2020_SPRING_RUSH/week5/week5_5.2_naver_movies_rank.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🦉COSADAMA Introduction to Data Science Study\n",
    "\n",
    "- 일자: 2020-04-13\n",
    "- 작성자: 박하람 \n",
    "- 참고자료: 파이썬으로 데이터 주무르기(민형기), 점프 투 파이썬(박응용), 파이썬 입문과 크롤링 기초 부트캠프(잔재미코딩)\n",
    "- 교재: 158 - 166쪽 (깃헙 교안으로 공부하면서, 궁금한 부분은 교재를 참고하세요. 혹은 깃헙 교안을 다 보고 교재를 복습 겸 읽어봐도 됩니다!)\n",
    "\n",
    "## 5-2. 네이버 영화 크롤링\n",
    "\n",
    "이번 주차에는 지난 시간에 배운 **웹 크롤링**을 활용해 **시카고 샌드위치 맛집 분석**을 해보려 합니다. 더불어 네이버 영화 사이트에서 **네이버 영화 평점 순으로 시각화**하는 것까지 해보려고 해요. 당연히 지난 시간에 배웠던 HTML/CSS 지식, 정규 표현식, BeautifulSoup과 urllib 등도 함께 모두 사용해 볼겁니다. \n",
    "\n",
    "'네이버 영화 크롤링'에서는 네이버 영화 사이트에서 영화 제목, 평점, 일자까지 가져와 영화를 평점 순대로 시각화해보고, 일자에 따라 평점이 변화는지까지도 살펴보려 합니다. BeautifulSoup과 urllib을 다시 활용해보고 matplotlib으로 시각화까지 하려 합니다. \n",
    "\n",
    "### 5.1 urllib과 BeautifulSoup으로 사이트에 접근하기 \n",
    "\n",
    "urllib과 BeautifulSoup으로 네이버 영화 평점 순 배열이 되어 있는 사이틀를 가져오려 합니다. 우리가 가져올 것은 영화의 이름, 평점, 날짜입니다.\n",
    "\n",
    "- [네이버 영화](https://movie.naver.com/movie/sdb/rank/rmovie.nhn?sel=cur&date=20200327) \n",
    "\n",
    "필요한 모듈들부터 import 해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "from urllib.request import urlopen\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그럼 해당 사이트의 html을 가져오기 위해서 필요한 기본적인 작업들을 가져와 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_base = 'https://movie.naver.com/'\n",
    "url_syb = 'movie/sdb/rank/rmovie.nhn?sel=cur&date=20200327'\n",
    "\n",
    "page = url_base+url_syb \n",
    "html = urlopen(page)\n",
    "soup = BeautifulSoup(html, 'html.parser')\n",
    "# soup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "잘 가져온 것 같네요ㅎㅎ 그러면 이제 다시 크롬 개발자 도구를 켜서 우리가 필요로 하는 정보들의 태그가 어디에 있는지 확인하는 것이 중요하겠죠. 이름이 어디있나 봤더니 div 태그의 class tit5에 있습니다. (직접 찾아보는 것, 잊지마세요!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=181710\" title=\"포드 V 페라리\">포드 V 페라리</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=19099\" title=\"트루먼 쇼\">트루먼 쇼</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=31013\" title=\"빌리 엘리어트\">빌리 엘리어트</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=189111\" title=\"두 교황\">두 교황</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=47152\" title=\"테이큰\">테이큰</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=148338\" title=\"동급생\">동급생</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=187308\" title=\"조조 래빗\">조조 래빗</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=168050\" title=\"스타 이즈 본\">스타 이즈 본</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=92075\" title=\"어바웃 타임\">어바웃 타임</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=158578\" title=\"흑집사 : 북 오브 더 아틀란틱\">흑집사 : 북 오브 더 아틀란틱</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=189000\" title=\"나이브스 아웃\">나이브스 아웃</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=17327\" title=\"히트\">히트</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=156185\" title=\"극장판 쿠로코의 농구 라스트 게임\">극장판 쿠로코의 농구 라스트 게임</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=24830\" title=\"노팅 힐\">노팅 힐</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=17970\" title=\"샤인\">샤인</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=188056\" title=\"결혼 이야기\">결혼 이야기</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=185282\" title=\"타오르는 여인의 초상\">타오르는 여인의 초상</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=96379\" title=\"비긴 어게인\">비긴 어게인</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=66158\" title=\"어거스트 러쉬\">어거스트 러쉬</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=187321\" title=\"1917\">1917</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=129072\" title=\"스포트라이트\">스포트라이트</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=27260\" title=\"파이트 클럽\">파이트 클럽</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=66440\" title=\"슬럼독 밀리어네어\">슬럼독 밀리어네어</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=186613\" title=\"작은 아씨들\">작은 아씨들</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=143456\" title=\"브리짓 