://www.assembly.go.kr/members/22st/KIMMINSEOK\n",
      "True 35 https://www.assembly.go.kr/members/22st/KIMMEENGEON\n",
      "True 36 https://www.assembly.go.kr/members/22st/KIMBYUNGKEE\n",
      "True 37 https://www.assembly.go.kr/members/22st/KIMBYUNGJOO\n",
      "True 38 https://www.assembly.go.kr/members/22st/KIMSANGWOOK\n",
      "True 39 https://www.assembly.go.kr/members/22st/KIMSANGHOON\n",
      "True 40 https://www.assembly.go.kr/members/22st/KIMSEOKKI\n",
      "True 41 https://www.assembly.go.kr/members/22st/KIMSUNGYO\n",
      "True 42 https://www.assembly.go.kr/members/22st/KIMSUNMIN\n",
      "True 43 https://www.assembly.go.kr/members/22st/KIMSUNGWON\n",
      "True 44 https://www.assembly.go.kr/members/22st/KIMSUNGWHAN\n",
      "True 45 https://www.assembly.go.kr/members/22st/KIMSUNGHOI\n",
      "True 46 https://www.assembly.go.kr/members/22st/KIMSOHEE\n",
      "True 47 https://www.assembly.go.kr/members/22st/KIMSEUNGSU\n",
      "True 48 https://www.assembly.go.kr/members/22st/KIMSEUNGWON\n",
      "True 49 https://www.assembly.go.kr/members/22st/KIMYOUNGBAE\n",
      "True 50 https://www.assembly.go.kr/members/22st/KIMYOUNGJIN\n",
      "True 51 https://www.assembly.go.kr/members/22st/KIMYOUNGHO\n",
      "True 52 https://www.assembly.go.kr/members/22st/KIMYOUNGWHAN\n",
      "True 53 https://www.assembly.go.kr/members/22st/KIMYEAJI\n",
      "True 54 https://www.assembly.go.kr/members/22st/KIMYONGMAN\n",
      "True 55 https://www.assembly.go.kr/members/22st/KIMYONGMIN\n",
      "True 56 https://www.assembly.go.kr/members/22st/YONGTAEKIM\n",
      "True 57 https://www.assembly.go.kr/members/22st/KIMWOOYOUNG\n",
      "True 58 https://www.assembly.go.kr/members/22st/KIMWONI\n",
      "True 59 https://www.assembly.go.kr/members/22st/KIMWISANG\n",
      "True 60 https://www.assembly.go.kr/members/22st/KIMYOON\n",
      "True 61 https://www.assembly.go.kr/members/22st/KIMYUNDUCK\n",
      "True 62 https://www.assembly.go.kr/members/22st/KIMEUNHYE\n",
      "True 63 https://www.assembly.go.kr/members/22st/KIMJANGKYOM\n",
      "True 64 https://www.assembly.go.kr/members/22st/KIMJAESUB\n",
      "True 65 https://www.assembly.go.kr/members/22st/JAEWONK\n",
      "True 66 https://www.assembly.go.kr/members/22st/KIMJUNGJAE\n",
      "True 67 https://www.assembly.go.kr/members/22st/KIMJUNGHO\n",
      "True 68 https://www.assembly.go.kr/members/22st/KIMJONGMIN\n",
      "True 69 https://www.assembly.go.kr/members/22st/KIMJONGYANG\n",
      "True 70 https://www.assembly.go.kr/members/22st/KIMJUYOUNG\n",
      "True 71 https://www.assembly.go.kr/members/22st/KIMJUNHYUK\n",
      "True 72 https://www.assembly.go.kr/members/22st/KIMJOONHYUNG\n",
      "True 73 https://www.assembly.go.kr/members/22st/KIMTAENYEON\n",
      "True 74 https://www.assembly.go.kr/members/22st/KIMTAESEON\n",
      "True 75 https://www.assembly.go.kr/members/22st/KIMTAEHO\n",
      "True 76 https://www.assembly.go.kr/members/22st/KIMHANKYU\n",
      "True 77 https://www.assembly.go.kr/members/22st/KIMHyun\n",
      "True 78 https://www.assembly.go.kr/members/22st/KIMHYUNJUNG\n",
      "True 79 https://www.assembly.go.kr/members/22st/KIMHYUNGDONG\n",
      "True 80 https://www.assembly.go.kr/members/22st/KIMHeeJung\n",
      "True 81 https://www.assembly.go.kr/members/22st/NAKYUNGWON\n",
      "True 82 https://www.assembly.go.kr/members/22st/NAMINSOON\n",
      "True 83 https://www.assembly.go.kr/members/22st/NOHJONGMYUN\n",
      "True 84 https://www.assembly.go.kr/members/22st/MAENGSUNGKYU\n",
      "True 85 https://www.assembly.go.kr/members/22st/MOGYEONGJONG\n",
      "True 86 https://www.assembly.go.kr/members/22st/MUNGEUMJU\n",
      "True 87 https://www.assembly.go.kr/members/22st/MOONDAELIM\n",
      "True 88 https://www.assembly.go.kr/members/22st/MOONJEONGBOG\n",
      "True 89 https://www.assembly.go.kr/members/22st/MOONJINSEOG\n",
      "True 90 https://www.assembly.go.kr/members/22st/MINBYOUNGDUG\n",
      "True 91 https://www.assembly.go.kr/members/22st/MINHYUNGBAE\n",
      "True 92 https://www.assembly.go.kr/members/22st/MINHONGCHUL\n",
      "True 93 https://www.assembly.go.kr/members/22st/PARKKYOONTAEK\n",
      "True 94 https://www.assembly.go.kr/members/22st/PARKDAECHUL\n",
      "True 95 https://www.assembly.go.kr/members/22st/PARKDUKHYUM\n",
      "True 96 https://www.assembly.go.kr/members/22st/PARKMINKYU\n",
      "True 97 https://www.assembly.go.kr/members/22st/PARKBEOMKYE\n",
      "True 98 https://www.assembly.go.kr/members/22st/PARKSANGWOONG\n",
      "True 99 https://www.assembly.go.kr/members/22st/PARKSANGHYUK\n",
      "True 100 https://www.assembly.go.kr/members/22st/PARKSUNWON\n",
      "True 101 https://www.assembly.go.kr/members/22st/PARKSEONGMIN\n",
      "True 102 https://www.assembly.go.kr/members/22st/PARKSUNGJOON\n",
      "True 103 https://www.assembly.go.kr/members/22st/PARKSUNGHOON\n",
      "True 104 https://www.assembly.go.kr/members/22st/PARKSOOMIN\n",
      "True 105 https://www.assembly.go.kr/members/22st/PARKSOOYOUNG\n",
      "True 106 https://www.assembly.go.kr/members/22st/PARKSooHyun\n",
      "True 107 https://www.assembly.go.kr/members/22st/PARKYONGKAB\n",
      "True 108 https://www.assembly.go.kr/members/22st/PARKEUNJUNG\n",
      "True 109 https://www.assembly.go.kr/members/22st/PARKJEUNG\n",
      "True 110 https://www.assembly.go.kr/members/22st/PARKJEONGHA\n",
      "True 111 https://www.assembly.go.kr/members/22st/PARKJEONGHYEON\n",
      "True 112 https://www.assembly.go.kr/members/22st/PARKJEONGHUN\n",
      "True 113 https://www.assembly.go.kr/members/22st/PARKJUMIN\n",
      "True 114 https://www.assembly.go.kr/members/22st/PARKJUNTAE\n",
      "True 115 https://www.assembly.go.kr/members/22st/PARKJIEWON\n",
      "True 116 https://www.assembly.go.kr/members/22st/PARKJEEHYE\n",
      "True 117 https://www.assembly.go.kr/members/22st/PARKCHANDAE\n",
      "True 118 https://www.assembly.go.kr/members/22st/PARKCHOONGKWON\n",
      "True 119 https://www.assembly.go.kr/members/22st/PARKHAECHEOL\n",
      "True 120 https://www.assembly.go.kr/members/22st/PARKHYEUNGSOO\n",
      "True 121 https://www.assembly.go.kr/members/22st/PARKHONGKEUN\n",
      "True 122 https://www.assembly.go.kr/members/22st/PARKHONGBAE\n",
      "True 123 https://www.assembly.go.kr/members/22st/PARKHEESEONG\n",
      "True 124 https://www.assembly.go.kr/members/22st/BAEJUNEYOUNG\n",
      "True 125 https://www.assembly.go.kr/members/22st/BAEHYUNJIN\n",
      "True 126 https://www.assembly.go.kr/members/22st/BAKSEUNGA\n",
      "True 127 https://www.assembly.go.kr/members/22st/BAEKJONGHEAN\n",
      "True 128 https://www.assembly.go.kr/members/22st/BACKHYERYUN\n",
      "True 129 https://www.assembly.go.kr/members/22st/BOKKIWANG\n",
      "True 130 https://www.assembly.go.kr/members/22st/BOOSEUNGCHAN\n",
      "True 131 https://www.assembly.go.kr/members/22st/SUHMYUNGOK\n",
      "True 132 https://www.assembly.go.kr/members/22st/SEOMIHWA\n",
      "True 133 https://www.assembly.go.kr/members/22st/SUHBUMSOO\n",
      "True 134 https://www.assembly.go.kr/members/22st/SEOSAMSEOK\n",
      "True 135 https://www.assembly.go.kr/members/22st/SEOYOUNGKYO\n",
      "True 136 https://www.assembly.go.kr/members/22st/SEOYOUNGSEOK\n",
      "True 137 https://www.assembly.go.kr/members/22st/SEOWANGJIN\n",
      "True 138 https://www.assembly.go.kr/members/22st/SEOILJUN\n",
      "True 139 https://www.assembly.go.kr/members/22st/SEOJIYOUNG\n",
      "True 140 https://www.assembly.go.kr/members/22st/SUHCHEONHO\n",
      "True 141 https://www.assembly.go.kr/members/22st/SUNGILJONG\n",
      "True 142 https://www.assembly.go.kr/members/22st/SOBYUNGHOON\n",
      "True 143 https://www.assembly.go.kr/members/22st/SONMYOUNGSOO\n",
      "True 144 https://www.assembly.go.kr/members/22st/SONGKIHUN\n",
      "True 145 https://www.assembly.go.kr/members/22st/SONGSEOGJUN\n",
      "True 146 https://www.assembly.go.kr/members/22st/SONGEONSEOG\n",
      "True 147 https://www.assembly.go.kr/members/22st/SONGOKJOO\n",
      "True 148 https://www.assembly.go.kr/members/22st/SONGJAEBONG\n",
      "True 149 https://www.assembly.go.kr/members/22st/SHINDONGUK\n",
      "True 150 https://www.assembly.go.kr/members/22st/SHINSungBum\n",
      "True 151 https://www.assembly.go.kr/members/22st/SHINYEONGDAE\n",
      "True 152 https://www.assembly.go.kr/members/22st/SHINCHANGSIK\n",
      "True 153 https://www.assembly.go.kr/members/22st/SHINJEONGHOON\n",
      "True 154 https://www.assembly.go.kr/members/22st/AHNGYUBACK\n",
      "True 155 https://www.assembly.go.kr/members/22st/AHNDOGEOL\n",
      "True 156 https://www.assembly.go.kr/members/22st/AHNSANGHOON\n",
      "True 157 https://www.assembly.go.kr/members/22st/AHNCHEOLSOO\n",
      "True 158 https://www.assembly.go.kr/members/22st/ANTAEJUN\n",
      "True 159 https://www.assembly.go.kr/members/22st/ANHOYOUNG\n",
      "True 160 https://www.assembly.go.kr/members/22st/YANGMOONSEOK\n",
      "True 161 https://www.assembly.go.kr/members/22st/YANGBUNAM\n",
      "True 162 https://www.assembly.go.kr/members/22st/EOHKIYKU\n",
      "True 163 https://www.assembly.go.kr/members/22st/EOMTAEYOUNG\n",
      "True 164 https://www.assembly.go.kr/members/22st/YEOMTAEYOUNG\n",
      "True 165 https://www.assembly.go.kr/members/22st/OHGIHYOUNG\n",
      "True 166 https://www.assembly.go.kr/members/22st/OHSAEHEE\n",
      "True 167 https://www.assembly.go.kr/members/22st/YONGHYEIN\n",
      "True 168 https://www.assembly.go.kr/members/22st/WOOWONSHIK\n",
