2020_SPRING_RUSH/week6/week6_6_Self_oil_station_price.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🦉COSADAMA Introduction to Data Science Study\n",
    "\n",
    "- 일자: 2020-05-11\n",
    "- 작성자: 박하람 \n",
    "- 참고자료: 파이썬으로 데이터 주무르기(민형기)\n",
    "- 교재: 169 - 198쪽 (깃헙 교안으로 공부하면서, 궁금한 부분은 교재를 참고하세요. 혹은 깃헙 교안을 다 보고 교재를 복습 겸 읽어봐도 됩니다!)\n",
    "\n",
    "## 6. 셀프 주유소는 정말 저렴할까\n",
    "\n",
    "모두들 중간고사는 무사히 잘 치르셨나요? 스스로 해보는 데이터 분석 과제도 어떤 자료를 가지고 하셨는지 무척 궁금하네요😊 오늘은 중간고사 전 마지막 주차에서 배운 웹 크롤링 BeautifulSoup을 다시 활용해볼 것이구요, urllib 라이브러리 대신 Selenium을 사용해 urllib으로 크롤링이 불가능한 사이트를 크롤링해보려 합니다. 더불어 중간고사 이전부터 학습한 pandas, Google Maps, Folium과 Seaborn 등도 모두 다시 활용해 볼 것이에요. 좋은 리뷰가 될 것 같죠?💖\n",
    "\n",
    "이번 프로젝트는 '셀프 주요소는 정말로 저렴한지'를 팩트 체크해보는 것입니다. 한국석유공사 오피넷에서 Selenium으로 데이터를 크롤링해볼 것이구요, 엑셀파일에 담아 glob 모듈을 활용해 다수의 엑셀파일을 위치를 찾아 읽어보는 것까지 해볼 겁니다. 또한 박스 플롯으로 시각화해 실제로 셀프 주유소가 저렴한지 알아보고, 대체적으로 휘발유 값이 저렴한 구는 어디인지 Google Maps와 Folium으로 시각화해볼 겁니다.(익숙하죵?ㅎㅎ) 마지막으로는 주유소 가격 상하위 10개를 뽑아 지도에 시각화해 볼 것이랍니다. 이번 주차도 매우 흥미로운 주제이나, 다만 Selenium을 활용하는 것이 어려울 수 있어요. 그러니 궁금한 것이나 막히는 부분이 있다면 언제든 슬랙으로 질문해주세요! 그럼 이번 주차 공부도 시작합니다🐻\n",
    "\n",
    "### 6.1 Selenium 사용하기\n",
    "\n",
    "지난 시간에는 urllib을 활용해서 정보를 가져왔어요. 하지만 이번에는 urllib 라이브러리를 사용해 크롤링할 수 없기 때문에 이 때 사용하는 Selenium를 배워볼 것이에요. 우선, 우리가 주유소 가격과 위치를 끌어오려면, 주유소 정보들이 있는 opinet 사이트를 이용해보려 합니다. \n",
    "\n",
    "- [opinet - 싼 주유소 찾기 - 지역별](http://www.opinet.co.kr/searRgSelect.do): 이 페이지를 선택하면 opinet 사이트가 나올 것이에요. 여기에서 싼 주유소 찾기를 누르고, 지역별을 누르면 우리가 얻고자 하는 사이트가 나와요. \n",
    "\n",
    "그런데 문제는 이렇게 지역별 사이트에 들어가도, url 주소가 달라지지 않았기 때문에 해당 url을 복사해 다시 로드하면 Opinet 처음 사이트로 돌아가게 된답니다. 그럴 때에는 url 주소만 있으면 되는 urllib를 사용할 수 없겠죠. 그렇게 해서 사용하는 것이 바로 Selenium입니다.\n",
    "\n",
    "#### Selenium 설치와 웹 드라이버 \n",
    "\n",
    "Selenium은 터미널에 pip로 설치하시면 됩니다. 그러나 Selenium을 사용하기 위해서는 사용하는 브라우저에 맞춰 웹 드라이버를 다운 받아야 해요. 본인이 사파리를 쓰시면 사파리 웹 드라이버를, 크롬을 쓰시는 크롬 웹 드라이버를 쓰시면 돼요. 저는 크롬 드라이버가 더 깔기 편해서 크롬 드라이버를 추천드려요. \n",
    "\n",
    "- pip install selenium 터미널에 입력 \n",
    "- [본인에게 맞는 웹브라우저 별로 드라이브 설치 - 경로설정 주의](https://sacko.tistory.com/13)\n",
    "\n",
    "웹 브라우저를 다운받으신 후, data나 소스코드가 있는 폴더에 drive를 압축 해제해주시면 된답니다.(교재 171쪽) 이 때 파일경로가 중요한데요, 파일경로를 확인하는 방법은 아주 간단하게 Terminal(명령 프롬프트)을 열고 drive 파일을 터미널에 끌어 올려다 넣으시면 이동경로가 나타나게 됩니다. 아래 분홍색이 이동경로에요.\n",
    "\n",
    "![파일 경로](images/file_path_img.png)\n",
    "\n",
    "이동경로를 기억해 두시고 먼저 selenium을 import 해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from selenium import webdriver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이동경로를 아래에다 넣어주고, get에다가는 원하는 사이트의 주소를 입력합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver = webdriver.Chrome('/Users/harampark/Documents/chromedriver') # 나의 이동경로라 복붙 X\n",
    "driver.get(\"https://nid.naver.com/nidlogin.login?mode=form&url=https%3A%2F%2Fwww.naver.com\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selenium으로 Naver 로그인하기 \n",
    "\n",
    "어, 그러니까 갑자기 새로운 화면창이 떴죠? 위에 'Chrome이 자동화된 테스트 소프트웨어에 의해 제어되고 있습니다.'라고 하는게 뜨면 잘 된 것이랍니다. 웬만하면 이 화면은 직접 건들이지 않고, Selenium이 만지게 하는게 좋아요.(코드를 작성할 때 혼선이 생길 수 있기 때문) \n",
    "\n",
    "스크린샷을 하고 싶다면 아래와 같은 명령을 사용하면 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 스크린샷 - 파일 경로 확인, 확장자 확인, 미리 images 폴더 만들어놓아야 함\n",
    "driver.save_screenshot('001.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![파일 경로](images/001.png)\n",
    "\n",
    "이렇게 나오면 잘 캡처가 되었다고 할 수 있죠. 그럼 이제 Selenium으로 로그인을 해보도록 할게요. 로그인을 하려면, 크롬 개발자 도구를 사용해 아이디와 패스워드가 적힌 곳의 id를 알아야 해요. 잘 기억이 안난다면 136-138쪽을 보시구요, 이번에는 find_element_by_id를 사용해서 id를 찾는 것이랍니다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "elem_login = driver.find_element_by_id(\"id\")\n",
    "elem_login.clear()  # 미리 입력된 글자를 지워주는 것(자동로그인 경우)\n",
    "elem_login.send_keys(\"********\")\n",
    "\n",
    "elem_login = driver.find_element_by_id(\"pw\")\n",
    "elem_login.clear()\n",
    "elem_login.send_keys(\"**********\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![파일 경로](images/002.png)\n",
    "\n",
    "이렇게 스스로 적혀져 있다면 잘 하셨습니다. 그 다음에는 로그인을 눌러야 클릭이 되잖아요? 그 클릭하는 방법은 xpath를 찾아서 클릭하는 방법이에요. 교재 174-175쪽을 보면 xpath copy를 하는 방법을 알 수 있어요. 그렇게 복사한 xpath을 아래에다 적어주시면 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "xpath = \"\"\"//*[@id=\"log.login\"]\"\"\"\n",
    "driver.find_element_by_xpath(xpath).click() # xpath 위치를 클릭해라"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![003](images/003.png)\n",
    "\n",
    "엇, 로그인을 했는데 이렇게 막히네요. 이게 바로 캡차라는 기술인데요, 요렇게 자동으로 로그인이 되는 것을 막기 위해서 컴퓨터를 이용해 로그인 했다고 의심이 되는 로그인은 캡차로 인증하게 합니다. 네이버에서 매크로로 댓글 조작을 했니 어쨌니 이런 논란이 많았잖아요. 네이버는 아무래도 사측에서 암묵적으로 가담했다는 증거를 피하기 위해서 혹은 해킹이 쉽게 되지 않기 위해서 이런 캡차를 해놓은 것이랍니다. \n",
    "\n",
    "여담으로 캡차는 고서를 찍어서 올리는 경우도 있는데요, 이런 캡차도 있지만, 다른 캡차들 중에는 고서에서 컴퓨터가 알아보기 힘들지만, 사람은 입력할 수 있는 단어를 찍어서 올리기도 해요. 그래서 그 데이터를 쌓아서 충분한 데이터가 모이면 컴퓨터도 이제 그것을 읽을 수 있도록 학습하는 거죠. 신기하죵😉\n",
    "\n",
    "#### Selenium으로 다음 사이트 로그인하기 \n",
    "\n",
    "Anyway, 캡차를 피해가는 방법도 있지만, 이게 네이버에서 수시로 피해가는 방법을 막아버리기도 하고, 캡차를 넘어서면서까지 크롤링할 필요는 아직 없어서 다음 사이트에서 로그인을 해볼게요. 앞서 어떻게 사용하는지 알았으니, 다음 로그인 페이지는 아이디와 패스워드를 입력해 클릭하는 것까지 생각해보시고 그 다음 코드를 보면 더 좋을 것 같네요! \n",
    "\n",
    "- [다음 로그인 사이트](https://accounts.kakao.com/login?continue=https%3A%2F%2Flogins.daum.net%2Faccounts%2Fksso.do%3Frescue%3Dtrue%26url%3Dhttps%253A%252F%252Fwww.daum.net%252F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from selenium import webdriver\n",
    "import time\n",
    "\n",
    "driver = webdriver.Chrome('/Users/harampark/Documents/chromedriver')\n",
    "driver.get(\"https://accounts.kakao.com/login?continue=https%3A%2F%2Flogins.daum.net%2Faccounts%2Fksso.do%3Frescue%3Dtrue%26url%3Dhttps%253A%252F%252Fwww.daum.net%252F\")\n",
    "\n",
    "elem_login = driver.find_element_by_id(\"id_email_2\")\n",
    "elem_login.clear()\n",
    "elem_login.send_keys(\"**********\")\n",
    "time.sleep(3)\n",
    "\n",
    "elem_login = driver.find_element_by_id(\"id_password_3\")\n",
    "elem_login.clear()\n",
    "elem_login.send_keys(\"**********\")\n",
    "time.sleep(3)\n",
    "\n",
    "xpath = \"\"\"//*[@id=\"login-form\"]/fieldset/div[8]/button\"\"\"\n",
    "driver.find_element_by_xpath(xpath).click()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](images/004.png)\n",
    "\n",
    "이렇게 나오면 성공! \n",
    "\n",
    "#### 다음 메일 사용자 정보 얻기\n",
    "그 다음에는 메일에 들어가 메일 사용자들을 크롤링해보려 합니다. 그럼 메일 사이트에 접근해야겠죠? Xpath를 이용해 클릭할 수 있겠지만, 아래처럼 바로 메일 사이트에 접근할 수 있어요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 메일 접근 \n",
    "driver.get(\"https://mail.daum.net/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![이메일 접근](images/005.png)\n",
    "\n",
    "위와 같이 나오면 된겁니다. 여기에서 저렇게 빨간색 네모칸에 있는 이메일 보낸 이들을 추출해보려고 합니다. (안 쓰는 메일이라...ㅋㅋㅋㅋㅋ 코어벨 이런거 밖에 없네요.) 그 다음은 익숙한 BeautifulSoup으로 가공해주면 됩니다! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "\n",
    "html = driver.page_source # 현재 접근한 페이지의 소스를 받을 수 있음.\n",
    "soup = BeautifulSoup(html, 'html.parser')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "크롬 개발자 도구를 사용해서 태그가 무엇이고 class는 뭔지도 알아내볼게요. a 태그의 link_from이라는 태그이고, find_all로 다 가져와 보겠습니다. (꼭 찾아보셔야 해요!) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"영어이야기클럽 운영자\" &lt;yhhah@hanmail.net&gt;'>영어이야기클럽 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>,\n",
       " <a class=\"link_from\" href=\"javascript:;\" title='\"코아벨스 미국법 연구소 운영자\" &lt;koa-bels@hanmail.net&gt;'>코아벨스 미국법 연구소 운영자</a>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_list = soup.find_all('a', 'link_from')\n",
    "raw_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "잘 가져와 졌고요. 사용자 이름만 뽑아내려면 아래와 같이 하면 되겠죠."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'코아벨스 미국법 연구소 운영자'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_list[0].get_text()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그럼 for문으로 이 이름들만 가져와 볼게요. 지난 시간에 한거 떠올리시고 먼저 코드가 어떻게 작성될 지 생각한 후에 실행해보고 아래 코드들을 보시면 더 좋을 것 같네요!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "코아벨스 미국법 연구소 운영자\n",
      "코아벨스 미국법 연구소 운영자\n",
      "코아벨스 미국법 연구소 운영자\n",
      "코아벨스 미국법 연구소 운영자\n",
      "영어이야기클럽 운영자\n",
      "코아벨스 미국법 연구소 운영자\n",
      "코아벨스 미국법 연구소 운영자\n",
      "코아벨스 미국법 연구소 운영자\n",
      "코아벨스 미국법 연구소 운영자\n",
      "코아벨스 미국법 연구소 운영자\n",
      "코아벨스 미국법 연구소 운영자\n",
      "코아벨스 미국법 연구소 운영자\n",
      "코아벨스 미국법 연구소 운영자\n",
      "코아벨스 미국법 연구소 운영자\n",
      "코아벨스 미국법 연구소 운영자\n"
     ]
    }
   ],
   "source": [
    "for n in range(0, len(raw_list)):\n",
    "    send_list = raw_list[n].get_text()\n",
    "    print(send_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 정보를 모두 얻었으니, 이만 driver를 종료해봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver.close()  # 크롬 드라이버 종료"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그럼 대략적인 Selenium 사용법에 대해 익힌 것 같네요. 그럼 본격적으로 이번주 프로젝트를 시작해보겠습니다. \n",
    "\n",
    "### 6.2 서울시 구별 주유소 가격 정보 얻기 \n",
    "\n",
    "- [opinet - 싼 주유소 찾기 - 지역별](http://www.opinet.co.kr/searRgSelect.do)\n",
    "\n",
    "지역별 주소 사이트를 selenium으로 들어가려면 어떻게 해야 할까요? 아래와 같이 하면 됩니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver = webdriver.Chrome('/Users/harampark/Documents/chromedriver')\n",
    "driver.get(\"http://www.opinet.co.kr/searRgSelect.do\")\n",
    "driver.get(\"http://www.opinet.co.kr/searRgSelect.do\") # 두번 해야 들어가 짐."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "짜잔! 이렇게 두번 해주면 지역별 사이트에 들어갈 수 있어요. \n",
    "\n",
    "![지역별](images/006.png)\n",
    "\n",
    "서울시 구별로 정보를 얻어올 것이니, 빨간색 박스 마포구라 적혀져 있는 곳을 바꾸어줘야겠고, 바꾼 결과로 조회 버튼까지 눌러줘야겠지요. 또 아래처럼 엑셀 저장 버튼까지 눌러줘야 엑셀로 결과가 저장되겠죠. \n",
    "\n",
    "![엑셀 저장](images/007.png)\n",
    "\n",
    "그럼 우선 '구' 버튼부터 눌르며 바꿔보죠. 이 때 필요한 것이 xpath에요. 크롬 개발자 도구로 해당 구 버튼의 xpath를 copy하고요, 확보한 xpath를 이용해 element를 찾고 gu_list_raw 변수에 저장합니다.\n",