존스의 베이비\">브리짓 존스의 베이비</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=154298\" title=\"아이리시맨\">아이리시맨</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=179307\" title=\"벌새\">벌새</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=31565\" title=\"브리짓 존스의 일기\">브리짓 존스의 일기</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=17062\" title=\"세븐\">세븐</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=134963\" title=\"라라랜드\">라라랜드</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=190728\" title=\"다크 워터스\">다크 워터스</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=10453\" title=\"졸업\">졸업</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=74610\" title=\"미드나잇 인 파리\">미드나잇 인 파리</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=101950\" title=\"그녀\">그녀</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=140895\" title=\"러브 라이브! 더 스쿨 아이돌 무비\">러브 라이브! 더 스쿨 아이돌 무비</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=189046\" title=\"신비아파트 극장판 하늘도깨비 대 요르문간드\">신비아파트 극장판 하늘도깨비 대 요르문간드</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=167651\" title=\"극한직업\">극한직업</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=161967\" title=\"기생충\">기생충</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=92505\" title=\"아무르\">아무르</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=66381\" title=\"맘마미아!\">맘마미아!</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=53152\" title=\"500일의 썸머\">500일의 썸머</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=173840\" title=\"레토\">레토</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=97612\" title=\"그랜드 부다페스트 호텔\">그랜드 부다페스트 호텔</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=86843\" title=\"케빈에 대하여\">케빈에 대하여</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=89755\" title=\"레미제라블\">레미제라블</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=181711\" title=\"러브 라이브! 선샤인!! 더 스쿨 아이돌 무비 오버 더 레인보우\">러브 라이브! 선샤인!! 더 스쿨 아이돌 무비 오버 더 레인보우</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=139438\" title=\"유스\">유스</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=179398\" title=\"메기\">메기</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=190720\" title=\"젠틀맨\">젠틀맨</a>\n",
       " </div>, <div class=\"tit5\">\n",
       " <a href=\"/movie/bi/mi/basic.nhn?code=78845\" title=\"우리도 사랑일까\">우리도 사랑일까</a>\n",
       " </div>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.find_all('div', class_='tit5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "잘 가져왔네요! 그럼 제목만 가져와보겠습니다. 우선 리스트를 벗기고, 첫번째 제목부터 가져와보도록 하죠."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<div class=\"tit5\">\n",
       "<a href=\"/movie/bi/mi/basic.nhn?code=181710\" title=\"포드 V 페라리\">포드 V 페라리</a>\n",
       "</div>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.find_all('div', class_='tit5')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'포드 V 페라리'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.find_all('div', class_='tit5')[0].a['title']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'포드 V 페라리'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.find_all('div', class_='tit5')[0].a.string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 string을 써서도 가져올 수 있습니다. 그 다음은 평점을 가져와 볼게요! tag가 무엇인지 꼭 확인해보세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'9.50'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.find_all('td', class_='point')[0].string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "날짜에 따라 달라지는 평점을 보고 싶기 때문에, 날짜를 판다스로 만들어 주려고 합니다. 날짜를 만덜어 주는 함수는 date_range입니다.\n",
    "\n",
    "2019-12-01부터 시작해 총 100일의 날짜를 생성하였습니다. freq에 무엇이 들어가냐에 따라 출력되는 날이 달라져요. 'D'는 일(day)이고, 시간까지 함께 뽑고 싶다면 'H'를 적어주면 됩니다. 그 외에도 다양한 것들이 있어요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-12-01', '2019-12-02', '2019-12-03', '2019-12-04',\n",
       "               '2019-12-05', '2019-12-06', '2019-12-07', '2019-12-08',\n",
       "               '2019-12-09', '2019-12-10', '2019-12-11', '2019-12-12',\n",