      "True 169 https://www.assembly.go.kr/members/22st/WOOJAEJUN\n",
      "True 170 https://www.assembly.go.kr/members/22st/WISEONGGON\n",
      "True 171 https://www.assembly.go.kr/members/22st/WISUNGLAC\n",
      "True 172 https://www.assembly.go.kr/members/22st/YOODONGSOO\n",
      "True 173 https://www.assembly.go.kr/members/22st/YOOSANGBUM\n",
      "True 174 https://www.assembly.go.kr/members/22st/YOOYEONGHA\n",
      "True 175 https://www.assembly.go.kr/members/22st/YUYONGWEON\n",
      "True 176 https://www.assembly.go.kr/members/22st/YOUNKUNYOUNG\n",
      "True 177 https://www.assembly.go.kr/members/22st/YOONSANGHYUN\n",
      "True 178 https://www.assembly.go.kr/members/22st/YOONYOUNGSEOK\n",
      "True 179 https://www.assembly.go.kr/members/22st/YUNJAEOK\n",
      "True 180 https://www.assembly.go.kr/members/22st/YOONJONGKUN\n",
      "True 181 https://www.assembly.go.kr/members/22st/YOONJONGO\n",
      "True 182 https://www.assembly.go.kr/members/22st/YOONJOONBYEONG\n",
      "True 183 https://www.assembly.go.kr/members/22st/YOONHANHONG\n",
      "True 184 https://www.assembly.go.kr/members/22st/YUNHOJUNG\n",
      "True 185 https://www.assembly.go.kr/members/22st/YOONHUDUK\n",
      "True 186 https://www.assembly.go.kr/members/22st/LEEKANGILL\n",
      "True 187 https://www.assembly.go.kr/members/22st/LEEKAIHO\n",
      "True 188 https://www.assembly.go.kr/members/22st/LEEGEONTAE\n",
      "True 189 https://www.assembly.go.kr/members/22st/LEEGOANGHEE\n",
      "True 190 https://www.assembly.go.kr/members/22st/LEEKIHEON\n",
      "True 191 https://www.assembly.go.kr/members/22st/LEEDALHEE\n",
      "True 192 https://www.assembly.go.kr/members/22st/LEEMANHEE\n",
      "True 193 https://www.assembly.go.kr/members/22st/YIBYEONGJIN\n",
      "True 194 https://www.assembly.go.kr/members/22st/LEESANGSIK\n",
      "True 195 https://www.assembly.go.kr/members/22st/LEESANGHWI\n",
      "True 196 https://www.assembly.go.kr/members/22st/LEESEONGKWEUN\n",
      "True 197 https://www.assembly.go.kr/members/22st/LEESUNGYOON\n",
      "True 198 https://www.assembly.go.kr/members/22st/LEESOYOUNG\n",
      "True 199 https://www.assembly.go.kr/members/22st/LEESOOJIN\n",
      "True 200 https://www.assembly.go.kr/members/22st/LEEYANGSOO\n",
      "True 201 https://www.assembly.go.kr/members/22st/LEEUNJU\n",
      "True 202 https://www.assembly.go.kr/members/22st/LEEYEONHEE\n",
      "True 203 https://www.assembly.go.kr/members/22st/LEEYONGSUN\n",
      "True 204 https://www.assembly.go.kr/members/22st/YONGWOOLEE\n",
      "True 205 https://www.assembly.go.kr/members/22st/LEEWONTAEG\n",
      "True 206 https://www.assembly.go.kr/members/22st/LEEINSEON\n",
      "True 207 https://www.assembly.go.kr/members/22st/LEEINYOUNG\n",
      "True 208 https://www.assembly.go.kr/members/22st/LEEJAEGANG\n",
      "True 209 https://www.assembly.go.kr/members/22st/LEEJAEKWAN\n",
      "True 210 https://www.assembly.go.kr/members/22st/LEEJAEMYUNG\n",
      "True 211 https://www.assembly.go.kr/members/22st/LEEJAEJUNG\n",
      "True 212 https://www.assembly.go.kr/members/22st/LEEJUNGMUN\n",
      "True 213 https://www.assembly.go.kr/members/22st/LEEJEONGHEON\n",
      "True 214 https://www.assembly.go.kr/members/22st/LEEJONGBAE\n",
      "True 215 https://www.assembly.go.kr/members/22st/LEEJONHWOOK\n",
      "True 216 https://www.assembly.go.kr/members/22st/JUYOUNGLEE\n",
      "True 217 https://www.assembly.go.kr/members/22st/LEEJUNSEOK\n",
      "True 218 https://www.assembly.go.kr/members/22st/LEECHULGYU\n",
      "True 219 https://www.assembly.go.kr/members/22st/LEECHOONSUAK\n",
      "True 220 https://www.assembly.go.kr/members/22st/LEEHACKYOUNG\n",
      "True 221 https://www.assembly.go.kr/members/22st/LEEHAIMIN\n",
      "True 222 https://www.assembly.go.kr/members/22st/LEEHAESIK\n",
      "True 223 https://www.assembly.go.kr/members/22st/LEEHUNSEUNG\n",
      "True 224 https://www.assembly.go.kr/members/22st/LEEHOONKI\n",
      "True 225 https://www.assembly.go.kr/members/22st/IHNYOHAN\n",
      "True 226 https://www.assembly.go.kr/members/22st/LIMKWANGHYUN\n",
      "True 227 https://www.assembly.go.kr/members/22st/LIMMIAE\n",
      "True 228 https://www.assembly.go.kr/members/22st/LIMOKYEONG\n",
      "True 229 https://www.assembly.go.kr/members/22st/LIMLEEJA\n",
      "True 230 https://www.assembly.go.kr/members/22st/LIMJONGDEUK\n",
      "True 231 https://www.assembly.go.kr/members/22st/LIMHOSEON\n",
      "True 232 https://www.assembly.go.kr/members/22st/JANGKYUNGTAE\n",
      "True 233 https://www.assembly.go.kr/members/22st/JANGDONGHYEOK\n",
      "True 234 https://www.assembly.go.kr/members/22st/CHANGJONGTAE\n",
      "True 235 https://www.assembly.go.kr/members/22st/CHULMINJANG\n",
      "True 236 https://www.assembly.go.kr/members/22st/JEONYONGGI\n",
      "True 237 https://www.assembly.go.kr/members/22st/CHUNJAESOO\n",
      "True 238 https://www.assembly.go.kr/members/22st/CHUNJONGDUK\n",
      "True 239 https://www.assembly.go.kr/members/22st/JEONJINSOOK\n",
      "True 240 https://www.assembly.go.kr/members/22st/JEONHYUNHEUI\n",
      "True 241 https://www.assembly.go.kr/members/22st/JEONGDONGMAN\n",
      "True 242 https://www.assembly.go.kr/members/22st/CHUNGDONGYOUNG\n",
      "True 243 https://www.assembly.go.kr/members/22st/JUNGSUNGKOOK\n",
      "True 244 https://www.assembly.go.kr/members/22st/JUNGSUNGHO\n",
      "True 245 https://www.assembly.go.kr/members/22st/JUNGYEONWOOK\n",
      "True 246 https://www.assembly.go.kr/members/22st/JUNGEULHO\n",
      "True 247 https://www.assembly.go.kr/members/22st/CHUNGILYOUNG\n",
      "True 248 https://www.assembly.go.kr/members/22st/JEONGJEOMSIG\n",
      "True 249 https://www.assembly.go.kr/members/22st/JEONGJUNHO\n",
      "True 250 https://www.assembly.go.kr/members/22st/CHUNGCHINOOK\n",
      "True 251 https://www.assembly.go.kr/members/22st/JUNGCHUNGRAE\n",
      "True 252 https://www.assembly.go.kr/members/22st/CHUNGCHOONSAENG\n",
      "True 253 https://www.assembly.go.kr/members/22st/JUNGTAEHO\n",
      "True 254 https://www.assembly.go.kr/members/22st/JUNGHYEKYUNG\n",
      "True 255 https://www.assembly.go.kr/members/22st/JUNGHEEYONG\n",
      "True 256 https://www.assembly.go.kr/members/22st/CHOKYOUNGTAE\n",
      "True 257 https://www.assembly.go.kr/members/22st/CHOGYEWON\n",
      "True 258 https://www.assembly.go.kr/members/22st/CHOKUK\n",
      "True 259 https://www.assembly.go.kr/members/22st/CHOBAESOOK\n",
      "True 260 https://www.assembly.go.kr/members/22st/JOSEOUNGLAE\n",
      "True 261 https://www.assembly.go.kr/members/22st/CHOSEUNGHWAN\n",
      "True 262 https://www.assembly.go.kr/members/22st/CHOEUNHEE\n",
      "True 263 https://www.assembly.go.kr/members/22st/CHOINCHEUL\n",
      "True 264 https://www.assembly.go.kr/members/22st/CHOJEONGSIK\n",
      "True 265 https://www.assembly.go.kr/members/22st/CHOJUNGHUN\n",
      "True 266 https://www.assembly.go.kr/members/22st/CHOJIYEON\n",
      "True 267 https://www.assembly.go.kr/members/22st/JOOJINWOO\n",
      "True 268 https://www.assembly.go.kr/members/22st/JUCHEOLHYEON\n",
      "True 269 https://www.assembly.go.kr/members/22st/JOOHOYOUNG\n",
      "True 270 https://www.assembly.go.kr/members/22st/JINSUNMEE\n",
      "True 271 https://www.assembly.go.kr/members/22st/JINSUNGJOON\n",
      "True 272 https://www.assembly.go.kr/members/22st/JINJONGOH\n",
      "True 273 https://www.assembly.go.kr/members/22st/CHAGYUGEUN\n",
      "True 274 https://www.assembly.go.kr/members/22st/CHAJIHO\n",
      "True 275 https://www.assembly.go.kr/members/22st/CHAIHYUNIL\n",
      "True 276 https://www.assembly.go.kr/members/22st/CHEONJUNHO\n",
      "True 277 https://www.assembly.go.kr/members/22st/CHUNHARAM\n",
      "True 278 https://www.assembly.go.kr/members/22st/CHOIKISANG\n",
      "True 279 https://www.assembly.go.kr/members/22st/CHOIMinhee\n",
      "True 280 https://www.assembly.go.kr/members/22st/CHOIBOYUN\n",
      "True 281 https://www.assembly.go.kr/members/22st/CHOISOOJIN\n",
      "True 282 https://www.assembly.go.kr/members/22st/CHOIEUNSEOK\n",
      "True 283 https://www.assembly.go.kr/members/22st/CHOIHYUNGDU\n",
      "True 284 https://www.assembly.go.kr/members/22st/CHOOKYUNGHO\n",
      "True 285 https://www.assembly.go.kr/members/22st/CHOOMIAE\n",
      "True 286 https://www.assembly.go.kr/members/22st/HANKIHO\n",
      "True 287 https://www.assembly.go.kr/members/22st/HANMINSOO\n",
      "True 288 https://www.assembly.go.kr/members/22st/HANBYUNGDO\n",
      "True 289 https://www.assembly.go.kr/members/22st/HANJEOUNGAE\n",
      "True 290 https://www.assembly.go.kr/members/22st/HANJUNHO\n",
      "True 291 https://www.assembly.go.kr/members/22st/HANZEEA\n",
      "True 292 https://www.assembly.go.kr/members/22st/HANCHANGMIN\n",
      "True 293 https://www.assembly.go.kr/members/22st/HUHSUNGMOO\n",
      "True 294 https://www.assembly.go.kr/members/22st/HUHYOUNG\n",
      "True 295 https://www.assembly.go.kr/members/22st/HEOJONGSIK\n",
      "True 296 https://www.assembly.go.kr/members/22st/HONGKEEWON\n",
      "True 297 https://www.assembly.go.kr/members/22st/HWANGMYEONGSEON\n",
      "True 298 https://www.assembly.go.kr/members/22st/HWANGUNHA\n",
      "True 299 https://www.assembly.go.kr/members/22st/HWANGJUNGA\n",
      "True 300 https://www.assembly.go.kr/members/22st/HWANGHEE\n",
      "300\n",
      "===> 스크래핑 끝\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "from urllib.parse import urljoin\n",
    "\n",