    "\n",
    "- [selenium 문법](https://github.com/SeongJaeMoon/PracPython/blob/master/selenium_dom_selector.md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "gu_list_raw = driver.find_element_by_xpath(\"\"\"//*[@id=\"SIGUNGU_NM0\"]\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그 다음에는 구 이름이 option이라는 태그에 저장되어 있으니까, 아래와 같이 option이라는 태그를 찾아 줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<selenium.webdriver.remote.webelement.WebElement (session=\"a469a7b1c5b169154f414967f8278567\", element=\"d918f706-e514-43ad-81a0-aa58f61d4ba0\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"a469a7b1c5b169154f414967f8278567\", element=\"c10978a4-6320-4406-9574-e4ffe7f1bc5b\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"a469a7b1c5b169154f414967f8278567\", element=\"1ee91ad2-2a32-4ac5-bc5d-7526e91d859a\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"a469a7b1c5b169154f414967f8278567\", element=\"1c19bd81-6e49-4f70-9152-e24535a844ed\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"a469a7b1c5b169154f414967f8278567\", element=\"8cac6a4e-9afb-456b-a659-f226ed6e226e\")>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gu_list = gu_list_raw.find_elements_by_tag_name(\"option\")\n",
    "gu_list[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이것처럼 gu_list에는 우리가 원한게 잘 보이지 않죠? 구 이름은 option의 value라는 곳에 있었으니까요, value를 아래와 같이 가져옵니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['강남구',\n",
       " '강동구',\n",
       " '강북구',\n",
       " '강서구',\n",
       " '관악구',\n",
       " '광진구',\n",
       " '구로구',\n",
       " '금천구',\n",
       " '노원구',\n",
       " '도봉구',\n",
       " '동대문구',\n",
       " '동작구',\n",
       " '마포구',\n",
       " '서대문구',\n",
       " '서초구',\n",
       " '성동구',\n",
       " '성북구',\n",
       " '송파구',\n",
       " '양천구',\n",
       " '영등포구',\n",
       " '용산구',\n",
       " '은평구',\n",
       " '종로구',\n",
       " '중구',\n",
       " '중랑구']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gu_names = [option.get_attribute(\"value\") for option in gu_list]\n",
    "gu_names.remove('')  # 이것을 넣었다 빼보세요. 어떤 기능인지 알 수 있습니다.\n",
    "gu_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gu_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "리스트로 구성되어 있죠? 총 구가 25이네요. 이후 엑셀 파일이 총 25개가 다운로드 되었는지 확인하셔야 해요. 구 이름이 있는 태그에 gu_names에서 첫번째 것만 한번 입력해보도록 할게요. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "element = driver.find_element_by_id(\"SIGUNGU_NM0\")\n",
    "element.send_keys(gu_names[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![강남구](images/008.png)\n",
    "\n",
    "저렇게 강남구로 바뀌었죠? 그러면 이제는 조회버튼을 눌러야 되겠네요. 그럼 조회 버튼은 또 xpath를 사용해 클릭해주면 됩니다. (조회버튼 xpath copy하는 것 잊지 마세요!) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#조회버튼의 Xpath를 찾아서 클릭\n",
    "xpath ='''//*[@id=\"searRgSelect\"]/span'''\n",
    "element_sel_gu = driver.find_element_by_xpath(xpath).click()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![강남구 조회](images/009.png)\n",
    "\n",
    "이렇게 바뀌었다면, okay! 이제는 엑셀을 얻어야 하니까, 또 xpath를 알아내서 엑셀 저장 버튼을 클릭하면 되겠죠! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "xpath ='''//*[@id=\"glopopd_excel\"]/span'''\n",
    "element_get_excel = driver.find_element_by_xpath(xpath).click()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그러면 웹 브라우저에서 다운로드 시 사용되는 폴더에서 '지역_위치별(주유소)'가 생성된 것을 확인할 수 있을 겁니다. \n",
    "\n",
    "그렇다면 이제 알았으니, 전체 구들의 엑셀 파일을 모두 가져와봅시다. 시간이 꽤 오래 걸리는 작업일 것이기 때문에 tqdm을 사용해서 걸리는 시간을 시각화 해볼 것이구요. 어떻게 코드가 구성될지 한번 생각해보신다음 아래 코드를 확인하세요! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7d1ffd8f2ba470abc7c42baaa49a63d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=25), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "for gu in tqdm_notebook(gu_names): \n",
    "    element = driver.find_element_by_id(\"SIGUNGU_NM0\")\n",
    "    element.send_keys(gu)\n",
    "    \n",
    "    time.sleep(2) # 중간중간 기다리라는 time.sleep 명령 \n",
    "    \n",
    "    xpath ='''//*[@id=\"searRgSelect\"]/span'''\n",
    "    element_sel_gu = driver.find_element_by_xpath(xpath).click()\n",
    "    \n",
    "    time.sleep(1)\n",
    "    \n",
    "    xpath ='''//*[@id=\"glopopd_excel\"]/span'''\n",
    "    element_get_excel = driver.find_element_by_xpath(xpath).click()\n",
    "    \n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![다운로드](images/010.png)\n",
    "\n",
    "이렇게 총 25개의 파일이 다운로드되어 있으면 됩니다. 그럼 이제 웹 드라이버를 종료하면 끝! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 구별 주유 가격에 대한 데이터의 정리 \n",
    "\n",
    "이제는 구별 주유 가격에 대한 데이터를 정리해보려 합니다. 이제는 정말 익숙해진 pandas를 활용하여 이것저것 요리해부죠. 우선 다운로드 폴더에 담겨져 있는 엑셀 파일을 본인의 코드가 적혀있는 파일에다 옮깁니다. 저는 새롭게 data라는 폴더를 모두 담아서 거기다 넣어놨어요. \n",
    "\n",
    "#### glob으로 원하는 파일 쉽게 가져오기\n",
    "원하는 파일을 가져오려면 파일경로가 필요합니다. 쉽게 파일을 가져올 수 있도록 해주는 모듈이 glob이에요. 해당 모듈들을 import 해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [glob의 간단한 사용법](https://wikidocs.net/83)\n",
    "\n",
    "glob을 이용하면 쉽게 파일들을 가져올 수 있어요. 아래 *는 지역으로 시작하는 것은 모두 가져오라는 거에요. 지역 뒤의 이름은 상관이 없는거죠."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/지역_위치별(주유소) (9).xls',\n",
       " 'data/지역_위치별(주유소) (12).xls',\n",
       " 'data/지역_위치별(주유소) (5).xls',\n",
       " 'data/지역_위치별(주유소) (24).xls',\n",
       " 'data/지역_위치별(주유소) (4).xls',\n",
       " 'data/지역_위치별(주유소) (13).xls',\n",
       " 'data/지역_위치별(주유소) (8).xls',\n",
       " 'data/지역_위치별(주유소) (3).xls',\n",
       " 'data/지역_위치별(주유소) (18).xls',\n",
       " 'data/지역_위치별(주유소) (22).xls',\n",
       " 'data/지역_위치별(주유소) (14).xls',\n",
       " 'data/지역_위치별(주유소) (15).xls',\n",
       " 'data/지역_위치별(주유소) (23).xls',\n",
       " 'data/지역_위치별(주유소) (19).xls',\n",
       " 'data/지역_위치별(주유소) (2).xls',\n",
       " 'data/지역_위치별(주유소) (20).xls',\n",
       " 'data/지역_위치별(주유소) (1).xls',\n",
       " 'data/지역_위치별(주유소) (16).xls',\n",
       " 'data/지역_위치별(주유소) (17).xls',\n",
       " 'data/지역_위치별(주유소) (21).xls',\n",
       " 'data/지역_위치별(주유소) (10).xls',\n",
       " 'data/지역_위치별(주유소).xls',\n",
       " 'data/지역_위치별(주유소) (7).xls',\n",
       " 'data/지역_위치별(주유소) (6).xls',\n",
       " 'data/지역_위치별(주유소) (11).xls']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob('data/지역*.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(glob('data/지역*.xls'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "25개도 맞네요. 그럼 이것들을 모두 가져와서 file에다 저장해보겠습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/지역_위치별(주유소) (9).xls',\n",
       " 'data/지역_위치별(주유소) (12).xls',\n",
       " 'data/지역_위치별(주유소) (5).xls',\n",
       " 'data/지역_위치별(주유소) (24).xls',\n",
       " 'data/지역_위치별(주유소) (4).xls',\n",
       " 'data/지역_위치별(주유소) (13).xls',\n",
       " 'data/지역_위치별(주유소) (8).xls',\n",
       " 'data/지역_위치별(주유소) (3).xls',\n",
       " 'data/지역_위치별(주유소) (18).xls',\n",
       " 'data/지역_위치별(주유소) (22).xls',\n",
       " 'data/지역_위치별(주유소) (14).xls',\n",
       " 'data/지역_위치별(주유소) (15).xls',\n",
       " 'data/지역_위치별(주유소) (23).xls',\n",
       " 'data/지역_위치별(주유소) (19).xls',\n",
       " 'data/지역_위치별(주유소) (2).xls',\n",
       " 'data/지역_위치별(주유소) (20).xls',\n",
       " 'data/지역_위치별(주유소) (1).xls',\n",
       " 'data/지역_위치별(주유소) (16).xls',\n",
       " 'data/지역_위치별(주유소) (17).xls',\n",
       " 'data/지역_위치별(주유소) (21).xls',\n",
       " 'data/지역_위치별(주유소) (10).xls',\n",
       " 'data/지역_위치별(주유소).xls',\n",
       " 'data/지역_위치별(주유소) (7).xls',\n",
       " 'data/지역_위치별(주유소) (6).xls',\n",
       " 'data/지역_위치별(주유소) (11).xls']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_files = glob('data/지역*.xls')\n",
    "stations_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 25개 파일 병합하기\n",
    "\n",
    "pandas로 가공하려면 우선 이 25개의 파일들을 하나의 DataFrame에 담아야 하겠죠? 이를 모두 병합하기 위해서 열 방향으로 합쳐지는 concat 함수를 이용할 겁니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>지역</th>\n",
       "      <th>상호</th>\n",
       "      <th>주소</th>\n",
       "      <th>상표</th>\n",
       "      <th>전화번호</th>\n",
       "      <th>셀프여부</th>\n",
       "      <th>고급휘발유</th>\n",
       "      <th>휘발유</th>\n",
       "      <th>경유</th>\n",
       "      <th>실내등유</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>한이에너지(주)KLP제1주유소</td>\n",
       "      <td>서울 도봉구 도봉로 596 (창동)</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>02-902-5189</td>\n",
       "      <td>N</td>\n",
       "      <td>-</td>\n",
       "      <td>1277</td>\n",
       "      <td>1077</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>도봉제일주유소(주)송만에너지</td>\n",
       "      <td>서울 도봉구 도봉로 783 (도봉동)</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>02-955-5185</td>\n",
       "      <td>N</td>\n",
       "      <td>-</td>\n",
       "      <td>1277</td>\n",
       "      <td>1077</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>정다운셀프주유소</td>\n",
       "      <td>서울특별시 도봉구 도봉로 635 (쌍문동)</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>02-996-5151</td>\n",
       "      <td>Y</td>\n",
       "      <td>-</td>\n",
       "      <td>1279</td>\n",
       "      <td>1079</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>삼미북부셀프주유소</td>\n",
       "      <td>서울특별시 도봉구  도봉로 437 (쌍문동)</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>02-992-5561</td>\n",
       "      <td>Y</td>\n",
       "      <td>-</td>\n",
       "      <td>1279</td>\n",
       "      <td>1079</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>SK네트웍스(주)도봉행복주유소</td>\n",
       "      <td>서울 도봉구  도봉로 941 (도봉동)</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>02-3492-7371</td>\n",
       "      <td>Y</td>\n",
       "      <td>-</td>\n",
       "      <td>1279</td>\n",
       "      <td>1079</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      지역                상호                        주소      상표          전화번호  \\\n",