       "               '2019-12-13', '2019-12-14', '2019-12-15', '2019-12-16',\n",
       "               '2019-12-17', '2019-12-18', '2019-12-19', '2019-12-20',\n",
       "               '2019-12-21', '2019-12-22', '2019-12-23', '2019-12-24',\n",
       "               '2019-12-25', '2019-12-26', '2019-12-27', '2019-12-28',\n",
       "               '2019-12-29', '2019-12-30', '2019-12-31', '2020-01-01',\n",
       "               '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05',\n",
       "               '2020-01-06', '2020-01-07', '2020-01-08', '2020-01-09',\n",
       "               '2020-01-10', '2020-01-11', '2020-01-12', '2020-01-13',\n",
       "               '2020-01-14', '2020-01-15', '2020-01-16', '2020-01-17',\n",
       "               '2020-01-18', '2020-01-19', '2020-01-20', '2020-01-21',\n",
       "               '2020-01-22', '2020-01-23', '2020-01-24', '2020-01-25',\n",
       "               '2020-01-26', '2020-01-27', '2020-01-28', '2020-01-29',\n",
       "               '2020-01-30', '2020-01-31', '2020-02-01', '2020-02-02',\n",
       "               '2020-02-03', '2020-02-04', '2020-02-05', '2020-02-06',\n",
       "               '2020-02-07', '2020-02-08', '2020-02-09', '2020-02-10',\n",
       "               '2020-02-11', '2020-02-12', '2020-02-13', '2020-02-14',\n",
       "               '2020-02-15', '2020-02-16', '2020-02-17', '2020-02-18',\n",
       "               '2020-02-19', '2020-02-20', '2020-02-21', '2020-02-22',\n",
       "               '2020-02-23', '2020-02-24', '2020-02-25', '2020-02-26',\n",
       "               '2020-02-27', '2020-02-28', '2020-02-29', '2020-03-01',\n",
       "               '2020-03-02', '2020-03-03', '2020-03-04', '2020-03-05',\n",
       "               '2020-03-06', '2020-03-07', '2020-03-08', '2020-03-09'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = pd.date_range('2019-12-01', periods=100, freq='D')\n",
    "date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 100 페이지 크롤링 하기\n",
    "\n",
    "그럼 해당 정보들이 모두 어디에 담겨져 있는지 알았으니, 이를 이제 가져와야 하겠죠. 그 전에 urllib.parse.quote에 대해 알아볼 것인데요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'%ED%8C%8C%EC%9D%B4%EC%8D%AC'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import urllib \n",
    "\n",
    "url = urllib.parse.quote('파이썬')\n",
    "url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 urllib.parse.quote는 텍스트(한글이나 숫자)를 url에 적합한 용어로 바꿔주는 것을 의미합니다. 이것이 왜 필요하냐면, url 주소에 날짜가 적혀있으므로 날짜를 용이하게 바꿔주면서 크롤링 하기 위해서에요. \n",
    "\n",
    "- https://movie.naver.com/movie/sdb/rank/rmovie.nhn?sel=cur&date=20200327\n",
    "\n",
    "지난 시간에 했던 것이 기억날 것 같은데요, 여기에 맨 뒤 date를 변경해보세요. 그러면 그 일자에 맞춰서 영화 평점 순이 바뀌게 됩니다. 앞서 만들어둔 date를 활용해 url을 변경해주어 총 100 페이지에서 크롤링을 해올 것이랍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "790fb6eb18e64e99b05872ce77d9172b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import urllib\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "movie_date = []\n",
    "movie_name = []\n",
    "movie_point = []\n",
    "\n",
    "date = pd.date_range('2019-12-01', periods=100, freq='D')\n",
    "\n",
    "for today in tqdm_notebook(date):\n",
    "    html = \"http://movie.naver.com/\" + \\\n",
    "                                    \"movie/sdb/rank/rmovie.nhn?sel=cur&date={date}\"\n",
    "    response = urlopen(html.format(date=\n",
    "                                   urllib.parse.quote(today.strftime('%Y%m%d'))))\n",
    "    soup = BeautifulSoup(response, \"html.parser\")\n",
    "    \n",
    "    end = len(soup.find_all('td', 'point'))\n",
    "    \n",
    "    movie_date.extend([today for n in range(0, end)])\n",
    "    movie_name.extend([soup.find_all('div', 'tit5')[n].a.string for n in range(0, end)])\n",
    "    movie_point.extend([soup.find_all('td', 'point')[n].string for n in range(0, end)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그냥 data를 quote에 넣어주면 error가 뜨는데요, url 날짜 양식에 맞도록 형식을 저장해주는 것이 필요합니다. 형식에 맞춰서 년-월-일이 나타나도록 해주는 것이 .surftime이에요.\n",