    "print('===> 스크래핑 시작')\n",
    "#300명의 정보를 저장하는 List\n",
    "member_detail_list = []\n",
    "\n",
    "headers = {\n",
    "    'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36'\n",
    "}\n",
    "\n",
    "for idx,mem_id in enumerate(id_list_300,1):\n",
    "    detail_url = f'https://www.assembly.go.kr/members/22st/{mem_id}'\n",
    "    res = requests.get(detail_url, headers=headers)\n",
    "    print(res.ok, idx, detail_url)\n",
    "    \n",
    "    if res.ok:\n",
    "        soup = BeautifulSoup(res.text, 'html.parser')\n",
    "        \n",
    "        #1명의 정보를 저장하는 dict 선언\n",
    "        member_detail_dict = {}\n",
    "\n",
    "        #['선거구','당선횟수']\n",
    "        dt_list = [dt_tag.text for dt_tag in soup.select('ul.list li dt')]\n",
    "        #print(dt_list)\n",
    "\n",
    "        #['서울 영등포구을','4선(제15대']\n",
    "        dd_list = []        \n",
    "        for dd_tag in soup.select('ul.list li dd'):\n",
    "            pattern = re.compile(f'[\\n\\r\\t]')\n",
    "            dd_text = pattern.sub('',dd_tag.text.strip()).replace(\" \",\"\")\n",
    "            dd_list.append(dd_text)\n",
    "        #print(dd_list)\n",
    "        \n",
    "        member_detail_dict = dict(zip(dt_list, dd_list))\n",
    "        \n",
    "        # <strong>홍길동 (洪吉童)</strong>\n",
    "        name = soup.select_one('div.intro p.tit strong').text\n",
    "        #print(name.split(' ')[0])\n",
    "        member_detail_dict['이름'] = name.split(' ')[0] #['박주민','(朴柱民)']\n",
    "        \n",
    "        span_tag_age = soup.select_one('div.intro p.tit span.sm span')\n",
    "        member_detail_dict['생년월일'] = span_tag_age.text\n",
    "\n",
    "        #style=\"background-image: url('/static/portal/img/openassm/new/fee10a80d81d435885696adb29a6eb41.jpg')\n",
    "        spanimg_tag = soup.select('span.img-set span.img')\n",
    "        if spanimg_tag:\n",
    "            style_url = spanimg_tag[0]['style']\n",
    "            start_idx = style_url.find(\"(\")+1\n",
    "            end_idx = style_url.find(\")\")\n",
    "            img_sub_url = style_url[start_idx:end_idx].replace(\"'\",\"\")\n",
    "            # img_sub_url : /static/portal/img/openassm/new/0410a0c8e24b474cac2fd79c7700ca2f.jpg\n",
    "            # detail_url : https://www.assembly.go.kr/members/22st/KANGDEUKGU\n",
    "            # urljoin() 함수 : https://www.assembly.go.kr + /static/portal/img/openassm/new/0410a0c8e24b474cac2fd79c7700ca2f.jpg\n",
    "            img_url = urljoin(detail_url,img_sub_url)\n",
    "            #print(img_url)\n",
    "            member_detail_dict['이미지'] = img_url\n",
    "        \n",
    "        dd_tag_party = soup.select_one('div.tit dl dd')\n",
    "        member_detail_dict['정당'] =  dd_tag_party.text\n",
    "                \n",
    "        #print(member_detail_dict)\n",
    "        #1명의 정보가 저장된 dict를 list에 추가하기\n",
    "        member_detail_list.append(member_detail_dict)\n",
    "    else:\n",
    "        print(res.status_code)\n",
    "        \n",
    "print(len(member_detail_list))        \n",
    "print('===> 스크래핑 끝')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aea34312-46a3-40c2-a780-fab765f861bd",
   "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": 6,
   "id": "a9ebf0cf-a43f-4c26-be95-39009240b0dd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(300, 16)\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>선거구</th>\n",
       "      <th>소속위원회</th>\n",
       "      <th>당선횟수</th>\n",
       "      <th>사무실 전화</th>\n",
       "      <th>사무실 호실</th>\n",
       "      <th>의원 홈페이지</th>\n",
       "      <th>개별 홈페이지</th>\n",
       "      <th>이메일</th>\n",
       "      <th>보좌관</th>\n",
       "      <th>선임비서관</th>\n",
       "      <th>비서관</th>\n",
       "      <th>의원실 안내</th>\n",
       "      <th>이름</th>\n",
       "      <th>생년월일</th>\n",
       "      <th>이미지</th>\n",
       "      <th>정당</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>비례대표</td>\n",
       "      <td>교육위원회</td>\n",
       "      <td>초선(제22대)2024.05.30~제22대국회의원</td>\n",
       "      <td>02-784-5601</td>\n",
       "      <td>의원회관515호</td>\n",
       "      <td>www.assembly.go.kr/members/22nd/KANGKYUNGSOOK</td>\n",
       "      <td></td>\n",
       "      <td>gracesook52@rebuildingk.kr</td>\n",
       "      <td>김재삼,서용선</td>\n",
       "      <td>김홍,오세령</td>\n",
       "      <td>권혁,박문혁,김예닮,이경열,김희경</td>\n",
       "      <td></td>\n",
       "      <td>강경숙</td>\n",
       "      <td>1967-05-02</td>\n",
       "      <td>https://www.assembly.go.kr/static/portal/img/o...</td>\n",
       "      <td>조국혁신당</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>대구동구군위군을</td>\n",
       "      <td>국방위원회</td>\n",
       "      <td>재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~202...</td>\n",
       "      <td>02-784-5275</td>\n",
       "      <td>의원회관341호</td>\n",
       "      <td>www.assembly.go.kr/members/22nd/KANGDAESIK</td>\n",
       "      <td></td>\n",
       "      <td>kds21341@naver.com</td>\n",
       "      <td>박홍규,유진영</td>\n",
       "      <td>김기일,이기백</td>\n",
       "      <td>박순권,김광연,김현정,황인택,이정은</td>\n",
       "      <td></td>\n",
       "      <td>강대식</td>\n",
       "      <td>1959-11-02</td>\n",
       "      <td>https://www.assembly.go.kr/static/portal/img/o...</td>\n",
       "      <td>국민의힘</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        선거구  소속위원회                                               당선횟수  \\\n",
       "0      비례대표  교육위원회                        초선(제22대)2024.05.30~제22대국회의원   \n",
       "1  대구동구군위군을  국방위원회  재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~202...   \n",
       "\n",
       "        사무실 전화    사무실 호실                                        의원 홈페이지  \\\n",
       "0  02-784-5601  의원회관515호  www.assembly.go.kr/members/22nd/KANGKYUNGSOOK   \n",
       "1  02-784-5275  의원회관341호     www.assembly.go.kr/members/22nd/KANGDAESIK   \n",
       "\n",
       "  개별 홈페이지                         이메일      보좌관    선임비서관                  비서관  \\\n",
       "0          gracesook52@rebuildingk.kr  김재삼,서용선   김홍,오세령   권혁,박문혁,김예닮,이경열,김희경   \n",
       "1                  kds21341@naver.com  박홍규,유진영  김기일,이기백  박순권,김광연,김현정,황인택,이정은   \n",
       "\n",
       "  의원실 안내   이름        생년월일                                                이미지  \\\n",
       "0         강경숙  1967-05-02  https://www.assembly.go.kr/static/portal/img/o...   \n",
       "1         강대식  1959-11-02  https://www.assembly.go.kr/static/portal/img/o...   \n",
       "\n",
       "      정당  \n",
       "0  조국혁신당  \n",
       "1   국민의힘  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "member_json_df = pd.read_json('data/member.json')\n",
    "print(member_json_df.shape)\n",
    "member_json_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c072f18",
   "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>생년월일</th>\n",
       "      <th>당선횟수</th>\n",
       "      <th>선거구</th>\n",
       "      <th>소속위원회</th>\n",
       "      <th>이미지</th>\n",
       "      <th>사무실 전화</th>\n",
       "      <th>사무실 호실</th>\n",
       "      <th>의원 홈페이지</th>\n",
       "      <th>개별 홈페이지</th>\n",
       "      <th>이메일</th>\n",
       "      <th>보좌관</th>\n",
       "      <th>선임비서관</th>\n",
       "      <th>비서관</th>\n",
       "      <th>의원실 안내</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>강경숙</td>\n",
       "      <td>조국혁신당</td>\n",
       "      <td>1967-05-02</td>\n",
       "      <td>초선(제22대)2024.05.30~제22대국회의원</td>\n",
       "      <td>비례대표</td>\n",
       "      <td>교육위원회</td>\n",
       "      <td>https://www.assembly.go.kr/static/portal/img/o...</td>\n",
       "      <td>02-784-5601</td>\n",
       "      <td>의원회관515호</td>\n",
       "      <td>www.assembly.go.kr/members/22nd/KANGKYUNGSOOK</td>\n",
       "      <td></td>\n",
       "      <td>gracesook52@rebuildingk.kr</td>\n",
       "      <td>김재삼,서용선</td>\n",
       "      <td>김홍,오세령</td>\n",
       "      <td>권혁,박문혁,김예닮,이경열,김희경</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>강대식</td>\n",
       "      <td>국민의힘</td>\n",
       "      <td>1959-11-02</td>\n",
       "      <td>재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~202...</td>\n",
       "      <td>대구동구군위군을</td>\n",
       "      <td>국방위원회</td>\n",
       "      <td>https://www.assembly.go.kr/static/portal/img/o...</td>\n",
       "      <td>02-784-5275</td>\n",
       "      <td>의원회관341호</td>\n",
       "      <td>www.assembly.go.kr/members/22nd/KANGDAESIK</td>\n",
       "      <td></td>\n",
       "      <td>kds21341@naver.com</td>\n",
       "      <td>박홍규,유진영</td>\n",
       "      <td>김기일,이기백</td>\n",
       "      <td>박순권,김광연,김현정,황인택,이정은</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    이름     정당        생년월일                                               당선횟수  \\\n",
       "0  강경숙  조국혁신당  1967-05-02                        초선(제22대)2024.05.30~제22대국회의원   \n",
       "0  강대식   국민의힘  1959-11-02  재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~202...   \n",
       "\n",
       "        선거구  소속위원회                                                이미지  \\\n",
       "0      비례대표  교육위원회  https://www.assembly.go.kr/static/portal/img/o...   \n",
       "0  대구동구군위군을  국방위원회  https://www.assembly.go.kr/static/portal/img/o...   \n",
       "\n",
       "        사무실 전화    사무실 호실                                        의원 홈페이지  \\\n",
       "0  02-784-5601  의원회관515호  www.assembly.go.kr/members/22nd/KANGKYUNGSOOK   \n",
       "0  02-784-5275  의원회관341호     www.assembly.go.kr/members/22nd/KANGDAESIK   \n",