       "0  서울특별시  한이에너지(주)KLP제1주유소       서울 도봉구 도봉로 596 (창동)  현대오일뱅크   02-902-5189   \n",
       "1  서울특별시   도봉제일주유소(주)송만에너지      서울 도봉구 도봉로 783 (도봉동)  현대오일뱅크   02-955-5185   \n",
       "2  서울특별시          정다운셀프주유소   서울특별시 도봉구 도봉로 635 (쌍문동)   SK에너지   02-996-5151   \n",
       "3  서울특별시         삼미북부셀프주유소  서울특별시 도봉구  도봉로 437 (쌍문동)   SK에너지   02-992-5561   \n",
       "4  서울특별시  SK네트웍스(주)도봉행복주유소     서울 도봉구  도봉로 941 (도봉동)   SK에너지  02-3492-7371   \n",
       "\n",
       "  셀프여부 고급휘발유   휘발유    경유  실내등유  \n",
       "0    N     -  1277  1077     -  \n",
       "1    N     -  1277  1077   900  \n",
       "2    Y     -  1279  1079     -  \n",
       "3    Y     -  1279  1079  1100  \n",
       "4    Y     -  1279  1079     -  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 25개의 DataFrame이 담길 리스트\n",
    "tmp_raw = []\n",
    "\n",
    "for file_name in stations_files:\n",
    "    # 한 개씩 DataFrame을 불러오기\n",
    "    tmp = pd.read_excel(file_name, header=2)\n",
    "    # tmp_raw에다 추가하기\n",
    "    tmp_raw.append(tmp)\n",
    "    \n",
    "# 25개의 DataFrame 병합하기    \n",
    "station_raw = pd.concat(tmp_raw)\n",
    "station_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 우리에게 익숙한 데이터프레임이네요. 여기에는 지역, 상호, 주소, 상표, 셀프여부, 휘발유 값 등이 들어있네요. 우리가 이번에 할 프로젝트의 주제는 셀프 주유소가 정말로 싼지 아닌지 팩트체크를 해보는 것이었잖아요. 그러니까 여기에서 중요한 컬럼은 셀프여부가 되겠구요, 구별로 알아보려고 했으니 주소가 중요할 겁니다. (지역은 모두 서울이라 필요가 없어요.) 이렇게 데이터의 특성을 파악하는게 중요하고요. 전반적으로 어떻게 담겨있는지 알기 위해서 아래를 확인해보았습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 501 entries, 0 to 9\n",
      "Data columns (total 10 columns):\n",
      "지역       501 non-null object\n",
      "상호       501 non-null object\n",
      "주소       501 non-null object\n",
      "상표       501 non-null object\n",
      "전화번호     501 non-null object\n",
      "셀프여부     501 non-null object\n",
      "고급휘발유    501 non-null object\n",
      "휘발유      501 non-null object\n",
      "경유       501 non-null object\n",
      "실내등유     501 non-null object\n",
      "dtypes: object(10)\n",
      "memory usage: 43.1+ KB\n"
     ]
    }
   ],
   "source": [
    "station_raw.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "중요한 컬럼이 휘발유라고 했죠? 휘발유 값만 알아볼 것인데 이게 숫자가 아닌 object로 되어 있어서 숫자형으로 바꾸어줘야 계산이 가능하겠죠. 이를 후에 처리해줘야겠죠. \n",
    "\n",
    "필요한 컬럼들만 추려서 stations에 담아볼게요. 중요한 것이 1) 상호 2) 주소 3) 휘발유 4) 셀프여부 5) 상호니까 이것들만 남겨보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Oil_store</th>\n",
       "      <th>주소</th>\n",
       "      <th>가격</th>\n",
       "      <th>셀프</th>\n",
       "      <th>상표</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>한이에너지(주)KLP제1주유소</td>\n",
       "      <td>서울 도봉구 도봉로 596 (창동)</td>\n",
       "      <td>1277</td>\n",
       "      <td>N</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>도봉제일주유소(주)송만에너지</td>\n",
       "      <td>서울 도봉구 도봉로 783 (도봉동)</td>\n",
       "      <td>1277</td>\n",
       "      <td>N</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>정다운셀프주유소</td>\n",
       "      <td>서울특별시 도봉구 도봉로 635 (쌍문동)</td>\n",
       "      <td>1279</td>\n",
       "      <td>Y</td>\n",
       "      <td>SK에너지</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>삼미북부셀프주유소</td>\n",
       "      <td>서울특별시 도봉구  도봉로 437 (쌍문동)</td>\n",
       "      <td>1279</td>\n",
       "      <td>Y</td>\n",
       "      <td>SK에너지</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>SK네트웍스(주)도봉행복주유소</td>\n",
       "      <td>서울 도봉구  도봉로 941 (도봉동)</td>\n",
       "      <td>1279</td>\n",
       "      <td>Y</td>\n",
       "      <td>SK에너지</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Oil_store                        주소    가격 셀프      상표\n",
       "0  한이에너지(주)KLP제1주유소       서울 도봉구 도봉로 596 (창동)  1277  N  현대오일뱅크\n",
       "1   도봉제일주유소(주)송만에너지      서울 도봉구 도봉로 783 (도봉동)  1277  N  현대오일뱅크\n",
       "2          정다운셀프주유소   서울특별시 도봉구 도봉로 635 (쌍문동)  1279  Y   SK에너지\n",
       "3         삼미북부셀프주유소  서울특별시 도봉구  도봉로 437 (쌍문동)  1279  Y   SK에너지\n",
       "4  SK네트웍스(주)도봉행복주유소     서울 도봉구  도봉로 941 (도봉동)  1279  Y   SK에너지"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations = pd.DataFrame({'Oil_store':station_raw['상호'],\n",
    "                        '주소':station_raw['주소'],\n",
    "                        '가격':station_raw['휘발유'],\n",
    "                        '셀프':station_raw['셀프여부'],\n",
    "                        '상표':station_raw['상표']})\n",
    "stations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 데이터 가공하기 \n",
    "\n",
    "이제는 주소에서 구만 뽑아서 저장해줘야겠죠. 주소에서 구만 가져올 거에요. split을 사용해 쉽게 가져오면 됩니다. 새로운 컬럼 구에다 담아주었어요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Oil_store</th>\n",
       "      <th>주소</th>\n",
       "      <th>가격</th>\n",
       "      <th>셀프</th>\n",
       "      <th>상표</th>\n",
       "      <th>구</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>한이에너지(주)KLP제1주유소</td>\n",
       "      <td>서울 도봉구 도봉로 596 (창동)</td>\n",
       "      <td>1277</td>\n",
       "      <td>N</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>도봉구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>도봉제일주유소(주)송만에너지</td>\n",
       "      <td>서울 도봉구 도봉로 783 (도봉동)</td>\n",
       "      <td>1277</td>\n",
       "      <td>N</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>도봉구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>정다운셀프주유소</td>\n",
       "      <td>서울특별시 도봉구 도봉로 635 (쌍문동)</td>\n",
       "      <td>1279</td>\n",
       "      <td>Y</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>도봉구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>삼미북부셀프주유소</td>\n",
       "      <td>서울특별시 도봉구  도봉로 437 (쌍문동)</td>\n",
       "      <td>1279</td>\n",
       "      <td>Y</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>도봉구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>SK네트웍스(주)도봉행복주유소</td>\n",
       "      <td>서울 도봉구  도봉로 941 (도봉동)</td>\n",
       "      <td>1279</td>\n",
       "      <td>Y</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>도봉구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Oil_store                        주소    가격 셀프      상표    구\n",
       "0  한이에너지(주)KLP제1주유소       서울 도봉구 도봉로 596 (창동)  1277  N  현대오일뱅크  도봉구\n",
       "1   도봉제일주유소(주)송만에너지      서울 도봉구 도봉로 783 (도봉동)  1277  N  현대오일뱅크  도봉구\n",
       "2          정다운셀프주유소   서울특별시 도봉구 도봉로 635 (쌍문동)  1279  Y   SK에너지  도봉구\n",
       "3         삼미북부셀프주유소  서울특별시 도봉구  도봉로 437 (쌍문동)  1279  Y   SK에너지  도봉구\n",
       "4  SK네트웍스(주)도봉행복주유소     서울 도봉구  도봉로 941 (도봉동)  1279  Y   SK에너지  도봉구"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations['구'] = [eachAddress.split()[1] for eachAddress in stations['주소']]\n",
    "stations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "25개의 구가 모두 잘 추출되었는지 확인하기 위해서는 아래와 같이 unique()를 통해 확인할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['도봉구', '마포구', '광진구', '중랑구', '관악구', '서대문구', '노원구', '강서구', '양천구',\n",
       "       '종로구', '서초구', '성동구', '중구', '영등포구', '강북구', '용산구', '강동구', '성북구',\n",
       "       '송파구', '은평구', '동대문구', '강남구', '금천구', '구로구', '동작구'], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations['구'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이상하게 추출된 구도 없고, 대충 다 담긴 것 같은데, 총 25개인지 확인해보는게 좋을 것 같네요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stations['구'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그 다음엔 가격들을 전반적으로 살펴보도록 할게요. false 밖에 없다는 말은 결측값이 없다는 말인데, 결측값이 없다고 다 된걸까요? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    501\n",
       "Name: 가격, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations['가격'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1277, 1279, 1285, 1289, 1299, 1325, 1335, 1337, 1365, 1369, 1379,\n",
       "       1383, 1498, '1447', '1457', '1498', '1567', '1569', '1619', '1639',\n",
       "       '1709', '1749', '1829', '-', 1306, 1307, 1327, 1345, 1357, 1359,\n",
       "       1378, 1398, 1399, 1430, 1459, 1560, 1267, 1297, 1348, 1358, 1372,\n",
       "       1375, 1374, 1417, 1418, 1439, 1458, 1469, 1478, 1489, 1499, 1550,\n",
       "       1329, 1344, 1349, 1680, 1917, 1354, 1392, 1397, 1480, 1495, 1525,\n",
       "       1598, 1258, 1286, 1287, 1290, 1295, 1314, 1346, 1347, 1387, 1394,\n",
       "       1415, 1419, 1424, 1425, 1434, 1435, 1448, 1462, 1488, 1535, 1789,\n",
       "       1268, 1284, 1294, 1315, 1316, 1318, 1336, 1377, 1396, 1483, 1579,\n",
       "       1787, 1420, 1490, 1898, 1988, 1998, 1999, 1319, 1407, 1409, 1428,\n",
       "       1437, 1438, 1445, 1446, 1447, 1467, 1497, 1519, 1589, 1879, 1887,\n",
       "       1978, 1308, 1326, 1368, 1388, 1429, 1455, 1648, 1798, 1968, 1688,\n",
       "       1758, 1859, 2035, 2049, 2059, 2079, 2088, 2099, 1339, 1588, 1799,\n",
       "       1840, 1875, 1890, 1929, 1930, 1936, 1257, 1309, 1558, 1668, 1669,\n",
       "       1757, 1884, 1948, 1985, 1990, 2018, 1380, 1463, 1468, 1487, 1528,\n",
       "       1548, 1237, 1262, 1266, 1272, 1281, 1298, 1317, 1385, 1411, 1412,\n",
       "       1413, 1443, 1449, 1619, '1279', '1285', '1294', '1297', '1299',\n",
       "       '1305', '1339', '1348', '1349', '1368', '1375', '1443', 1275, 1595,\n",
       "       '1409', '1419', '1428', '1438', '1439', '1455', '1458', '1469',\n",
       "       '1485', '1495', '1595', '1648', '1663', '1676', '1698', '1699',\n",
       "       '1729', '1777', '1799', '1838', '1844', '1875', '1878', '1880',\n",
       "       '1883', '1895', '2082', 1355, 1338, 1565], dtype=object)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations['가격'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "중간에 '-'가 보이시죠? '-'로 가격정보가 없는 것을 저렇게 표시해놨네요. 어떤 행이 가격 정보가 없는 것인지 불린 인덱싱을 사용해서 알아볼게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Oil_store</th>\n",