    "\n",
    "또한 여기서는 append가 아닌 extend()를 사용했죠. 차이점은 아래 링크를 보시면 됩니다. \n",
    "- [extend()와 append()의 차이점](https://hashcode.co.kr/questions/23/파이썬에-append와-extend의-차이점)\n",
    "\n",
    "그럼 잘 가져와졌는지 확인해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2019-12-01 00:00:00', freq='D'),\n",
       " Timestamp('2019-12-01 00:00:00', freq='D'),\n",
       " Timestamp('2019-12-01 00:00:00', freq='D'),\n",
       " Timestamp('2019-12-01 00:00:00', freq='D'),\n",
       " Timestamp('2019-12-01 00:00:00', freq='D')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_date[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['주전장', '교회오빠', '시간을 달리는 소녀', '윤희에게', '늑대아이']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_name[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['9.49', '9.27', '9.20', '9.17', '9.11']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_point[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "날짜, 영화 이름, 영화 평점까지 모두 잘 가져왔습니다. 이것을 이제 dataframe에 담아 가공하기 좋게 넣어보죠."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>date</th>\n",
       "      <th>name</th>\n",
       "      <th>point</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>주전장</td>\n",
       "      <td>9.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>교회오빠</td>\n",
       "      <td>9.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>시간을 달리는 소녀</td>\n",
       "      <td>9.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>윤희에게</td>\n",
       "      <td>9.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>늑대아이</td>\n",
       "      <td>9.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>미쓰백</td>\n",
       "      <td>9.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>아이리시맨</td>\n",
       "      <td>8.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>벌새</td>\n",
       "      <td>8.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>여배우는 오늘도</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>너의 이름은.</td>\n",
       "      <td>8.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date        name point\n",
       "0 2019-12-01         주전장  9.49\n",
       "1 2019-12-01        교회오빠  9.27\n",
       "2 2019-12-01  시간을 달리는 소녀  9.20\n",
       "3 2019-12-01        윤희에게  9.17\n",
       "4 2019-12-01        늑대아이  9.11\n",
       "5 2019-12-01         미쓰백  9.10\n",
       "6 2019-12-01       아이리시맨  8.96\n",
       "7 2019-12-01          벌새  8.89\n",
       "8 2019-12-01    여배우는 오늘도  8.84\n",
       "9 2019-12-01     너의 이름은.  8.76"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie = pd.DataFrame({'date':movie_date, 'name':movie_name,\n",
    "                     'point':movie_point})\n",
    "movie.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여기에는 날짜 별로 영화들이 담겨있어요. 날짜가 아닌 영화별로 점수를 보고 싶다면, pivot_table을 활용하면 됩니다. 여러 날짜에 동일한 영화들이 있으니, 평점은 점수의 평균으로 합쳐보겠습니다. (교재는 합산이지만 저는 평균이 더 낫겠다 생각했어요. 합산으로 할 경우 영화가 걸린 일 수에 영향을 많이 받으니까요) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "DataError",
     "evalue": "No numeric types to aggregate",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mDataError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-305c43c74370>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmovie_unique\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmovie\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mmovie_best\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmovie_unique\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'point'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmovie_best\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/pivot.py\u001b[0m in \u001b[0;36mpivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0mgrouped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m     \u001b[0magged\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrouped\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdropna\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magged\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magged\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m         \u001b[0magged\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magged\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"all\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1453\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"aggregate\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1454\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0maggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1455\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1456\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1457\u001b[0m     \u001b[0magg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maggregate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    227\u001b[0m         \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_maybe_mangle_lambdas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m         \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_aggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_level\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_aggregate\u001b[0;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[1;32m    568\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_cython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 570\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    571\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    572\u001b[0m         \u001b[0;31m# caller can react\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1203\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1204\u001b[0m             return self._cython_agg_general(\n\u001b[0;32m-> 1205\u001b[0;31m                 \u001b[0;34m\"mean\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1206\u001b[0m             )\n\u001b[1;32m   1207\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mGroupByError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36m_cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count)\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_cython_agg_general\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m         new_items, new_blocks = self._cython_agg_blocks(\n\u001b[0;32m--> 128\u001b[0;31m             \u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_count\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m         )\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap_agged_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_items\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36m_cython_agg_blocks\u001b[0;34m(self, how, alt, numeric_only, min_count)\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_blocks\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mDataError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No numeric types to aggregate\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;31m# reset the locs in the blocks to correspond to our\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDataError\u001b[0m: No numeric types to aggregate"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "movie_unique = pd.pivot_table(movie, index=['name'], aggfunc=np.mean)\n",
    "movie_best = movie_unique.sort_values(by='point', ascending=False)\n",
    "movie_best.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "어, 이렇게 하니 오류가 뜹니다. 왜 일까요? 오류를 읽어보니 'No numeric types to aggregate'라고 하죠? 그렇다는 얘기는 계산할 수 있는 숫자가 아니라는 얘깁니다. 그러면 point 컬럼의 자료형이 뭔지 볼게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4499 entries, 0 to 4498\n",
      "Data columns (total 3 columns):\n",
      "date     4499 non-null datetime64[ns]\n",
      "name     4499 non-null object\n",
      "point    4499 non-null object\n",
      "dtypes: datetime64[ns](1), object(2)\n",
      "memory usage: 105.6+ KB\n"
     ]
    }
   ],
   "source": [