       "\n",
       "  개별 홈페이지                         이메일      보좌관    선임비서관                  비서관  \\\n",
       "0          gracesook52@rebuildingk.kr  김재삼,서용선   김홍,오세령   권혁,박문혁,김예닮,이경열,김희경   \n",
       "0                  kds21341@naver.com  박홍규,유진영  김기일,이기백  박순권,김광연,김현정,황인택,이정은   \n",
       "\n",
       "  의원실 안내  \n",
       "0         \n",
       "0         "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "member_df = pd.DataFrame(columns=['이름','정당','생년월일','당선횟수','선거구','소속위원회','이미지','사무실 전화','사무실 호실',\\\n",
    "                                  '의원 홈페이지','개별 홈페이지','이메일','보좌관','선임비서관','비서관','의원실 안내'])\n",
    "\n",
    "for member_detail in member_detail_list:\n",
    "    df_new_row = pd.DataFrame.from_records([member_detail])\n",
    "    member_df = pd.concat([member_df, df_new_row])\n",
    "    # dict <=> Series\n",
    "    #series_obj = pd.Series(member_detail)\n",
    "    # Series 객체를 append \n",
    "    #member_df = member_df.append(series_obj, ignore_index=True)\n",
    "    \n",
    "member_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "93f535fc-9887-4f33-9087-db87361e5f3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 16)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "22dd495e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "       ...\n",
       "       291, 292, 293, 294, 295, 296, 297, 298, 299, 300],\n",
       "      dtype='int32', length=300)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "#index 변경\n",
    "member_df.index = np.arange(1, len(member_df)+1)\n",
    "member_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ed1331ff",
   "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>생년월일</th>\n",
       "      <th>당선횟수</th>\n",
       "      <th>선거구</th>\n",
       "      <th>소속위원회</th>\n",
       "      <th>이미지</th>\n",
       "      <th>사무실 전화</th>\n",
       "      <th>사무실 호실</th>\n",
       "      <th>의원 홈페이지</th>\n",
       "      <th>개별 홈페이지</th>\n",
       "      <th>이메일</th>\n",
       "      <th>보좌관</th>\n",
       "      <th>선임비서관</th>\n",
       "      <th>비서관</th>\n",
       "      <th>의원실 안내</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>황정아</td>\n",
       "      <td>더불어민주당</td>\n",
       "      <td>1977-03-07</td>\n",
       "      <td>초선(제22대)2024.05.30~제22대국회의원</td>\n",
       "      <td>대전유성구을</td>\n",
       "      <td>과학기술정보방송통신위원회,예산결산특별위원회</td>\n",
       "      <td>https://www.assembly.go.kr/static/portal/img/o...</td>\n",
       "      <td>02-784-2566</td>\n",
       "      <td>의원회관717호</td>\n",
       "      <td>www.assembly.go.kr/members/22nd/HWANGJUNGA</td>\n",
       "      <td>https://blog.naver.com/k0heaven</td>\n",
       "      <td>hwang_0410@naver.com</td>\n",
       "      <td>권영근,이태주</td>\n",
       "      <td>박찬중,조나연</td>\n",
       "      <td>이혜지,성기학,허윤정,우민경,박정수</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>황희</td>\n",
       "      <td>더불어민주당</td>\n",
       "      <td>1967-07-28</td>\n",
       "      <td>3선(제20대,제21대,제22대)2024.05.30~제22대국회의원2020.05.3...</td>\n",
       "      <td>서울양천구갑</td>\n",
       "      <td>국방위원회</td>\n",
       "      <td>https://www.assembly.go.kr/static/portal/img/o...</td>\n",
       "      <td>02-784-8551</td>\n",
       "      <td>의원회관838호</td>\n",
       "      <td>www.assembly.go.kr/members/22nd/HWANGHEE</td>\n",
       "      <td>http://blog.naver.com/hwanghee67</td>\n",
       "      <td>hwanghee6728@gmail.com</td>\n",
       "      <td>나성채,조주연</td>\n",
       "      <td>김병기,정대중</td>\n",
       "      <td>오상혁,나유경,김용대,문경림,신소라</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      이름      정당        생년월일  \\\n",
       "299  황정아  더불어민주당  1977-03-07   \n",
       "300   황희  더불어민주당  1967-07-28   \n",
       "\n",
       "                                                  당선횟수     선거구  \\\n",
       "299                        초선(제22대)2024.05.30~제22대국회의원  대전유성구을   \n",
       "300  3선(제20대,제21대,제22대)2024.05.30~제22대국회의원2020.05.3...  서울양천구갑   \n",
       "\n",
       "                       소속위원회  \\\n",
       "299  과학기술정보방송통신위원회,예산결산특별위원회   \n",
       "300                    국방위원회   \n",
       "\n",
       "                                                   이미지       사무실 전화    사무실 호실  \\\n",
       "299  https://www.assembly.go.kr/static/portal/img/o...  02-784-2566  의원회관717호   \n",
       "300  https://www.assembly.go.kr/static/portal/img/o...  02-784-8551  의원회관838호   \n",
       "\n",
       "                                        의원 홈페이지  \\\n",
       "299  www.assembly.go.kr/members/22nd/HWANGJUNGA   \n",
       "300    www.assembly.go.kr/members/22nd/HWANGHEE   \n",
       "\n",
       "                              개별 홈페이지                     이메일      보좌관  \\\n",
       "299   https://blog.naver.com/k0heaven    hwang_0410@naver.com  권영근,이태주   \n",
       "300  http://blog.naver.com/hwanghee67  hwanghee6728@gmail.com  나성채,조주연   \n",
       "\n",
       "       선임비서관                  비서관 의원실 안내  \n",
       "299  박찬중,조나연  이혜지,성기학,허윤정,우민경,박정수         \n",
       "300  김병기,정대중  오상혁,나유경,김용대,문경림,신소라         "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d0a155b-8500-4250-8627-f0737f33294a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 300 entries, 1 to 300\n",
      "Data columns (total 16 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   이름       300 non-null    object\n",
      " 1   정당       300 non-null    object\n",
      " 2   생년월일     300 non-null    object\n",
      " 3   당선횟수     300 non-null    object\n",
      " 4   선거구      300 non-null    object\n",
      " 5   소속위원회    300 non-null    object\n",
      " 6   이미지      300 non-null    object\n",
      " 7   사무실 전화   300 non-null    object\n",
      " 8   사무실 호실   300 non-null    object\n",
      " 9   의원 홈페이지  300 non-null    object\n",
      " 10  개별 홈페이지  300 non-null    object\n",
      " 11  이메일      300 non-null    object\n",
      " 12  보좌관      300 non-null    object\n",
      " 13  선임비서관    300 non-null    object\n",
      " 14  비서관      300 non-null    object\n",
      " 15  의원실 안내   300 non-null    object\n",
      "dtypes: object(16)\n",
      "memory usage: 38.7+ KB\n"
     ]
    }
   ],
   "source": [
    "member_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f6439be-c469-4579-839c-be0eae297367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['조국혁신당', '국민의힘', '더불어민주당', '새로운미래', '기본소득당', '무소속', '진보당', '개혁신당',\n",
       "       '사회민주당'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['정당'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2f091719-a57a-4383-b7fd-0d71ef1894b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "정당\n",
       "더불어민주당    170\n",
       "국민의힘      108\n",
       "조국혁신당      12\n",
       "진보당         3\n",
       "개혁신당        3\n",
       "새로운미래       1\n",
       "기본소득당       1\n",
       "무소속         1\n",
       "사회민주당       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['정당'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c75dcc32-7877-46b5-895f-09e761ca5ae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "당선횟수\n",
       "초선(제22대)2024.05.30~제22대국회의원                                                                        131\n",
       "재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~2024.05.29제21대국회의원                                       64\n",
       "3선(제20대,제21대,제22대)2024.05.30~제22대국회의원2020.05.30~2024.05.29제21대국회의원2016.05.30~2020.05.29제20대국회의원     32\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2ac1b3ce-0be3-43b4-bbbc-64a9948c7376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "선거구\n",
       "비례대표           46\n",
       "충북청주시흥덕구        1\n",
       "경기부천시병          1\n",
       "충북청주시서원구        1\n",
       "경기고양시병          1\n",
       "               ..\n",
       "부산북구을           1\n",
       "서울강남구을          1\n",
       "부산남구            1\n",
       "충남공주시부여군청양군     1\n",
       "서울양천구갑          1\n",
       "Name: count, Length: 255, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['선거구'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7046cafe-2084-4ae2-9be1-40a6f44daeac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.strings.accessor.StringMethods'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>당선횟수</th>\n",
       "      <th>당선횟수2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>초선(제22대)2024.05.30~제22대국회의원</td>\n",
       "      <td>초선</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~202...</td>\n",
       "      <td>재선</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~202...</td>\n",
       "      <td>재선</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                당선횟수 당선횟수2\n",
       "1                        초선(제22대)2024.05.30~제22대국회의원    초선\n",
       "2  재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~202...    재선\n",
       "3  재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~202...    재선"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#당선횟수2 컬럼을 새로 추가\n",
    "#재선(20대,21대) -> 재선\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1d93aabb-af5f-4873-8de8-ca5c4d6796c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.strings.accessor.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>1</th>\n",
       "      <td>초선(제22대)2024.05.30~제22대국회의원</td>\n",
       "      <td>초선</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~202...</td>\n",
       "      <td>재선</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~202...</td>\n",
       "      <td>재선</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                당선횟수 당선횟수2\n",