       "      <th>주소</th>\n",
       "      <th>가격</th>\n",
       "      <th>셀프</th>\n",
       "      <th>상표</th>\n",
       "      <th>구</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>서강주유소</td>\n",
       "      <td>서울 마포구 독막로 134 (창전동)</td>\n",
       "      <td>-</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>마포구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>삼융주유소</td>\n",
       "      <td>서울 은평구 수색로 299 (수색동)</td>\n",
       "      <td>-</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>은평구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>삼성주유소</td>\n",
       "      <td>서울 강남구 삼성로 521 (삼성동)</td>\n",
       "      <td>-</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>강남구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>동우주유소</td>\n",
       "      <td>서울특별시 강남구  봉은사로 311 (논현동)</td>\n",
       "      <td>-</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>강남구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Oil_store                         주소 가격 셀프     상표    구\n",
       "12     서강주유소       서울 마포구 독막로 134 (창전동)  -  N  SK에너지  마포구\n",
       "16     삼융주유소       서울 은평구 수색로 299 (수색동)  -  N  SK에너지  은평구\n",
       "40     삼성주유소       서울 강남구 삼성로 521 (삼성동)  -  N  SK에너지  강남구\n",
       "41     동우주유소  서울특별시 강남구  봉은사로 311 (논현동)  -  N  SK에너지  강남구"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations[stations['가격'] == '-']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "총 4개의 주유소에서 가격이 없군요. 이 row를 없애도록 하겠습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Oil_store</th>\n",
       "      <th>주소</th>\n",
       "      <th>가격</th>\n",
       "      <th>셀프</th>\n",
       "      <th>상표</th>\n",
       "      <th>구</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Oil_store, 주소, 가격, 셀프, 상표, 구]\n",
       "Index: []"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations = stations[stations['가격'] != '-']\n",
    "stations[stations['가격'] == '-']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "잘 지워졌네요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 497 entries, 0 to 9\n",
      "Data columns (total 6 columns):\n",
      "Oil_store    497 non-null object\n",
      "주소           497 non-null object\n",
      "가격           497 non-null object\n",
      "셀프           497 non-null object\n",
      "상표           497 non-null object\n",
      "구            497 non-null object\n",
      "dtypes: object(6)\n",
      "memory usage: 27.2+ KB\n"
     ]
    }
   ],
   "source": [
    "stations.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "가격이 아직도 숫자가 아닌 object니 수정해보도록 하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 497 entries, 0 to 9\n",
      "Data columns (total 6 columns):\n",
      "Oil_store    497 non-null object\n",
      "주소           497 non-null object\n",
      "가격           497 non-null float64\n",
      "셀프           497 non-null object\n",
      "상표           497 non-null object\n",
      "구            497 non-null object\n",
      "dtypes: float64(1), object(5)\n",
      "memory usage: 27.2+ KB\n"
     ]
    }
   ],
   "source": [
    "stations['가격'] = stations['가격'].astype('float')\n",
    "stations.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "잘 변경 됐네요! 이 정도로 데이터를 가공하면 된 것 같습니다.  \n",
    "\n",
    "### 6.4 boxplot으로 셀프 주유소 가격 확인해보기 \n",
    "\n",
    "저어번에 boxplot에 대해 배워봤을 겁니다. 그만큼 boxplot은 데이터의 전반적인 수치들을 확인하기에 편리한 시각화에요. 셀프 주유소가 정말로 저렴한지 boxplot으로 시각화해보겠습니다. 우선 matplotlib의 한글 문제를 처리하고 필요한 모듈들을 import 할게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import platform\n",
    "\n",
    "path = \"c:/Windows/Fonts/malgun.ttf\"\n",
    "from matplotlib import font_manager, rc\n",
    "if platform.system() == 'Darwin':\n",
    "    rc('font', family='AppleGothic')\n",
    "elif platform.system() == 'Windows':\n",
    "    font_name = font_manager.FontProperties(fname=path).get_name()\n",
    "    rc('font', family=font_name)\n",
    "else:\n",
    "    print('Unknown system... sorry~~~~') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "셀프 유무에 따른 가격을 확인해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24c6d5d0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stations.boxplot(column='가격', by='셀프', figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "확실히 셀프 주유소가 싼 가격이란게 보이죠? 검은색 점들은 이상치라는 거고, 평균도 셀프주유소가 훨씬 낮고, 전반적으로 셀프 주유소가 더 싼 것을 알 수 있습니다. \n",
    "\n",
    "이제는 상표에 따라서 boxplot을 시각화해볼게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(x='상표', y='가격', hue='셀프', data=stations, palette='Set3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "어헛, sk에너지가 상당히 비싸네요. 여기서도 같은 상표라도 셀프 주유소들이 더 싸게 가격이 분포되어 있고요, 알뜰 주유소가 알뜰이라는 이름 답게 싸다는 것 또한 알 수 있네요.\n",
    "\n",
    "셀프 주유소인지 아닌지는 제외하고 상표로만 boxplot을 보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(x='상표', y='가격', data=stations, palette='Set3')\n",
    "sns.swarmplot(x='상표', y='가격', data=stations, color='.8')  # 1로 하면 환공포증 느낌...\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1로 해보세요... 진짜 징그러워요...(소름) 아무튼 알뜰주유소 이용을 추천합니다ㅎㅎ 대기업들은 돈도 많으면서 더 비싸네요😅\n",
    "\n",
    "결과적으로 '셀프 주유소는 더 저렴한가'는 맞다고 할 수 있네요! 요렇게 팩트체크를 완료해봤습니다. \n",
    "\n",
    "### 6.5 서울시 구별 주유 가격 확인하기 \n",
    "\n",
    "여기까지만 하면 아쉽잖아요. 그래서 구별로 가격의 정도를 시각화해보려고 합니다. 그래서 앞서 주소에서 구를 추출한 것이에요. 이전에 사용했던 json, folium, googlemaps를 사용해서 시각화해줄 겁니다. 필요한 모듈을 import 해줄게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import folium\n",
    "import googlemaps\n",
    "import warnings  # futurewarning 뜨지 않도록! \n",
    "warnings.simplefilter(action = \"ignore\", category = FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "가격별로 먼저 제일 비싼곳부터 나열해볼게요. 딱 10개를 가져와보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Oil_store</th>\n",
       "      <th>주소</th>\n",
       "      <th>가격</th>\n",
       "      <th>셀프</th>\n",
       "      <th>상표</th>\n",
       "      <th>구</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>서남주유소</td>\n",
       "      <td>서울 중구 통일로 30</td>\n",
       "      <td>2099.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>장충주유소</td>\n",
       "      <td>서울 중구 장충단로 202 (장충동1가)</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>뉴서울(강남)</td>\n",
       "      <td>서울 강남구 언주로 716 (논현동)</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>강남구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>에스씨(주) 역전주유소</td>\n",
       "      <td>서울 중구 퇴계로 15</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>서계주유소</td>\n",
       "      <td>서울 용산구 청파로 367 (서계동)</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>용산구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>필동주유소</td>\n",
       "      <td>서울 중구  퇴계로 196 (필동2가)</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>SK에너지(주) 퇴계로주유소</td>\n",
       "      <td>서울 중구 퇴계로 228 (필동2가)</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>지에스칼텍스(주)초동주유소</td>\n",
       "      <td>서울 중구 마른내로 31</td>\n",
       "      <td>2035.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>수림석유 제3한강주유소</td>\n",
       "      <td>서울 용산구 한남대로 45 (한남동)</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>용산구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>SK네트웍스(주)재동주유소</td>\n",
       "      <td>서울 종로구 율곡로 58 (경운동)</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>종로구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Oil_store                      주소      가격 셀프     상표    구\n",
       "11            서남주유소            서울 중구 통일로 30  2099.0  N  SK에너지   중구\n",
       "10            장충주유소  서울 중구 장충단로 202 (장충동1가)  2088.0  N  SK에너지   중구\n",
       "39          뉴서울(강남)    서울 강남구 언주로 716 (논현동)  2082.0  N  SK에너지  강남구\n",
       "9      에스씨(주) 역전주유소            서울 중구 퇴계로 15  2079.0  N  GS칼텍스   중구\n",
       "13            서계주유소    서울 용산구 청파로 367 (서계동)  2059.0  N  GS칼텍스  용산구\n",
       "8             필동주유소   서울 중구  퇴계로 196 (필동2가)  2059.0  N  GS칼텍스   중구\n",
       "7   SK에너지(주) 퇴계로주유소    서울 중구 퇴계로 228 (필동2가)  2049.0  N  SK에너지   중구\n",
       "6    지에스칼텍스(주)초동주유소           서울 중구 마른내로 31  2035.0  N  GS칼텍스   중구\n",
       "12     수림석유 제3한강주유소    서울 용산구 한남대로 45 (한남동)  2018.0  N  GS칼텍스  용산구\n",
       "8    SK네트웍스(주)재동주유소     서울 종로구 율곡로 58 (경운동)  1999.0  N  SK에너지  종로구"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations.sort_values(by='가격', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "강남이 비쌀 줄 알았는데, 중구, 용산구 쪽에 비싼 주유소들이 많군요. 중구에는 광화문, 시청 등이 있어서 일까요. 상당히 비싸네요. 2000원대가 웬말입니까!! \n",
    "\n",
    "다음에는 저의 집이 서대문에 있어서 싼 주소가 어딨는지 알아봤어요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>Oil_store</th>\n",
       "      <th>주소</th>\n",
       "      <th>가격</th>\n",
       "      <th>셀프</th>\n",
       "      <th>상표</th>\n",
       "      <th>구</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>(주)창원이엔지 홍제지점</td>\n",
       "      <td>서울 서대문구 통일로 455 (홍제동)</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>N</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>삼미상사(주)서부주유소</td>\n",
       "      <td>서울 서대문구 성산로 255 (연희동)</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>(주)가재울뉴타운주유소</td>\n",
       "      <td>서울 서대문구 모래내로 205</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>(주)마봉 구도일주유소 두꺼비</td>\n",
       "      <td>서울 서대문구 성산로 312 (연희동)</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>S-OIL</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>남정주유소</td>\n",
       "      <td>서울특별시 서대문구  모래내로 243 (남가좌동)</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>(주)마봉 신우주유소</td>\n",
       "      <td>서울 서대문구 가좌로 106 (홍은동)</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>S-OIL</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>코리아상사연세주유소</td>\n",
       "      <td>서울 서대문구 연희로 184</td>\n",