    "movie.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "보니까 point는 object라 계산이 안되었군요. 사실 앞의 13번 코드를 보면 이미 숫자로 출력되지 않은 것을 알 수 있습니다. 그럼 point를 숫자로 바꾸어줘야 계산이 되겠죠. 아래와 같이 바꾸어 주면 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie['point'] = movie['point'].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37215.25"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie['point'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 잘 계산이 되네요. 그럼 다시 pivot_table을 해보죠."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>point</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>그린 북</td>\n",
       "      <td>9.606667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>가버나움</td>\n",
       "      <td>9.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>포드 V 페라리</td>\n",
       "      <td>9.540729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>주전장</td>\n",
       "      <td>9.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>원더</td>\n",
       "      <td>9.490000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             point\n",
       "name              \n",
       "그린 북      9.606667\n",
       "가버나움      9.590000\n",
       "포드 V 페라리  9.540729\n",
       "주전장       9.510000\n",
       "원더        9.490000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "movie_unique = pd.pivot_table(movie, index=['name'], aggfunc=np.mean)\n",
    "movie_best = movie_unique.sort_values(by='point', ascending=False)\n",
    "movie_best.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "오호라, 잘 되었습니다. 그린 북과 가버나움이 평점이 높네요. 이것들 모두 제가 알기론 인권에 관한 영화로 알고 있는데 말이죠... 포드 V 페라리도 평점이 높습니다. 그럼 저는 82년생 김지영 영화가 얼마나 테러를 당했는지 보기 위해서 이 영화만 평점을 가져와 볼게요.\n",
    "\n",
    "- [pandas query 함수](https://kongdols-room.tistory.com/120)\n",
    "\n",
    "query 함수를 사용해 볼 것인데요, query는 조건식을 문자열로 입력 받아 해당 조건에 만족하는 행을 추출해주는 함수에요. 불린 인덱싱과 기능이 비슷하나, query가 방대한 양의 데이터를 처리할 때 성능면에서 더 우위가 높다고 해요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>date</th>\n",
       "      <th>name</th>\n",
       "      <th>point</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>82년생 김지영</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>82년생 김지영</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>82년생 김지영</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>82년생 김지영</td>\n",
       "      <td>6.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>231</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>82년생 김지영</td>\n",
       "      <td>6.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3727</td>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>82년생 김지영</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3769</td>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>82년생 김지영</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3812</td>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>82년생 김지영</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3855</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>82년생 김지영</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3899</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>82년생 김지영</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date      name  point\n",
       "39   2019-12-01  82년생 김지영   6.69\n",
       "87   2019-12-02  82년생 김지영   6.69\n",
       "133  2019-12-03  82년생 김지영   6.69\n",
       "181  2019-12-04  82년생 김지영   6.68\n",
       "231  2019-12-05  82년생 김지영   6.68\n",
       "...         ...       ...    ...\n",
       "3727 2020-02-21  82년생 김지영   6.69\n",
       "3769 2020-02-22  82년생 김지영   6.69\n",
       "3812 2020-02-23  82년생 김지영   6.69\n",
       "3855 2020-02-24  82년생 김지영   6.69\n",
       "3899 2020-02-25  82년생 김지영   6.69\n",
       "\n",
       "[81 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = movie.query('name == [\"82년생 김지영\"]')\n",
    "tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ㅋㅋㅋㅋㅋ 6.68-6.69 처참하네요..ㅋㅋ 저는 불린 인덱싱으로 메기 영화도 한번 평점을 봤어요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>name</th>\n",
       "      <th>point</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>메기</td>\n",