       "1                        초선(제22대)2024.05.30~제22대국회의원    초선\n",
       "2  재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~202...    재선\n",
       "3  재선(제21대,제22대)2024.05.30~제22대국회의원2020.05.30~202...    재선"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#당선횟수2 컬럼을 새로 추가\n",
    "#재선(20대,21대) -> 재선\n",
    "print(type(member_df['당선횟수']))\n",
    "print(type(member_df['당선횟수'].str))\n",
    "#Series객체를 StringMethods 객체로 변환(문자열 슬라이싱을 하기 위해서)\n",
    "temp_str = member_df['당선횟수'].str\n",
    "#2글자 슬라이싱한 값을 당선횟수2 라는 컬럼으로 저장\n",
    "member_df['당선횟수2'] = temp_str[:2]\n",
    "\n",
    "member_df.loc[0:3,['당선횟수','당선횟수2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "473118c3-ed63-4df6-910f-1137a57fbb89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "당선횟수2\n",
       "초선    131\n",
       "재선     80\n",
       "3선     47\n",
       "4선     24\n",
       "5선     14\n",
       "6선      4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['당선횟수2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79b7698a-dfe4-4a14-bd44-f808b15f251d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.strings.accessor.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>선거구</th>\n",
       "      <th>선거구2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>비례대표</td>\n",
       "      <td>비례</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>대구동구군위군을</td>\n",
       "      <td>대구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>경기안양시만안구</td>\n",
       "      <td>경기</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        선거구 선거구2\n",
       "1      비례대표   비례\n",
       "2  대구동구군위군을   대구\n",
       "3  경기안양시만안구   경기"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#선거구2 컬럼을 새로 추가\n",
    "#경남창원시성산구 -> 경남\n",
    "print(type(member_df['선거구']))\n",
    "print(type(member_df['선거구'].str))\n",
    "#Series객체를 StringMethods 객체로 변환(문자열 슬라이싱을 하기 위해서)\n",
    "temp_str = member_df['선거구'].str\n",
    "#2글자 슬라이싱 값을 선거구2 라는 컬럼으로 저장\n",
    "member_df['선거구2'] = temp_str[:2]\n",
    "\n",
    "member_df.loc[0:3,['선거구','선거구2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b5b73dd1-6815-4b04-942e-1fee1d1e4b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "선거구2\n",
       "경기    60\n",
       "서울    48\n",
       "비례    46\n",
       "부산    18\n",
       "경남    16\n",
       "인천    14\n",
       "경북    13\n",
       "대구    12\n",
       "충남    11\n",
       "전북    10\n",
       "전남    10\n",
       "충북     8\n",
       "광주     8\n",
       "강원     8\n",
       "대전     7\n",
       "울산     6\n",
       "제주     3\n",
       "세종     2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['선거구2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8cd1ba37-4f95-4e5c-8cc0-0ba25b9d6100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "선거구2\n",
       "경기    0.200000\n",
       "서울    0.160000\n",
       "비례    0.153333\n",
       "부산    0.060000\n",
       "경남    0.053333\n",
       "인천    0.046667\n",
       "경북    0.043333\n",
       "대구    0.040000\n",
       "충남    0.036667\n",
       "전북    0.033333\n",
       "전남    0.033333\n",
       "충북    0.026667\n",
       "광주    0.026667\n",
       "강원    0.026667\n",
       "대전    0.023333\n",
       "울산    0.020000\n",
       "제주    0.010000\n",
       "세종    0.006667\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f4216a4d-7b9d-4f90-a70d-7612759754f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1967-05-02\n",
       "2    1959-11-02\n",
       "3    1963-05-27\n",
       "Name: 생년월일, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a525f351-a244-4768-a77d-04259f6abdb7",
   "metadata": {},
   "source": [
    "[DatetimeIndex](https://pandas.pydata.org/docs/reference/api/pandas.DatetimeIndex.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4ad555e4-7f2f-40e0-a495-56b1b2acf51d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([1967, 1959, 1963, 1977, 1971, 1966, 1978, 1963, 1975, 1964,\n",
      "       ...\n",
      "       1978, 1973, 1963, 1970, 1962, 1964, 1966, 1962, 1977, 1967],\n",
      "      dtype='int32', name='생년월일', length=300)\n",
      "int32\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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1967</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day\n",
       "1  1967      5    2\n",
       "2  1959     11    2\n",
       "3  1963      5   27"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DatetimeIndex 객체를 사용해서 생년월일 컬럼의 값을 year,month,day 로 각각 추출한다. \n",
    "#year,month, day 3개의 컬럼을 새로 추가한다.\n",
    "print(pd.DatetimeIndex(member_df['생년월일']).year)\n",
    "\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",
    "print(member_df['year'].dtypes)\n",
    "\n",
    "member_df.loc[0:3,['year','month','day']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6956b89e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1991"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['year'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b89d56d6-2ce4-433b-bcbd-45befc616eb3",
   "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>선거구2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>전용기</td>\n",
       "      <td>더불어민주당</td>\n",
       "      <td>경기</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      이름      정당 선거구2\n",
       "236  전용기  더불어민주당   경기"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#가장 나이가 어린 사람은?\n",
    "member_df.loc[member_df['year'] == member_df['year'].max(),['이름','정당','선거구2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "84bd81c4-5b3d-4a9f-ac0c-6e461a5bc703",
   "metadata": {},
   "outputs": [],
   "source": [
    "#나이를 계산하는 함수 정의하기\n",
    "from datetime import date\n",
    "\n",
    "# 인자로 받는 birth는 datetime.date 객체이다.\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": 24,
   "id": "e5a9eded-db8a-44a8-80aa-a3faac348869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_age(date(1990,4,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cd9ee5ec-4c2f-477f-bb9c-770fbb01821e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_age(date(1980,1,17))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "034ae644-4cde-4d64-9aef-c5f1e64b16a9",
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1967</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1977</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   year  month  day\n",
       "1  1967      5    2\n",
       "2  1959     11    2\n",
       "3  1963      5   27\n",
       "4  1977      6    7"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.loc[0:4,['year','month','day']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "47ff12fd-b602-4bc8-a806-ffa318d45fd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[57, 64, 61, 47, 53, 57, 46, 60, 48, 59]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Row별로 년,월,일 컬럼의 값으로 나이를 계산하고 \n",
    "#계산된 나이값을 age_list에 추가하기\n",
    "age_list = []\n",
    "for idx,row in member_df.iterrows():\n",
    "    #print(idx,type(row), row['year'])\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[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "66293341-5602-4d2a-9b12-957ab1f54680",
   "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>1</th>\n",
       "      <td>1967-05-02</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959-11-02</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963-05-27</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1977-06-07</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1971-03-03</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         생년월일  나이\n",
       "1  1967-05-02  57\n",
       "2  1959-11-02  64\n",
       "3  1963-05-27  61\n",
       "4  1977-06-07  47\n",
       "5  1971-03-03  53"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#나이 컬럼추가\n",
    "member_df['나이'] = age_list\n",
    "member_df[['생년월일','나이']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7ddb3694-ea57-406c-9c16-11bd4e1604f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    300.00000\n",
       "mean      56.56000\n",
       "std        7.49223\n",
       "min       32.00000\n",
       "25%       53.00000\n",
       "50%       58.00000\n",
       "75%       61.00000\n",
       "max       82.00000\n",
       "Name: 나이, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['나이'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c275c19e-a1c2-4354-b32b-c1d142b1c344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['이름', '정당', '생년월일', '당선횟수', '선거구', '소속위원회', '이미지', '사무실 전화', '사무실 호실',\n",
       "       '의원 홈페이지', '개별 홈페이지', '이메일', '보좌관', '선임비서관', '비서관', '의원실 안내', '당선횟수2',\n",
       "       '선거구2', 'year', 'month', 'day', '나이'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aee4bc08-a930-4f9e-908d-b4085eab978f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "296    https://www.assembly.go.kr/static/portal/img/o...\n",
       "297    https://www.assembly.go.kr/static/portal/img/o...\n",
       "298    https://www.assembly.go.kr/static/portal/img/o...\n",
       "299    https://www.assembly.go.kr/static/portal/img/o...\n",
       "300    https://www.assembly.go.kr/static/portal/img/o...\n",
       "Name: 이미지, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['이미지'].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8410d06-a75a-486d-8076-3448d76731ad",
   "metadata": {},
   "source": [
    "### 시각화(Visualization)\n",
    "* Image 출력 - IPython에서 제공하는 Image 객체, display() 함수 사용\n",
    "* seaborn - countplot, distplot, heatmap\n",
    "* matplotlib - histogram, pie chart\n",