       "      <td>1369.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>지에스칼텍스(주)홍제동주유소</td>\n",
       "      <td>서울 서대문구 통일로 372</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>삼보셀프주유소</td>\n",
       "      <td>서울 서대문구 연희로 78 (연희동)</td>\n",
       "      <td>1378.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>광호주유소</td>\n",
       "      <td>서울 서대문구 연희로 266 (홍은동)</td>\n",
       "      <td>1378.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>연희주유소</td>\n",
       "      <td>서울 서대문구 연희로 129 (연희동 89-7번지)</td>\n",
       "      <td>1378.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>중앙에너비스 동교지점</td>\n",
       "      <td>서울 서대문구 연희로 12 (창천동)</td>\n",
       "      <td>1378.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>KH에너지㈜직영 독립문주유소</td>\n",
       "      <td>서울 서대문구 통일로 384 (홍제동)</td>\n",
       "      <td>1398.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>문화촌주유소</td>\n",
       "      <td>서울 서대문구 세검정로 87 (홍제동)</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>N</td>\n",
       "      <td>S-OIL</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>행촌제2주유소</td>\n",
       "      <td>서울 서대문구 성산로 490 (대신동)</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>서대문구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Oil_store                            주소      가격 셀프      상표     구\n",
       "0      (주)창원이엔지 홍제지점         서울 서대문구 통일로 455 (홍제동)  1329.0  N  현대오일뱅크  서대문구\n",
       "1       삼미상사(주)서부주유소         서울 서대문구 성산로 255 (연희동)  1344.0  Y   SK에너지  서대문구\n",
       "2       (주)가재울뉴타운주유소              서울 서대문구 모래내로 205  1344.0  Y  현대오일뱅크  서대문구\n",
       "3   (주)마봉 구도일주유소 두꺼비         서울 서대문구 성산로 312 (연희동)  1344.0  Y   S-OIL  서대문구\n",
       "4              남정주유소   서울특별시 서대문구  모래내로 243 (남가좌동)  1349.0  N   GS칼텍스  서대문구\n",
       "5        (주)마봉 신우주유소         서울 서대문구 가좌로 106 (홍은동)  1349.0  Y   S-OIL  서대문구\n",
       "6         코리아상사연세주유소               서울 서대문구 연희로 184  1369.0  Y   GS칼텍스  서대문구\n",
       "7    지에스칼텍스(주)홍제동주유소               서울 서대문구 통일로 372  1375.0  Y   GS칼텍스  서대문구\n",
       "8            삼보셀프주유소          서울 서대문구 연희로 78 (연희동)  1378.0  Y   SK에너지  서대문구\n",
       "9              광호주유소         서울 서대문구 연희로 266 (홍은동)  1378.0  N   SK에너지  서대문구\n",
       "10             연희주유소  서울 서대문구 연희로 129 (연희동 89-7번지)  1378.0  N   SK에너지  서대문구\n",
       "11       중앙에너비스 동교지점          서울 서대문구 연희로 12 (창천동)  1378.0  Y   SK에너지  서대문구\n",
       "12   KH에너지㈜직영 독립문주유소         서울 서대문구 통일로 384 (홍제동)  1398.0  N   SK에너지  서대문구\n",
       "13            문화촌주유소         서울 서대문구 세검정로 87 (홍제동)  1680.0  N   S-OIL  서대문구\n",
       "14           행촌제2주유소         서울 서대문구 성산로 490 (대신동)  1917.0  N   SK에너지  서대문구"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations[stations['구'] == '서대문구'].sort_values(by='가격')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "오홍, 1번째 index가 우리집과 제일 가까운 곳이네요^6^ \n",
    "\n",
    "서울시에서 가장 낮은 주유소들을 어디에 있는지도 볼게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Oil_store</th>\n",
       "      <th>주소</th>\n",
       "      <th>가격</th>\n",
       "      <th>셀프</th>\n",
       "      <th>상표</th>\n",
       "      <th>구</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>강산제2주유소</td>\n",
       "      <td>서울 성북구 보국문로 52</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>현대오일뱅크</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>이케이에너지㈜ 강산주유소</td>\n",
       "      <td>서울 성북구 보문로 27 (보문동7가)</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>(주)삼표에너지 정릉주유소</td>\n",
       "      <td>서울 성북구 보국문로 35 (정릉동)</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>성북구</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>원천주유소</td>\n",
       "      <td>서울 성북구 돌곶이로 142 (장위동)</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>N</td>\n",
       "      <td>알뜰주유소</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>북서울고속주유소</td>\n",
       "      <td>서울 강북구 삼양로 410 (수유동)</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>S-OIL</td>\n",
       "      <td>강북구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>(주)석산에너지</td>\n",
       "      <td>서울 강북구 삼양로 316 (수유동)</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>현대오일뱅크</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>화곡역주유소</td>\n",
       "      <td>서울특별시 강서구  강서로 154 (화곡동)</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>알뜰주유소</td>\n",
       "      <td>강서구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>뉴신정주유소</td>\n",
       "      <td>서울 강서구 곰달래로 207(화곡동)</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>N</td>\n",
       "      <td>알뜰주유소</td>\n",
       "      <td>강서구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>형산석유(주)원주유소</td>\n",
       "      <td>서울 양천구 남부순환로 408</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>N</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>양천구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>현대주유소</td>\n",
       "      <td>서울 양천구 남부순환로 372 (신월동)</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>S-OIL</td>\n",
       "      <td>양천구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Oil_store                        주소      가격 셀프      상표    구\n",
       "2         강산제2주유소            서울 성북구 보국문로 52  1237.0  Y  현대오일뱅크  성북구\n",
       "0   이케이에너지㈜ 강산주유소     서울 성북구 보문로 27 (보문동7가)  1237.0  Y   GS칼텍스  성북구\n",
       "3  (주)삼표에너지 정릉주유소      서울 성북구 보국문로 35 (정릉동)  1237.0  Y   GS칼텍스  성북구\n",
       "1           원천주유소     서울 성북구 돌곶이로 142 (장위동)  1237.0  N   알뜰주유소  성북구\n",
       "1        북서울고속주유소      서울 강북구 삼양로 410 (수유동)  1257.0  Y   S-OIL  강북구\n",
       "0        (주)석산에너지      서울 강북구 삼양로 316 (수유동)  1257.0  Y  현대오일뱅크  강북구\n",
       "1          화곡역주유소  서울특별시 강서구  강서로 154 (화곡동)  1258.0  Y   알뜰주유소  강서구\n",
       "0          뉴신정주유소      서울 강서구 곰달래로 207(화곡동)  1258.0  N   알뜰주유소  강서구\n",
       "0     형산석유(주)원주유소          서울 양천구 남부순환로 408  1258.0  N  현대오일뱅크  양천구\n",
       "1           현대주유소    서울 양천구 남부순환로 372 (신월동)  1258.0  Y   S-OIL  양천구"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations.sort_values(by='가격', ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "성북구, 강북구, 강서구 이런 구들에 많군요. \n",
    "\n",
    "하지만 단순히 10개의 데이터를 가지고 이런 구들이 비싸다 싸다를 말하긴 힘들죠. 그래서 평균을 내서 대체적인 가격을 비교해보겠습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>가격</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>구</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>강남구</td>\n",
       "      <td>1615.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>강동구</td>\n",
       "      <td>1461.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>강북구</td>\n",
       "      <td>1297.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>강서구</td>\n",
       "      <td>1381.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>관악구</td>\n",
       "      <td>1456.375000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              가격\n",
       "구               \n",
       "강남구  1615.775000\n",
       "강동구  1461.312500\n",
       "강북구  1297.461538\n",
       "강서구  1381.647059\n",
       "관악구  1456.375000"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "gu_data = pd.pivot_table(stations, index=['구'], values=[\"가격\"], aggfunc=np.mean)\n",
    "gu_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이것을 지도에 시각화해서 나타내보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1a2634f850>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_path = 'data/skorea_municipalities_geo_simple.json'\n",
    "geo_str = json.load(open(geo_path, encoding='utf-8'))\n",
    "\n",
    "map = folium.Map(location=[37.5502, 126.982], zoom_start=10.5, \n",
    "                 tiles='Stamen Toner')\n",
    "\n",
    "map.choropleth(geo_data = geo_str,\n",
    "               data = gu_data,\n",
    "               columns=[gu_data.index, '가격'],\n",
    "               fill_color='PuRd', #PuRd, YlGnBu\n",
    "               key_on='feature.id')\n",
    "map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "오호라 이렇게 하니, 확실히 중구와 용산구가 비싸네요. 거기다 종로구도 무척 비싸고, 강남구도 확실히 비싸네요. \n",
    "\n",
    "### 6.6 서울시 주유 가격 상하위 10개 주유소 지도에 표시하기\n",
    "\n",
    "이번에는 서울시에 있는 주유가격 상하위 10개 주유소를 지도에 표시해보도록 하겠습니다. 구별로 따지는게 아니라, 개별 주유소별로 따져보는 거에요. 상하위 10개 주유소만 따로 변수에 담아보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Oil_store</th>\n",
       "      <th>주소</th>\n",
       "      <th>가격</th>\n",
       "      <th>셀프</th>\n",
       "      <th>상표</th>\n",
       "      <th>구</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>서남주유소</td>\n",
       "      <td>서울 중구 통일로 30</td>\n",
       "      <td>2099.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>장충주유소</td>\n",
       "      <td>서울 중구 장충단로 202 (장충동1가)</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>뉴서울(강남)</td>\n",
       "      <td>서울 강남구 언주로 716 (논현동)</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>강남구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>에스씨(주) 역전주유소</td>\n",
       "      <td>서울 중구 퇴계로 15</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>서계주유소</td>\n",
       "      <td>서울 용산구 청파로 367 (서계동)</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>용산구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>필동주유소</td>\n",
       "      <td>서울 중구  퇴계로 196 (필동2가)</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>SK에너지(주) 퇴계로주유소</td>\n",
       "      <td>서울 중구 퇴계로 228 (필동2가)</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>지에스칼텍스(주)초동주유소</td>\n",
       "      <td>서울 중구 마른내로 31</td>\n",
       "      <td>2035.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>중구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>수림석유 제3한강주유소</td>\n",