       "      <td>8.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>메기</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>메기</td>\n",
       "      <td>8.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>메기</td>\n",
       "      <td>8.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>218</td>\n",
       "      <td>메기</td>\n",
       "      <td>8.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3758</td>\n",
       "      <td>메기</td>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3800</td>\n",
       "      <td>메기</td>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3844</td>\n",
       "      <td>메기</td>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3887</td>\n",
       "      <td>메기</td>\n",
       "      <td>8.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3930</td>\n",
       "      <td>메기</td>\n",
       "      <td>8.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  point\n",
       "23     메기   8.34\n",
       "70     메기   8.33\n",
       "118    메기   8.32\n",
       "167    메기   8.32\n",
       "218    메기   8.32\n",
       "...   ...    ...\n",
       "3758   메기   8.26\n",
       "3800   메기   8.26\n",
       "3844   메기   8.26\n",
       "3887   메기   8.25\n",
       "3930   메기   8.25\n",
       "\n",
       "[88 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.loc[movie['name'] == '메기', ['name','point']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "오, 이건 비교적 좋습니다. 그럼 '82년생 김지영' 영화를 시간에 따른 평점을 시각화해볼게요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "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": [
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline \n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(tmp['date'], tmp['point'])\n",
    "plt.legend(loc='best')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아, 이게 이거 하나만 표시하다보니 아주 세밀하게 y축이 나타났네요. 뭐 아무튼 평점은 6.68과 6.69에서 왔다갔다 합니다. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 영화별 날짜 변화에 따른 평점 변화 시각화 \n",
    "\n",
    "이제는 날짜에 따라서 영화의 평점 변화가 어떻게 나타나는지 확인해보려고 해요. 여기에서도 pivot_table을 사용하려 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"21\" halign=\"left\">point</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th>1917</th>\n",
       "      <th>21 브릿지: 테러 셧다운</th>\n",
       "      <th>7번방의 선물</th>\n",
       "      <th>82년생 김지영</th>\n",
       "      <th>가버나움</th>\n",
       "      <th>가장 따뜻한 색, 블루</th>\n",
       "      <th>가장 보통의 연애</th>\n",
       "      <th>감쪽같은 그녀</th>\n",
       "      <th>걸어도 걸어도</th>\n",
       "      <th>겟 아웃</th>\n",
       "      <th>...</th>\n",
       "      <th>하이큐!! 승자와 패자</th>\n",
       "      <th>하이큐!! 재능과 센스</th>\n",
       "      <th>항거:유관순 이야기</th>\n",
       "      <th>해리 포터와 아즈카반의 죄수</th>\n",
       "      <th>해치지않아</th>\n",
       "      <th>해피 엔드</th>\n",
       "      <th>핵소 고지</th>\n",
       "      <th>행동하는 양심 김대중</th>\n",
       "      <th>환상의 빛</th>\n",
       "      <th>히트맨</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.68</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.68</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.68</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 193 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           point                                                               \\\n",
       "name        1917 21 브릿지: 테러 셧다운 7번방의 선물 82년생 김지영  가버나움 가장 따뜻한 색, 블루 가장 보통의 연애   \n",
       "date                                                                            \n",
       "2019-12-01   NaN            NaN     NaN     6.69   NaN          NaN      7.83   \n",
       "2019-12-02   NaN            NaN     NaN     6.69   NaN          NaN      7.83   \n",
       "2019-12-03   NaN            NaN     NaN     6.69   NaN          NaN       NaN   \n",
       "2019-12-04   NaN            NaN     NaN     6.68   NaN          NaN       NaN   \n",
       "2019-12-05   NaN            NaN     NaN     6.68   NaN          NaN       NaN   \n",
       "2019-12-06   NaN            NaN     NaN     6.68   NaN          NaN       NaN   \n",