    "* 한글폰트 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c0e0c842-28b6-48d5-b2ef-0744439455e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.assembly.go.kr/static/portal/img/openassm/new/74ea2ce7977b41978e6769c570c0e90b.png\n",
      "https://www.assembly.go.kr/static/portal/img/openassm/new/21eedc628d844950876d6cdb52a5988a.png\n",
      "https://www.assembly.go.kr/static/portal/img/openassm/new/b265059dddd24f329d9361e3d0f62e4e.jpg\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "\n",
    "for img_url in member_df['이미지'].sample(3):\n",
    "    print(img_url)\n",
    "    #display(Image(url=img_url))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2499a28b-fc0a-4fb5-8af6-4d41087bccea",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "35d9ae11-c5f5-4b6e-a17c-34fb6239b33e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bd365a55-597e-4cee-90cd-74875564984a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Malgun Gothic\n"
     ]
    }
   ],
   "source": [
    "#한글폰트 설정\n",
    "font_path = 'C:\\\\Windows\\\\Fonts\\\\malgun.ttf'\n",
    "# font의 파일정보로 font name을 알아내기\n",
    "font_prop = fm.FontProperties(fname=font_path).get_name()\n",
    "print(font_prop)\n",
    "# matplotlib의 rc(run command) 함수를 사용해서 폰트이름 설정\n",
    "matplotlib.rc('font', family=font_prop)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc06c0b-cdfb-4a46-b33b-99732045abbf",
   "metadata": {},
   "source": [
    "#### Seaborn의 막대그래프\n",
    "* barplot - x축과 y축을 둘 다 설정할 수 있음\n",
    "* countplot - x축 이나 y축 중에서 하나만 설정할 수 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6117fef3-8d66-4733-9858-b82fffa61d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "정당\n",
       "더불어민주당    170\n",
       "국민의힘      108\n",
       "조국혁신당      12\n",
       "진보당         3\n",
       "개혁신당        3\n",
       "새로운미래       1\n",
       "기본소득당       1\n",
       "무소속         1\n",
       "사회민주당       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['정당'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f824031e-2a29-45c3-977b-ef590534f9c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['더불어민주당', '국민의힘', '조국혁신당', '진보당', '개혁신당', '새로운미래', '기본소득당', '무소속',\n",
       "       '사회민주당'],\n",
       "      dtype='object', name='정당')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['정당'].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f19cc1ec-5bf8-4a7d-9452-4cfc2496cb0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='당선횟수2', ylabel='count'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,(axes1,axes2) = plt.subplots(nrows=2, ncols=1)\n",
    "figure.set_size_inches(18,12)\n",
    "\n",
    "sns.countplot(data=member_df, x='정당', ax=axes1, order=member_df['정당'].value_counts().index, hue='정당')\n",
    "sns.countplot(data=member_df, x='당선횟수2', ax=axes2, order=member_df['당선횟수2'].value_counts().index,\\\n",
    "              hue='당선횟수2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b68f4b51-c762-45d1-a905-3318adedb521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='count', ylabel='선거구2'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,axes1 = plt.subplots()\n",
    "figure.set_size_inches(18,12)\n",
    "\n",
    "sns.countplot(data=member_df, y='선거구2', ax=axes1, order=member_df['선거구2'].value_counts().index, hue='선거구2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "31df3b7c-8576-4ed8-9ab8-f72b589e7eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='정당', ylabel='나이'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#seaborn의 Scatterplot(산점도) 그리기\n",
    "#선거구2 와 나이 분포를 확인하기\n",
    "#정당과 나이분포를 확인하기\n",
    "\n",
    "figure,(axes1,axes2) = plt.subplots(nrows=2, ncols=1)\n",
    "figure.set_size_inches(18,12)\n",
    "\n",
    "sns.scatterplot(data=member_df, x='선거구2', y='나이', ax=axes1, hue='선거구2')\n",
    "sns.scatterplot(data=member_df, x='정당', y='나이', ax=axes2, hue='정당')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bc4e1a32-e3dc-4012-8de2-c0d38ded52ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ae492754-f84c-4af4-a6f5-ab60d8a5e15d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='나이', ylabel='Count'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#seaborn 의 distplot() (히스토그램) 그리기\n",
    "#나이 값의 분포를 확인하기\n",
    "\n",
    "figure,axes1 = plt.subplots()\n",
    "figure.set_size_inches(18,12)\n",
    "\n",
    "sns.histplot(data=member_df, x='나이', hue='나이')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "617bf5e5-8277-4f22-b769-902c65d78180",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  6.  11.  14.  29.  61. 113.  52.  12.   1.   1.]\n",
      "[32. 37. 42. 47. 52. 57. 62. 67. 72. 77. 82.]\n",
      "<BarContainer object of 10 artists>\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#matplotlib를 사용해서 Histogram 그리기\n",
    "arrays,bins,patches = plt.hist(member_df['나이'])\n",
    "print(arrays)\n",
    "print(bins)\n",
    "print(patches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "98028b2c-14d3-4243-af88-3485abe35e2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['경기', '서울', '비례', '부산', '경남', '인천', '경북', '대구', '충남', '전북', '전남', '충북',\n",
      "       '광주', '강원', '대전', '울산', '제주', '세종'],\n",
      "      dtype='object', name='선거구2')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "선거구2\n",
       "경기    0.200000\n",
       "서울    0.160000\n",
       "비례    0.153333\n",
       "부산    0.060000\n",
       "경남    0.053333\n",
       "인천    0.046667\n",
       "경북    0.043333\n",
       "대구    0.040000\n",
       "충남    0.036667\n",
       "전북    0.033333\n",
       "전남    0.033333\n",
       "충북    0.026667\n",
       "광주    0.026667\n",
       "강원    0.026667\n",
       "대전    0.023333\n",
       "울산    0.020000\n",
       "제주    0.010000\n",
       "세종    0.006667\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#row count를 %(퍼센티지) 비율로 나타내려면 normalize=True 로 설정\n",
    "cdf = member_df['선거구2'].value_counts(normalize=True)\n",
    "print(cdf.index)\n",
    "cdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dabb6f6f-7b74-47aa-a742-8cb5665e15a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '선거구 분포비율')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#matplotlib의 pie chart 그리기\n",
    "#figure size 설정\n",
    "\n",
    "#autopct는 값의 퍼센티지 포맷을 설정\n",
    "#startangle은 첫번째 pie의 각도 설정\n",
    "\n",
    "#pie plot를 그릴때 원의 형태를 유지하도록 하는 설정\n",
    "plt.pie(cdf, labels=cdf.index, autopct='%1.1f%%', startangle=140, shadow=True)\n",
    "#pie plot를 그릴때 원의 형태를 유지하도록 하는 설정\n",
    "plt.axis('equal')\n",
    "plt.title('선거구 분포비율')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa08cf39-73c3-4c77-ba3f-e6a003f0ad31",
   "metadata": {},
   "source": [
    "### Pivot Table \n",
    "* 피봇 테이블은 데이터를 요약하는 통계표\n",
    "* index : 행 위치에 들어갈 컬럼명을 설정하는 속성 \n",
    "* colums : 열 위치에 들어갈 컬럼명을 설정하는 속성\n",
    "* aggfunc : 집계함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "352eaaf0-6d83-4084-8e7d-d51e5dfecb32",
   "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>개혁신당</th>\n",
       "      <th>국민의힘</th>\n",
       "      <th>기본소득당</th>\n",
       "      <th>더불어민주당</th>\n",
       "      <th>무소속</th>\n",
       "      <th>사회민주당</th>\n",
       "      <th>새로운미래</th>\n",
       "      <th>조국혁신당</th>\n",
       "      <th>진보당</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "정당  개혁신당  국민의힘  기본소득당  더불어민주당  무소속  사회민주당  새로운미래  조국혁신당  진보당\n",
       "나이                                                          \n",
       "32   0.0   0.0    0.0     1.0  0.0    0.0    0.0    0.0  0.0\n",
       "33   0.0   1.0    0.0     0.0  0.0    0.0    0.0    0.0  0.0\n",
       "34   0.0   0.0    1.0     1.0  0.0    0.0    0.0    0.0  0.0\n",
       "36   0.0   1.0    0.0     1.0  0.0    0.0    0.0    0.0  0.0\n",
       "37   0.0   2.0    0.0     1.0  0.0    0.0    0.0    0.0  0.0\n",
       "38   1.0   1.0    0.0     0.0  0.0    0.0    0.0    0.0  0.0\n",
       "39   1.0   0.0    0.0     2.0  0.0    0.0    0.0    0.0  0.0\n",
       "40   0.0   1.0    0.0     1.0  0.0    0.0    0.0    0.0  0.0\n",
       "41   0.0   0.0    0.0     1.0  0.0    0.0    0.0    0.0  0.0\n",
       "42   1.0   0.0    0.0     0.0  0.0    0.0    0.0    0.0  0.0\n",
       "43   0.0   2.0    0.0     0.0  0.0    0.0    0.0    0.0  0.0\n",
       "44   0.0   2.0    0.0     4.0  0.0    0.0    0.0    0.0  0.0\n",
       "45   0.0   1.0    0.0     1.0  0.0    0.0    0.0    0.0  0.0\n",
       "46   0.0   1.0    0.0     2.0  0.0    0.0    0.0    0.0  0.0\n",
       "47   0.0   2.0    0.0     1.0  0.0    0.0    0.0    0.0  0.0\n",
       "48   0.0   0.0    0.0     2.0  0.0    0.0    0.0    0.0  1.0\n",
       "49   0.0   3.0    0.0     0.0  0.0    0.0    0.0    1.0  0.0\n",
       "50   0.0   2.0    0.0     6.0  0.0    0.0    0.0    0.0  0.0\n",
       "51   0.0   1.0    0.0     8.0  0.0    1.0    0.0    1.0  0.0\n",
       "52   0.0   0.0    0.0     2.0  0.0    0.0    0.0    1.0  1.0\n",
       "53   0.0   8.0    0.0     7.0  0.0    0.0    0.0    1.0  0.0\n",
       "54   0.0   1.0    0.0     8.0  0.0    0.0    0.0    0.0  0.0\n",
       "55   0.0   3.0    0.0    12.0  0.0    0.0    0.0    1.0  0.0\n",
       "56   0.0   5.0    0.0    10.0  0.0    0.0    0.0    1.0  0.0\n",
       "57   0.0   5.0    0.0    15.0  0.0    0.0    0.0    1.0  0.0\n",
       "58   0.0   5.0    0.0    14.0  0.0    0.0    0.0    0.0  0.0\n",
       "59   0.0   7.0    0.0    18.0  0.0    0.0    0.0    2.0  0.0\n",
       "60   0.0  11.0    0.0     9.0  0.0    0.0    1.0    1.0  1.0\n",
       "61   0.0  13.0    0.0     8.0  0.0    0.0    0.0    2.0  0.0\n",
       "62   0.0   6.0    0.0     9.0  0.0    0.0    0.0    0.0  0.0\n",