       "      <td>서울 용산구 한남대로 45 (한남동)</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>용산구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>SK네트웍스(주)재동주유소</td>\n",
       "      <td>서울 종로구 율곡로 58 (경운동)</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>종로구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Oil_store                      주소      가격 셀프     상표    구\n",
       "11            서남주유소            서울 중구 통일로 30  2099.0  N  SK에너지   중구\n",
       "10            장충주유소  서울 중구 장충단로 202 (장충동1가)  2088.0  N  SK에너지   중구\n",
       "39          뉴서울(강남)    서울 강남구 언주로 716 (논현동)  2082.0  N  SK에너지  강남구\n",
       "9      에스씨(주) 역전주유소            서울 중구 퇴계로 15  2079.0  N  GS칼텍스   중구\n",
       "13            서계주유소    서울 용산구 청파로 367 (서계동)  2059.0  N  GS칼텍스  용산구\n",
       "8             필동주유소   서울 중구  퇴계로 196 (필동2가)  2059.0  N  GS칼텍스   중구\n",
       "7   SK에너지(주) 퇴계로주유소    서울 중구 퇴계로 228 (필동2가)  2049.0  N  SK에너지   중구\n",
       "6    지에스칼텍스(주)초동주유소           서울 중구 마른내로 31  2035.0  N  GS칼텍스   중구\n",
       "12     수림석유 제3한강주유소    서울 용산구 한남대로 45 (한남동)  2018.0  N  GS칼텍스  용산구\n",
       "8    SK네트웍스(주)재동주유소     서울 종로구 율곡로 58 (경운동)  1999.0  N  SK에너지  종로구"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil_price_top10 = stations.sort_values(by='가격', ascending=False).head(10)\n",
    "oil_price_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2</td>\n",
       "      <td>강산제2주유소</td>\n",
       "      <td>서울 성북구 보국문로 52</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>현대오일뱅크</td>\n",
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       "      <td>0</td>\n",
       "      <td>이케이에너지㈜ 강산주유소</td>\n",
       "      <td>서울 성북구 보문로 27 (보문동7가)</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>성북구</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>(주)삼표에너지 정릉주유소</td>\n",
       "      <td>서울 성북구 보국문로 35 (정릉동)</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>성북구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>원천주유소</td>\n",
       "      <td>서울 성북구 돌곶이로 142 (장위동)</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>N</td>\n",
       "      <td>알뜰주유소</td>\n",
       "      <td>성북구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>북서울고속주유소</td>\n",
       "      <td>서울 강북구 삼양로 410 (수유동)</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>S-OIL</td>\n",
       "      <td>강북구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>(주)석산에너지</td>\n",
       "      <td>서울 강북구 삼양로 316 (수유동)</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>강북구</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>화곡역주유소</td>\n",
       "      <td>서울특별시 강서구  강서로 154 (화곡동)</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>알뜰주유소</td>\n",
       "      <td>강서구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>뉴신정주유소</td>\n",
       "      <td>서울 강서구 곰달래로 207(화곡동)</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>N</td>\n",
       "      <td>알뜰주유소</td>\n",
       "      <td>강서구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>형산석유(주)원주유소</td>\n",
       "      <td>서울 양천구 남부순환로 408</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>N</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>양천구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>현대주유소</td>\n",
       "      <td>서울 양천구 남부순환로 372 (신월동)</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>S-OIL</td>\n",
       "      <td>양천구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Oil_store                        주소      가격 셀프      상표    구\n",
       "2         강산제2주유소            서울 성북구 보국문로 52  1237.0  Y  현대오일뱅크  성북구\n",
       "0   이케이에너지㈜ 강산주유소     서울 성북구 보문로 27 (보문동7가)  1237.0  Y   GS칼텍스  성북구\n",
       "3  (주)삼표에너지 정릉주유소      서울 성북구 보국문로 35 (정릉동)  1237.0  Y   GS칼텍스  성북구\n",
       "1           원천주유소     서울 성북구 돌곶이로 142 (장위동)  1237.0  N   알뜰주유소  성북구\n",
       "1        북서울고속주유소      서울 강북구 삼양로 410 (수유동)  1257.0  Y   S-OIL  강북구\n",
       "0        (주)석산에너지      서울 강북구 삼양로 316 (수유동)  1257.0  Y  현대오일뱅크  강북구\n",
       "1          화곡역주유소  서울특별시 강서구  강서로 154 (화곡동)  1258.0  Y   알뜰주유소  강서구\n",
       "0          뉴신정주유소      서울 강서구 곰달래로 207(화곡동)  1258.0  N   알뜰주유소  강서구\n",
       "0     형산석유(주)원주유소          서울 양천구 남부순환로 408  1258.0  N  현대오일뱅크  양천구\n",
       "1           현대주유소    서울 양천구 남부순환로 372 (신월동)  1258.0  Y   S-OIL  양천구"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil_price_bottom10 = stations.sort_values(by='가격', ascending=True).head(10)\n",
    "oil_price_bottom10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "지도에 시각화 하려면 위도와 경도가 필요하니, 구글 맵키를 준비해주시구요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmaps_key = \"**************\"\n",
    "gmaps = googlemaps.Client(key=gmaps_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아래 코드를 입력하면 lat과 lng 정보를 모두 담아 oil_price_top10과 oil_price_bottom10에다 새로운 칼럼으로 담아줍니다. (코드는 교재에 실린 코드와는 다릅니다.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a705550d5b2467fbe165445672e33e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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",
       "        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>Oil_store</th>\n",
       "      <th>주소</th>\n",
       "      <th>가격</th>\n",
       "      <th>셀프</th>\n",
       "      <th>상표</th>\n",
       "      <th>구</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>서남주유소</td>\n",
       "      <td>서울 중구 통일로 30</td>\n",
       "      <td>2099.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>중구</td>\n",
       "      <td>37.558348</td>\n",
       "      <td>126.972090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>장충주유소</td>\n",
       "      <td>서울 중구 장충단로 202 (장충동1가)</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>중구</td>\n",
       "      <td>37.562719</td>\n",
       "      <td>127.007010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>뉴서울(강남)</td>\n",
       "      <td>서울 강남구 언주로 716 (논현동)</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>강남구</td>\n",
       "      <td>37.517636</td>\n",
       "      <td>127.035756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>에스씨(주) 역전주유소</td>\n",
       "      <td>서울 중구 퇴계로 15</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>중구</td>\n",
       "      <td>37.557001</td>\n",
       "      <td>126.974495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>서계주유소</td>\n",
       "      <td>서울 용산구 청파로 367 (서계동)</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>용산구</td>\n",
       "      <td>37.552290</td>\n",
       "      <td>126.968935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>필동주유소</td>\n",
       "      <td>서울 중구  퇴계로 196 (필동2가)</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>중구</td>\n",
       "      <td>37.560850</td>\n",
       "      <td>126.993653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>SK에너지(주) 퇴계로주유소</td>\n",
       "      <td>서울 중구 퇴계로 228 (필동2가)</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>중구</td>\n",
       "      <td>37.561648</td>\n",
       "      <td>126.997142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>지에스칼텍스(주)초동주유소</td>\n",
       "      <td>서울 중구 마른내로 31</td>\n",
       "      <td>2035.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>중구</td>\n",
       "      <td>37.564944</td>\n",
       "      <td>126.991136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>수림석유 제3한강주유소</td>\n",
       "      <td>서울 용산구 한남대로 45 (한남동)</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>용산구</td>\n",
       "      <td>37.532731</td>\n",
       "      <td>127.007481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>SK네트웍스(주)재동주유소</td>\n",
       "      <td>서울 종로구 율곡로 58 (경운동)</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>종로구</td>\n",
       "      <td>37.560850</td>\n",
       "      <td>126.993653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Oil_store                      주소      가격 셀프     상표    구        lat  \\\n",
       "11            서남주유소            서울 중구 통일로 30  2099.0  N  SK에너지   중구  37.558348   \n",
       "10            장충주유소  서울 중구 장충단로 202 (장충동1가)  2088.0  N  SK에너지   중구  37.562719   \n",
       "39          뉴서울(강남)    서울 강남구 언주로 716 (논현동)  2082.0  N  SK에너지  강남구  37.517636   \n",
       "9      에스씨(주) 역전주유소            서울 중구 퇴계로 15  2079.0  N  GS칼텍스   중구  37.557001   \n",
       "13            서계주유소    서울 용산구 청파로 367 (서계동)  2059.0  N  GS칼텍스  용산구  37.552290   \n",
       "8             필동주유소   서울 중구  퇴계로 196 (필동2가)  2059.0  N  GS칼텍스   중구  37.560850   \n",
       "7   SK에너지(주) 퇴계로주유소    서울 중구 퇴계로 228 (필동2가)  2049.0  N  SK에너지   중구  37.561648   \n",
       "6    지에스칼텍스(주)초동주유소           서울 중구 마른내로 31  2035.0  N  GS칼텍스   중구  37.564944   \n",
       "12     수림석유 제3한강주유소    서울 용산구 한남대로 45 (한남동)  2018.0  N  GS칼텍스  용산구  37.532731   \n",
       "8    SK네트웍스(주)재동주유소     서울 종로구 율곡로 58 (경운동)  1999.0  N  SK에너지  종로구  37.560850   \n",
       "\n",
       "           lng  \n",
       "11  126.972090  \n",
       "10  127.007010  \n",
       "39  127.035756  \n",
       "9   126.974495  \n",
       "13  126.968935  \n",
       "8   126.993653  \n",
       "7   126.997142  \n",
       "6   126.991136  \n",
       "12  127.007481  \n",
       "8   126.993653  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tqdm import tqdm_notebook\n",
    "\n",
    "lat = []\n",
    "lng = []\n",
    "\n",
    "for n in tqdm_notebook(oil_price_top10.index):\n",
    "    # 주소를 '(' 기준으로 나눠 첫번째 주소만 가져와라 \n",
    "    tmp_add = str(oil_price_top10['주소'][n]).split('(')[0]\n",
    "    # tmp_add를 기준으로 주소를 입력해라 \n",
    "    tmp_map = gmaps.geocode(tmp_add)\n",
    "\n",
    "    # tmp_map의 첫번째 값에서 'geometry'를 구하고, 거기에서 다시 'location'을 구해라\n",
    "    tmp_loc = tmp_map[0].get('geometry').get('location')\n",