       "63   0.0   8.0    0.0     8.0  0.0    0.0    0.0    0.0  0.0\n",
       "64   0.0   5.0    0.0     2.0  0.0    0.0    0.0    0.0  0.0\n",
       "65   0.0   4.0    0.0     5.0  0.0    0.0    0.0    0.0  0.0\n",
       "66   0.0   2.0    0.0     2.0  1.0    0.0    0.0    0.0  0.0\n",
       "67   0.0   2.0    0.0     2.0  0.0    0.0    0.0    0.0  0.0\n",
       "68   0.0   0.0    0.0     1.0  0.0    0.0    0.0    0.0  0.0\n",
       "69   0.0   0.0    0.0     1.0  0.0    0.0    0.0    0.0  0.0\n",
       "70   0.0   2.0    0.0     1.0  0.0    0.0    0.0    0.0  0.0\n",
       "71   0.0   1.0    0.0     2.0  0.0    0.0    0.0    0.0  0.0\n",
       "72   0.0   0.0    0.0     1.0  0.0    0.0    0.0    0.0  0.0\n",
       "82   0.0   0.0    0.0     1.0  0.0    0.0    0.0    0.0  0.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.pivot_table(index='나이', columns='정당', aggfunc='size').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1995df8d-10f3-437e-968d-caaec1b266e8",
   "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>개혁신당</th>\n",
       "      <th>국민의힘</th>\n",
       "      <th>기본소득당</th>\n",
       "      <th>더불어민주당</th>\n",
       "      <th>무소속</th>\n",
       "      <th>사회민주당</th>\n",
       "      <th>새로운미래</th>\n",
       "      <th>조국혁신당</th>\n",
       "      <th>진보당</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>36</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>37</th>\n",
       "      <td>0</td>\n",
       "      <td>2</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "정당  개혁신당  국민의힘  기본소득당  더불어민주당  무소속  사회민주당  새로운미래  조국혁신당  진보당\n",
       "나이                                                          \n",
       "32     0     0      0       1    0      0      0      0    0\n",
       "33     0     1      0       0    0      0      0      0    0\n",
       "34     0     0      1       1    0      0      0      0    0\n",
       "36     0     1      0       1    0      0      0      0    0\n",
       "37     0     2      0       1    0      0      0      0    0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#피봇테이블 그려서 NaN값을 0으로 채우고, 타입을 정수 타입으로 변경\n",
    "age_pivot_df=member_df.pivot_table(index='나이', columns='정당', aggfunc='size').fillna(0).astype(int)\n",
    "age_pivot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d107549b-2ee6-49eb-b7de-09f929bf8a54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='정당', ylabel='나이'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#seaborn의 Heatmap 그리기\n",
    "sns.heatmap(age_pivot_df, fmt='d', linewidths=1, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8438ec98-93a2-4559-a052-9b28b6dabe8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#나이구간 컬럼을 추가\n",
    "#해당조건을 만족하는 행을 선택하면서, 새로운 컬럼을 추가하는 방법\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": 50,
   "id": "6293a542-259b-445d-934c-4614c8fd82a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['이름', '정당', '생년월일', '당선횟수', '선거구', '소속위원회', '이미지', '사무실 전화', '사무실 호실',\n",
       "       '의원 홈페이지', '개별 홈페이지', '이메일', '보좌관', '선임비서관', '비서관', '의원실 안내', '당선횟수2',\n",
       "       '선거구2', 'year', 'month', 'day', '나이', '나이구간'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "73c3c96d-aa56-4a89-8e08-32bdd53fba0f",
   "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>나이</th>\n",
       "      <th>나이구간</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>53</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   나이  나이구간\n",
       "1  57  50.0\n",
       "2  64  60.0\n",
       "3  61  60.0\n",
       "4  47  40.0\n",
       "5  53  50.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df[['나이','나이구간']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "42b82e36-9a5e-4044-af10-cb2abe8537cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(member_df['나이구간'].dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "97ab0cc9-0878-4425-ba9d-124edb1e80d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "나이구간\n",
       "50.0    147\n",
       "60.0    104\n",
       "40.0     27\n",
       "30.0     14\n",
       "70.0      8\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['나이구간'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "091977b3-a5fd-4bd5-b325-b00b3c7228a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int32\n"
     ]
    }
   ],
   "source": [
    "#나이구간 컬럼의 타입을 float -> int 로 변경하기\n",
    "member_df=member_df.astype({'나이구간':int})\n",
    "print(member_df['나이구간'].dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9279a938-dcd5-45ad-8d50-a9bea8c0adb7",
   "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>개혁신당</th>\n",
       "      <th>국민의힘</th>\n",
       "      <th>기본소득당</th>\n",
       "      <th>더불어민주당</th>\n",
       "      <th>무소속</th>\n",
       "      <th>사회민주당</th>\n",
       "      <th>새로운미래</th>\n",
       "      <th>조국혁신당</th>\n",
       "      <th>진보당</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</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>40</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</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    개혁신당  국민의힘  기본소득당  더불어민주당  무소속  사회민주당  새로운미래  조국혁신당  진보당\n",
       "나이구간                                                          \n",
       "30       2     5      1       6    0      0      0      0    0\n",
       "40       1    12      0      12    0      0      0      1    1\n",
       "50       0    37      0     100    0      1      0      8    1\n",
       "60       0    51      0      47    1      0      1      3    1\n",
       "70       0     3      0       5    0      0      0      0    0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agesection_pivot_df=member_df.pivot_table(index='나이구간', columns='정당', aggfunc='size').fillna(0).astype(int)\n",
    "agesection_pivot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c935afd1-2974-4653-bc04-c0105d870172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='정당', ylabel='나이구간'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(agesection_pivot_df, linewidths=1, annot=True, fmt='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3551015c-4abc-458e-9532-acf3cd537692",
   "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>30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "30    NaN   5.0   NaN  1.0  NaN  1.0  NaN   NaN   4.0   2.0  NaN  NaN  1.0   \n",
       "40    NaN   4.0   NaN  3.0  1.0  NaN  2.0   2.0   9.0   4.0  NaN  2.0  NaN   \n",
       "50    4.0  34.0   5.0  3.0  5.0  2.0  2.0  11.0  22.0  27.0  1.0  NaN  6.0   \n",
       "60    3.0  15.0  11.0  5.0  2.0  9.0  2.0   5.0  11.0  15.0  1.0  4.0  7.0   \n",
       "70    1.0   2.0   NaN  1.0  NaN  NaN  1.0   NaN   NaN   NaN  NaN  NaN  NaN   \n",
       "\n",
       "선거구2   전남   전북   제주   충남   충북  \n",
       "나이구간                           \n",
       "30    NaN  NaN  NaN  NaN  NaN  \n",
       "40    NaN  NaN  NaN  NaN  NaN  \n",
       "50    6.0  5.0  3.0  7.0  4.0  \n",
       "60    3.0  4.0  NaN  4.0  3.0  \n",
       "70    1.0  1.0  NaN  NaN  1.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#나이구간 과 선거구2\n",
    "member_df.pivot_table(index='나이구간',columns='선거구2', aggfunc='size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "af689523-17b0-404a-bba2-cca4be46a4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>나이구간</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강원</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경기</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경남</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경북</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대전</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>비례</th>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>세종</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>울산</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전남</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전북</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주</th>\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>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "나이구간   30   40    50    60   70\n",
       "선거구2                           \n",
       "강원    NaN  NaN   4.0   3.0  1.0\n",
       "경기    5.0  4.0  34.0  15.0  2.0\n",
       "경남    NaN  NaN   5.0  11.0  NaN\n",
       "경북    1.0  3.0   3.0   5.0  1.0\n",
       "광주    NaN  1.0   5.0   2.0  NaN\n",
       "대구    1.0  NaN   2.0   9.0  NaN\n",
       "대전    NaN  2.0   2.0   2.0  1.0\n",
       "부산    NaN  2.0  11.0   5.0  NaN\n",
       "비례    4.0  9.0  22.0  11.0  NaN\n",
       "서울    2.0  4.0  27.0  15.0  NaN\n",
       "세종    NaN  NaN   1.0   1.0  NaN\n",
       "울산    NaN  2.0   NaN   4.0  NaN\n",
       "인천    1.0  NaN   6.0   7.0  NaN\n",
       "전남    NaN  NaN   6.0   3.0  1.0\n",
       "전북    NaN  NaN   5.0   4.0  1.0\n",
       "제주    NaN  NaN   3.0   NaN  NaN\n",
       "충남    NaN  NaN   7.0   4.0  NaN\n",
       "충북    NaN  NaN   4.0   3.0  1.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df.pivot_table(index='선거구2',columns='나이구간', aggfunc='size')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05017476-f578-463b-a484-1650b8a62bf2",
   "metadata": {},
   "source": [
    "### Mission1 \n",
    "* 1. 선거구3 컬럼을 새로 생성\n",
    "    * 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",
    "    * isin() 함수 사용\n",
    "* 2. Pivot Table 만들기\n",
    "   행은 선거구3, 열은 나이구간 \n",
    "* 3. 생성한 pivot table을 사용하여  Heatmap 그리기\n",
    "   * heatmap 색상 변경(선택)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "72f7ba34-1812-4690-963b-450997bd2626",
   "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']),'선거구3'] = '충청권'\n",
    "member_df.loc[member_df['선거구2'].isin(['경남','경북','대구','울산','부산']),'선거구3'] = '경상권'\n",
    "member_df.loc[member_df['선거구2'].isin(['광주','전남','전북']),'선거구3'] = '전라권'\n",
    "member_df.loc[member_df['선거구2'] == '강원','선거구3'] = '강원권'\n",
    "member_df.loc[member_df['선거구2'] == '제주','선거구3'] = '제주권'\n",
    "member_df.loc[member_df['선거구2'] == '비례','선거구3'] = '비례'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b49879dd-39a4-4196-91b2-3a5b2cc1a4a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "선거구3\n",
       "수도권    122\n",
       "경상권     65\n",
       "비례      46\n",
       "충청권     28\n",
       "전라권     28\n",
       "강원권      8\n",
       "제주권      3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "member_df['선거구3'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d04d7220-e9a1-481d-b426-c99ae260ea47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "member_df['선거구3'].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "28ddf7c2-cf16-4621-8eea-63035ffcb07d",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>나이구간</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강원권</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경상권</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>비례</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>수도권</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>67</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전라권</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주권</th>\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>2</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "나이구간  30  40  50  60  70\n",
       "선거구3                    \n",
       "강원권    0   0   4   3   1\n",
       "경상권    2   7  21  34   1\n",
       "비례     4   9  22  11   0\n",
       "수도권    8   8  67  37   2\n",
       "전라권    0   1  16   9   2\n",
       "제주권    0   0   3   0   0\n",
       "충청권    0   2  14  10   2"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_pivot_df = member_df.pivot_table(index='선거구3',columns='나이구간',aggfunc='size').fillna(0).astype(int)\n",
    "area_pivot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d468bec8-ff44-4dbc-ad01-704cf9d15f6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='나이구간', ylabel='선거구3'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(area_pivot_df, annot=True, fmt='d', cmap=sns.light_palette('red'),linewidths=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4214e58b-80c3-4f0c-893a-55ebc1704cf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='나이구간', ylabel='선거구3'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "17d6592b-11d2-4b68-a7c6-0dbc1cf926ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['이름', '정당', '생년월일', '당선횟수', '선거구', '소속위원회', '이미지', '사무실 전화', '사무실 호실',\n",
       "       '의원 홈페이지', '개별 홈페이지', '이메일', '보좌관', '선임비서관', '비서관', '의원실 안내', '당선횟수2',\n",
       "       '선거구2', 'year', 'month', 'day', '나이', '나이구간', '선거구3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### DB에 저장\n",
    "member_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1c92ada0",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_list = ['이름', '정당', '나이', '당선횟수2', '선거구2', '소속위원회', '이미지', '사무실 전화', '사무실 호실',\n",
    "       '의원 홈페이지', '개별 홈페이지', '이메일']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4e303ce7-c2db-489c-a6ed-71f066d0a3aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['이름', '정당', '나이', '당선횟수2', '선거구2', '소속위원회', '이미지', '사무실 전화', '사무실 호실',\n",
      "       '의원 홈페이지', '개별 홈페이지', '이메일'],\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>정당</th>\n",
       "      <th>나이</th>\n",
       "      <th>당선횟수2</th>\n",
       "      <th>선거구2</th>\n",
       "      <th>소속위원회</th>\n",
       "      <th>이미지</th>\n",
       "      <th>사무실 전화</th>\n",
       "      <th>사무실 호실</th>\n",
       "      <th>의원 홈페이지</th>\n",
       "      <th>개별 홈페이지</th>\n",
       "      <th>이메일</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>강경숙</td>\n",
       "      <td>조국혁신당</td>\n",
       "      <td>57</td>\n",
       "      <td>초선</td>\n",
       "      <td>비례</td>\n",
       "      <td>교육위원회</td>\n",
       "      <td>https://www.assembly.go.kr/static/portal/img/o...</td>\n",
       "      <td>02-784-5601</td>\n",
       "      <td>의원회관515호</td>\n",
       "      <td>www.assembly.go.kr/members/22nd/KANGKYUNGSOOK</td>\n",
       "      <td></td>\n",
       "      <td>gracesook52@rebuildingk.kr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>강대식</td>\n",
       "      <td>국민의힘</td>\n",
       "      <td>64</td>\n",
       "      <td>재선</td>\n",
       "      <td>대구</td>\n",
       "      <td>국방위원회</td>\n",
       "      <td>https://www.assembly.go.kr/static/portal/img/o...</td>\n",
       "      <td>02-784-5275</td>\n",
       "      <td>의원회관341호</td>\n",
       "      <td>www.assembly.go.kr/members/22nd/KANGDAESIK</td>\n",
       "      <td></td>\n",
       "      <td>kds21341@naver.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    이름     정당  나이 당선횟수2 선거구2  소속위원회  \\\n",
       "1  강경숙  조국혁신당  57    초선   비례  교육위원회   \n",
       "2  강대식   국민의힘  64    재선   대구  국방위원회   \n",
       "\n",
       "                                                 이미지       사무실 전화    사무실 호실  \\\n",
       "1  https://www.assembly.go.kr/static/portal/img/o...  02-784-5601  의원회관515호   \n",
       "2  https://www.assembly.go.kr/static/portal/img/o...  02-784-5275  의원회관341호   \n",
       "\n",
       "                                         의원 홈페이지 개별 홈페이지  \\\n",
       "1  www.assembly.go.kr/members/22nd/KANGKYUNGSOOK           \n",
       "2     www.assembly.go.kr/members/22nd/KANGDAESIK           \n",
       "\n",
       "                          이메일  \n",
       "1  gracesook52@rebuildingk.kr  \n",
       "2          kds21341@naver.com  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_df = member_df.loc[:,column_list]\n",
    "print(table_df.columns)\n",
    "table_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ee0ca40d-d824-453a-ada1-3f67dc680e95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['이름', '정당', '나이', '당선횟수', '선거구', '소속위원회', '이미지', '사무실 전화', '사무실 호실',\n",
       "       '의원 홈페이지', '개별 홈페이지', '이메일'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#컬럼명 변경\n",
    "table_df.rename(inplace=True, columns={'선거구2':'선거구','당선횟수2':'당선횟수'})\n",
    "table_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bb529d60-cdfa-4ba2-86f9-c475783f6a9b",
   "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>당선횟수</th>\n",
       "      <th>선거구</th>\n",
       "      <th>소속위원회</th>\n",
       "      <th>이미지</th>\n",
       "      <th>사무실 전화</th>\n",
       "      <th>사무실 호실</th>\n",
       "      <th>의원 홈페이지</th>\n",
       "      <th>개별 홈페이지</th>\n",
       "      <th>이메일</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>강경숙</td>\n",
       "      <td>조국혁신당</td>\n",
       "      <td>57</td>\n",
       "      <td>초선</td>\n",
       "      <td>비례</td>\n",
       "      <td>교육위원회</td>\n",
       "      <td>https://www.assembly.go.kr/static/portal/img/o...</td>\n",
       "      <td>02-784-5601</td>\n",
       "      <td>의원회관515호</td>\n",
       "      <td>www.assembly.go.kr/members/22nd/KANGKYUNGSOOK</td>\n",
       "      <td></td>\n",
       "      <td>gracesook52@rebuildingk.kr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>강대식</td>\n",
       "      <td>국민의힘</td>\n",
       "      <td>64</td>\n",
       "      <td>재선</td>\n",
       "      <td>대구</td>\n",
       "      <td>국방위원회</td>\n",
       "      <td>https://www.assembly.go.kr/static/portal/img/o...</td>\n",
       "      <td>02-784-5275</td>\n",
       "      <td>의원회관341호</td>\n",
       "      <td>www.assembly.go.kr/members/22nd/KANGDAESIK</td>\n",
       "      <td></td>\n",
       "      <td>kds21341@naver.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    이름     정당  나이 당선횟수 선거구  소속위원회  \\\n",
       "1  강경숙  조국혁신당  57   초선  비례  교육위원회   \n",
       "2  강대식   국민의힘  64   재선  대구  국방위원회   \n",
       "\n",
       "                                                 이미지       사무실 전화    사무실 호실  \\\n",
       "1  https://www.assembly.go.kr/static/portal/img/o...  02-784-5601  의원회관515호   \n",
       "2  https://www.assembly.go.kr/static/portal/img/o...  02-784-5275  의원회관341호   \n",
       "\n",
       "                                         의원 홈페이지 개별 홈페이지  \\\n",
       "1  www.assembly.go.kr/members/22nd/KANGKYUNGSOOK           \n",
       "2     www.assembly.go.kr/members/22nd/KANGDAESIK           \n",
       "\n",
       "                          이메일  \n",
       "1  gracesook52@rebuildingk.kr  \n",
       "2          kds21341@naver.com  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "76287e25-3bcf-4274-bf97-306e99b327cd",
   "metadata": {},
   "outputs": [],
   "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",
    "engine = None\n",
    "conn = None\n",
    "try:\n",
    "    #'mysql+mysqldb://scott:tiger@localhost/foo'\n",
    "    #Engine 객체 생성\n",
    "    engine = create_engine('mysql+mysqldb://python:python@localhost:3307/python_db?charset=utf8mb4')\n",
    "    #Engine을 사용해서 DB에 연결 \n",
    "    conn = engine.connect()\n",
    "\n",
    "    #DataFrame의 to_sql() 함수로 dataframe 객체를 Table로 저정\n",
    "    table_df.to_sql(name='member', con=engine, if_exists='replace',index=True, index_label='id')\n",
    "finally:\n",
    "    if conn is not None: \n",
    "        conn.close()\n",
    "    if engine is not None:\n",
    "        engine.dispose()"
   ]
  }
 ],
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