    "    lat.append(tmp_loc['lat'])\n",
    "    lng.append(tmp_loc['lng'])\n",
    "\n",
    "# 각각 새로운 컬럼 형성 \n",
    "oil_price_top10['lat'] = lat\n",
    "oil_price_top10['lng'] = lng\n",
    "oil_price_top10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "엇, 그런데 인덱스가 이상하네요. 8이 2번 들어가 있어요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b443b741a61c46a2b590fd3061ad0a68",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Oil_store</th>\n",
       "      <th>주소</th>\n",
       "      <th>가격</th>\n",
       "      <th>셀프</th>\n",
       "      <th>상표</th>\n",
       "      <th>구</th>\n",
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       "      <th>lng</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>강산제2주유소</td>\n",
       "      <td>서울 성북구 보국문로 52</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>성북구</td>\n",
       "      <td>37.609163</td>\n",
       "      <td>127.010074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>이케이에너지㈜ 강산주유소</td>\n",
       "      <td>서울 성북구 보문로 27 (보문동7가)</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>성북구</td>\n",
       "      <td>37.577912</td>\n",
       "      <td>127.022774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>(주)삼표에너지 정릉주유소</td>\n",
       "      <td>서울 성북구 보국문로 35 (정릉동)</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>성북구</td>\n",
       "      <td>37.607660</td>\n",
       "      <td>127.009858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>원천주유소</td>\n",
       "      <td>서울 성북구 돌곶이로 142 (장위동)</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>N</td>\n",
       "      <td>알뜰주유소</td>\n",
       "      <td>성북구</td>\n",
       "      <td>37.614921</td>\n",
       "      <td>127.052752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>북서울고속주유소</td>\n",
       "      <td>서울 강북구 삼양로 410 (수유동)</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>S-OIL</td>\n",
       "      <td>강북구</td>\n",
       "      <td>37.614921</td>\n",
       "      <td>127.052752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>(주)석산에너지</td>\n",
       "      <td>서울 강북구 삼양로 316 (수유동)</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>강북구</td>\n",
       "      <td>37.577912</td>\n",
       "      <td>127.022774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>화곡역주유소</td>\n",
       "      <td>서울특별시 강서구  강서로 154 (화곡동)</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>알뜰주유소</td>\n",
       "      <td>강서구</td>\n",
       "      <td>37.614921</td>\n",
       "      <td>127.052752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>뉴신정주유소</td>\n",
       "      <td>서울 강서구 곰달래로 207(화곡동)</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>N</td>\n",
       "      <td>알뜰주유소</td>\n",
       "      <td>강서구</td>\n",
       "      <td>37.577912</td>\n",
       "      <td>127.022774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>형산석유(주)원주유소</td>\n",
       "      <td>서울 양천구 남부순환로 408</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>N</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>양천구</td>\n",
       "      <td>37.577912</td>\n",
       "      <td>127.022774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>현대주유소</td>\n",
       "      <td>서울 양천구 남부순환로 372 (신월동)</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>S-OIL</td>\n",
       "      <td>양천구</td>\n",
       "      <td>37.614921</td>\n",
       "      <td>127.052752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Oil_store                        주소      가격 셀프      상표    구  \\\n",
       "2         강산제2주유소            서울 성북구 보국문로 52  1237.0  Y  현대오일뱅크  성북구   \n",
       "0   이케이에너지㈜ 강산주유소     서울 성북구 보문로 27 (보문동7가)  1237.0  Y   GS칼텍스  성북구   \n",
       "3  (주)삼표에너지 정릉주유소      서울 성북구 보국문로 35 (정릉동)  1237.0  Y   GS칼텍스  성북구   \n",
       "1           원천주유소     서울 성북구 돌곶이로 142 (장위동)  1237.0  N   알뜰주유소  성북구   \n",
       "1        북서울고속주유소      서울 강북구 삼양로 410 (수유동)  1257.0  Y   S-OIL  강북구   \n",
       "0        (주)석산에너지      서울 강북구 삼양로 316 (수유동)  1257.0  Y  현대오일뱅크  강북구   \n",
       "1          화곡역주유소  서울특별시 강서구  강서로 154 (화곡동)  1258.0  Y   알뜰주유소  강서구   \n",
       "0          뉴신정주유소      서울 강서구 곰달래로 207(화곡동)  1258.0  N   알뜰주유소  강서구   \n",
       "0     형산석유(주)원주유소          서울 양천구 남부순환로 408  1258.0  N  현대오일뱅크  양천구   \n",
       "1           현대주유소    서울 양천구 남부순환로 372 (신월동)  1258.0  Y   S-OIL  양천구   \n",
       "\n",
       "         lat         lng  \n",
       "2  37.609163  127.010074  \n",
       "0  37.577912  127.022774  \n",
       "3  37.607660  127.009858  \n",
       "1  37.614921  127.052752  \n",
       "1  37.614921  127.052752  \n",
       "0  37.577912  127.022774  \n",
       "1  37.614921  127.052752  \n",
       "0  37.577912  127.022774  \n",
       "0  37.577912  127.022774  \n",
       "1  37.614921  127.052752  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat = []\n",
    "lng = []\n",
    "\n",
    "for n in tqdm_notebook(oil_price_bottom10.index):\n",
    "    # 주소를 '(' 기준으로 나눠 첫번째 주소만 가져와라 \n",
    "    tmp_add = str(oil_price_bottom10['주소'][n]).split('(')[0]\n",
    "    # tmp_add를 기준으로 주소를 입력해라 \n",
    "    tmp_map = gmaps.geocode(tmp_add)\n",
    "\n",
    "    # tmp_map의 첫번째 값에서 'geometry'를 구하고, 거기에서 다시 'location'을 구해라\n",
    "    tmp_loc = tmp_map[0].get('geometry').get('location')\n",
    "    lat.append(tmp_loc['lat'])\n",
    "    lng.append(tmp_loc['lng'])\n",
    "\n",
    "# 각각 새로운 컬럼 형성 \n",
    "oil_price_bottom10['lat'] = lat\n",
    "oil_price_bottom10['lng'] = lng\n",
    "oil_price_bottom10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "어, 그런데 하위 10개도 index가 이상하죠. 아마 병합하는 과정에서 인덱스를 재설정하지 않아서 이런 결과가 나타난 것 같아요. 이러면 이후가 문제가 생기게 돼요. 아래처럼 인덱스 별로 위도와 경도를 뽑아보면, 6번째 값부터 이상하게 추출이 되죠? 그러면 Folium에 넣었을 때 오류가 떠요! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[37.5583477, 126.9720901]\n",
      "[37.5627192, 127.0070101]\n",
      "[37.51763589999999, 127.0357559]\n",
      "[37.5570008, 126.9744945]\n",
      "[37.5522895, 126.9689354]\n",
      "[8    37.56085\n",
      "8    37.56085\n",
      "Name: lat, dtype: float64, 8    126.993653\n",
      "8    126.993653\n",
      "Name: lng, dtype: float64]\n",
      "[37.5616483, 126.9971419]\n",
      "[37.5649444, 126.9911359]\n",
      "[37.5327311, 127.0074814]\n",
      "[8    37.56085\n",
      "8    37.56085\n",
      "Name: lat, dtype: float64, 8    126.993653\n",
      "8    126.993653\n",
      "Name: lng, dtype: float64]\n"
     ]
    }
   ],
   "source": [
    "for n in oil_price_top10.index:\n",
    "    print([oil_price_top10['lat'][n], oil_price_top10['lng'][n]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그래서 이 문제를 해결하기 위해 index를 다시 설정해줍니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>Oil_store</th>\n",
       "      <th>주소</th>\n",
       "      <th>가격</th>\n",
       "      <th>셀프</th>\n",
       "      <th>상표</th>\n",
       "      <th>구</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>서남주유소</td>\n",
       "      <td>서울 중구 통일로 30</td>\n",
       "      <td>2099.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>중구</td>\n",
       "      <td>37.558348</td>\n",
       "      <td>126.972090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>장충주유소</td>\n",
       "      <td>서울 중구 장충단로 202 (장충동1가)</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>중구</td>\n",
       "      <td>37.562719</td>\n",
       "      <td>127.007010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>뉴서울(강남)</td>\n",
       "      <td>서울 강남구 언주로 716 (논현동)</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>강남구</td>\n",
       "      <td>37.517636</td>\n",
       "      <td>127.035756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>에스씨(주) 역전주유소</td>\n",
       "      <td>서울 중구 퇴계로 15</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>중구</td>\n",
       "      <td>37.557001</td>\n",
       "      <td>126.974495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>서계주유소</td>\n",
       "      <td>서울 용산구 청파로 367 (서계동)</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>용산구</td>\n",
       "      <td>37.552290</td>\n",
       "      <td>126.968935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>필동주유소</td>\n",
       "      <td>서울 중구  퇴계로 196 (필동2가)</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>중구</td>\n",
       "      <td>37.560850</td>\n",
       "      <td>126.993653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>SK에너지(주) 퇴계로주유소</td>\n",
       "      <td>서울 중구 퇴계로 228 (필동2가)</td>\n",
       "      <td>2049.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>중구</td>\n",
       "      <td>37.561648</td>\n",
       "      <td>126.997142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>지에스칼텍스(주)초동주유소</td>\n",
       "      <td>서울 중구 마른내로 31</td>\n",
       "      <td>2035.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>중구</td>\n",
       "      <td>37.564944</td>\n",
       "      <td>126.991136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>수림석유 제3한강주유소</td>\n",
       "      <td>서울 용산구 한남대로 45 (한남동)</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>N</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>용산구</td>\n",
       "      <td>37.532731</td>\n",
       "      <td>127.007481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>SK네트웍스(주)재동주유소</td>\n",
       "      <td>서울 종로구 율곡로 58 (경운동)</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>N</td>\n",
       "      <td>SK에너지</td>\n",
       "      <td>종로구</td>\n",
       "      <td>37.560850</td>\n",
       "      <td>126.993653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Oil_store                      주소      가격 셀프     상표    구        lat  \\\n",
       "0            서남주유소            서울 중구 통일로 30  2099.0  N  SK에너지   중구  37.558348   \n",
       "1            장충주유소  서울 중구 장충단로 202 (장충동1가)  2088.0  N  SK에너지   중구  37.562719   \n",
       "2          뉴서울(강남)    서울 강남구 언주로 716 (논현동)  2082.0  N  SK에너지  강남구  37.517636   \n",
       "3     에스씨(주) 역전주유소            서울 중구 퇴계로 15  2079.0  N  GS칼텍스   중구  37.557001   \n",
       "4            서계주유소    서울 용산구 청파로 367 (서계동)  2059.0  N  GS칼텍스  용산구  37.552290   \n",
       "5            필동주유소   서울 중구  퇴계로 196 (필동2가)  2059.0  N  GS칼텍스   중구  37.560850   \n",
       "6  SK에너지(주) 퇴계로주유소    서울 중구 퇴계로 228 (필동2가)  2049.0  N  SK에너지   중구  37.561648   \n",
       "7   지에스칼텍스(주)초동주유소           서울 중구 마른내로 31  2035.0  N  GS칼텍스   중구  37.564944   \n",
       "8     수림석유 제3한강주유소    서울 용산구 한남대로 45 (한남동)  2018.0  N  GS칼텍스  용산구  37.532731   \n",
       "9   SK네트웍스(주)재동주유소     서울 종로구 율곡로 58 (경운동)  1999.0  N  SK에너지  종로구  37.560850   \n",
       "\n",
       "          lng  \n",
       "0  126.972090  \n",
       "1  127.007010  \n",
       "2  127.035756  \n",
       "3  126.974495  \n",
       "4  126.968935  \n",
       "5  126.993653  \n",
       "6  126.997142  \n",
       "7  126.991136  \n",
       "8  127.007481  \n",
       "9  126.993653  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil_price_top10.reset_index(inplace=True)\n",
    "oil_price_top10.drop('index',1,inplace=True)\n",
    "oil_price_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[37.5583477, 126.9720901]\n",
      "[37.5627192, 127.0070101]\n",
      "[37.51763589999999, 127.0357559]\n",
      "[37.5570008, 126.9744945]\n",
      "[37.5522895, 126.9689354]\n",
      "[37.56084999999999, 126.9936529]\n",
      "[37.5616483, 126.9971419]\n",
      "[37.5649444, 126.9911359]\n",
      "[37.5327311, 127.0074814]\n",
      "[37.56084999999999, 126.9936529]\n"
     ]
    }
   ],
   "source": [
    "for n in oil_price_top10.index:\n",
    "    print([oil_price_top10['lat'][n], oil_price_top10['lng'][n]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 잘 도출이 되네요! 그럼 하위 10개도 제대로 해줍시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>Oil_store</th>\n",
       "      <th>주소</th>\n",
       "      <th>가격</th>\n",
       "      <th>셀프</th>\n",
       "      <th>상표</th>\n",
       "      <th>구</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>강산제2주유소</td>\n",
       "      <td>서울 성북구 보국문로 52</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>성북구</td>\n",
       "      <td>37.609163</td>\n",
       "      <td>127.010074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>이케이에너지㈜ 강산주유소</td>\n",
       "      <td>서울 성북구 보문로 27 (보문동7가)</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>성북구</td>\n",
       "      <td>37.577912</td>\n",
       "      <td>127.022774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>(주)삼표에너지 정릉주유소</td>\n",
       "      <td>서울 성북구 보국문로 35 (정릉동)</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>GS칼텍스</td>\n",
       "      <td>성북구</td>\n",
       "      <td>37.607660</td>\n",
       "      <td>127.009858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>원천주유소</td>\n",
       "      <td>서울 성북구 돌곶이로 142 (장위동)</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>N</td>\n",
       "      <td>알뜰주유소</td>\n",
       "      <td>성북구</td>\n",
       "      <td>37.614921</td>\n",
       "      <td>127.052752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>북서울고속주유소</td>\n",
       "      <td>서울 강북구 삼양로 410 (수유동)</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>S-OIL</td>\n",
       "      <td>강북구</td>\n",
       "      <td>37.614921</td>\n",
       "      <td>127.052752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>(주)석산에너지</td>\n",
       "      <td>서울 강북구 삼양로 316 (수유동)</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>강북구</td>\n",
       "      <td>37.577912</td>\n",
       "      <td>127.022774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>화곡역주유소</td>\n",
       "      <td>서울특별시 강서구  강서로 154 (화곡동)</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>알뜰주유소</td>\n",
       "      <td>강서구</td>\n",
       "      <td>37.614921</td>\n",
       "      <td>127.052752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>뉴신정주유소</td>\n",
       "      <td>서울 강서구 곰달래로 207(화곡동)</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>N</td>\n",
       "      <td>알뜰주유소</td>\n",
       "      <td>강서구</td>\n",
       "      <td>37.577912</td>\n",
       "      <td>127.022774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>형산석유(주)원주유소</td>\n",
       "      <td>서울 양천구 남부순환로 408</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>N</td>\n",
       "      <td>현대오일뱅크</td>\n",
       "      <td>양천구</td>\n",
       "      <td>37.577912</td>\n",
       "      <td>127.022774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>현대주유소</td>\n",
       "      <td>서울 양천구 남부순환로 372 (신월동)</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>S-OIL</td>\n",
       "      <td>양천구</td>\n",
       "      <td>37.614921</td>\n",
       "      <td>127.052752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Oil_store                        주소      가격 셀프      상표    구  \\\n",
       "0         강산제2주유소            서울 성북구 보국문로 52  1237.0  Y  현대오일뱅크  성북구   \n",
       "1   이케이에너지㈜ 강산주유소     서울 성북구 보문로 27 (보문동7가)  1237.0  Y   GS칼텍스  성북구   \n",
       "2  (주)삼표에너지 정릉주유소      서울 성북구 보국문로 35 (정릉동)  1237.0  Y   GS칼텍스  성북구   \n",
       "3           원천주유소     서울 성북구 돌곶이로 142 (장위동)  1237.0  N   알뜰주유소  성북구   \n",
       "4        북서울고속주유소      서울 강북구 삼양로 410 (수유동)  1257.0  Y   S-OIL  강북구   \n",
       "5        (주)석산에너지      서울 강북구 삼양로 316 (수유동)  1257.0  Y  현대오일뱅크  강북구   \n",
       "6          화곡역주유소  서울특별시 강서구  강서로 154 (화곡동)  1258.0  Y   알뜰주유소  강서구   \n",
       "7          뉴신정주유소      서울 강서구 곰달래로 207(화곡동)  1258.0  N   알뜰주유소  강서구   \n",
       "8     형산석유(주)원주유소          서울 양천구 남부순환로 408  1258.0  N  현대오일뱅크  양천구   \n",
       "9           현대주유소    서울 양천구 남부순환로 372 (신월동)  1258.0  Y   S-OIL  양천구   \n",
       "\n",
       "         lat         lng  \n",
       "0  37.609163  127.010074  \n",
       "1  37.577912  127.022774  \n",
       "2  37.607660  127.009858  \n",
       "3  37.614921  127.052752  \n",
       "4  37.614921  127.052752  \n",
       "5  37.577912  127.022774  \n",
       "6  37.614921  127.052752  \n",
       "7  37.577912  127.022774  \n",
       "8  37.577912  127.022774  \n",
       "9  37.614921  127.052752  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil_price_bottom10.reset_index(inplace=True)\n",
    "oil_price_bottom10.drop('index',1,inplace=True)\n",
    "oil_price_bottom10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[37.6091627, 127.0100739]\n",
      "[37.5779125, 127.0227737]\n",
      "[37.6076598, 127.0098583]\n",
      "[37.614921, 127.0527521]\n",
      "[37.614921, 127.0527521]\n",
      "[37.5779125, 127.0227737]\n",
      "[37.614921, 127.0527521]\n",
      "[37.5779125, 127.0227737]\n",
      "[37.5779125, 127.0227737]\n",
      "[37.614921, 127.0527521]\n"
     ]
    }
   ],
   "source": [
    "for n in oil_price_bottom10.index:\n",
    "    print([oil_price_bottom10['lat'][n], oil_price_bottom10['lng'][n]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "잘 정리되었습니다! 그럼 이제 지도에다 시각화해봅시다! (아래 코드는 교재 코드에요. 교재에서는 혹시나 몰라서 결측값에 대비해 if로 처리해줬어요) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
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                {"bubblingMouseEvents": true, "color": "#3186cc", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 15, "stroke": true, "weight": 3}
            ).addTo(map_e2f2c1d40f3c4157b6751ffe69310158);
        
    
            var circle_marker_f76966a876914ae8ae64286e57d79b6b = L.circleMarker(
                [37.614921, 127.0527521],
                {"bubblingMouseEvents": true, "color": "#3186cc", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 15, "stroke": true, "weight": 3}
            ).addTo(map_e2f2c1d40f3c4157b6751ffe69310158);
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x111a78390>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map = folium.Map(location=[37.5202, 126.975], zoom_start=10.5)\n",
    "\n",
    "for n in oil_price_top10.index:\n",
    "    if pd.notnull(oil_price_top10['lat'][n]):\n",
    "        folium.CircleMarker([oil_price_top10['lat'][n], oil_price_top10['lng'][n]], \n",
    "                                  radius=15, color='#CD3181', \n",
    "                                  fill_color='#CD3181',\n",
    "                                  fill=True).add_to(map)\n",
    "    \n",
    "for n in oil_price_bottom10.index:\n",
    "    if pd.notnull(oil_price_bottom10['lat'][n]): \n",
    "        folium.CircleMarker([oil_price_bottom10['lat'][n], \n",
    "                                  oil_price_bottom10['lng'][n]], \n",
    "                                  radius=15, color='#3186cc', \n",
    "                                  fill_color='#3186cc',\n",
    "                                  fill=True).add_to(map)\n",
    "        \n",
    "map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "대체로 '중구','용산구'에 비싼 주유소가 몰려있고, '강북구','성북구'에 싼 주유소가 몰려있다는 것을 확인할 수 있네요! \n",
    "\n",
    "### 공부를 끝내며 \n",
    "\n",
    "이번 장은 Selenium을 활용하는데 많은 어려움을 겪으셨을 것 같습니다. 웹 드라이버 설치부터 사용까지 쉽지 않으셨을 것이라 생각하는데요! 그래도 여기까지 온 것이면 해결을 다 하셨다는 얘기겠죠? 이렇게 팩트 체크를 해보니 어떠셨나요? 이번 주차도 무척 재미있는 프로젝트였던 것 같습니다. 기존에 익숙했던 pandas, folium, google maps, sns, matplotlib 등은 계속해서 사용할테니 사용법에 익숙해지시면 좋겠네요! 그럼 다음 프로젝트에서 만나요!❤️"
   ]
  }
 ],
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