       "2019-12-07   NaN            NaN     NaN     6.68   NaN          NaN       NaN   \n",
       "2019-12-08   NaN            NaN     NaN     6.68  9.59          NaN       NaN   \n",
       "2019-12-09   NaN            NaN     NaN     6.68  9.59          NaN       NaN   \n",
       "2019-12-10   NaN            NaN     NaN     6.68  9.59          NaN       NaN   \n",
       "\n",
       "                                 ...                                       \\\n",
       "name       감쪽같은 그녀 걸어도 걸어도 겟 아웃  ... 하이큐!! 승자와 패자 하이큐!! 재능과 센스 항거:유관순 이야기   \n",
       "date                             ...                                        \n",
       "2019-12-01     NaN     NaN  NaN  ...          NaN          NaN        NaN   \n",
       "2019-12-02     NaN     NaN  NaN  ...          NaN          NaN        NaN   \n",
       "2019-12-03     NaN     NaN  NaN  ...          NaN          NaN        NaN   \n",
       "2019-12-04     NaN     NaN  NaN  ...          NaN          NaN        NaN   \n",
       "2019-12-05     NaN     NaN  NaN  ...          NaN          NaN        NaN   \n",
       "2019-12-06    8.21     NaN  NaN  ...          NaN          NaN        NaN   \n",
       "2019-12-07    8.05     NaN  NaN  ...          NaN          NaN        NaN   \n",
       "2019-12-08    7.90     NaN  NaN  ...          NaN          NaN        NaN   \n",
       "2019-12-09    7.80     NaN  NaN  ...          NaN          NaN        NaN   \n",
       "2019-12-10    7.70     NaN  NaN  ...          NaN          NaN        NaN   \n",
       "\n",
       "                                                                    \n",
       "name       해리 포터와 아즈카반의 죄수 해치지않아 해피 엔드 핵소 고지 행동하는 양심 김대중 환상의 빛 히트맨  \n",
       "date                                                                \n",
       "2019-12-01             NaN   NaN  8.27   NaN         NaN   NaN NaN  \n",
       "2019-12-02             NaN   NaN  8.27   NaN         NaN   NaN NaN  \n",
       "2019-12-03             NaN   NaN  8.27   NaN        2.83   NaN NaN  \n",
       "2019-12-04             NaN   NaN  8.27   NaN        3.19   NaN NaN  \n",
       "2019-12-05             NaN   NaN  8.27   NaN        3.15   NaN NaN  \n",
       "2019-12-06             NaN   NaN  8.27   NaN        3.10   NaN NaN  \n",
       "2019-12-07             NaN   NaN  8.27   NaN         NaN   NaN NaN  \n",
       "2019-12-08             NaN   NaN  8.27   NaN        3.06   NaN NaN  \n",
       "2019-12-09             NaN   NaN  8.27   NaN        3.05   NaN NaN  \n",
       "2019-12-10             NaN   NaN  8.27   NaN        3.08   NaN NaN  \n",
       "\n",
       "[10 rows x 193 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_pivot = pd.pivot_table(movie, index=['date'], columns=['name'], values=['point'])\n",
    "movie_pivot.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "흐헉 결측값이 상당히 많네요. 이게 네이버 평점을 매기는 기준을 보니까 '전국기준 현재 상영되고 있는 영화 중 평점 응답자가 300명 이상인 경우'이라고 하더라구요. 이것에 걸리는 것도 있겠고, 평점이 이 시기에 안 매겨지는 경우도 있을 수 있구요. 다양한 이유들이 있습니다.\n",
    "\n",
    "- [droplevel()](https://www.w3resource.com/pandas/series/series-droplevel.php)\n",
    "\n",
    "droplevel은 원하는 컬럼이나 인덱스 전체를 drop해주는 건데, 여기서 왜 해준지 도무지 모르겠어요... (아는 분은 제게 알려주시기 바람) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>1917</th>\n",
       "      <th>21 브릿지: 테러 셧다운</th>\n",
       "      <th>7번방의 선물</th>\n",
       "      <th>82년생 김지영</th>\n",
       "      <th>가버나움</th>\n",
       "      <th>가장 따뜻한 색, 블루</th>\n",
       "      <th>가장 보통의 연애</th>\n",
       "      <th>감쪽같은 그녀</th>\n",
       "      <th>걸어도 걸어도</th>\n",
       "      <th>겟 아웃</th>\n",
       "      <th>...</th>\n",
       "      <th>하이큐!! 승자와 패자</th>\n",
       "      <th>하이큐!! 재능과 센스</th>\n",
       "      <th>항거:유관순 이야기</th>\n",
       "      <th>해리 포터와 아즈카반의 죄수</th>\n",
       "      <th>해치지않아</th>\n",
       "      <th>해피 엔드</th>\n",
       "      <th>핵소 고지</th>\n",
       "      <th>행동하는 양심 김대중</th>\n",
       "      <th>환상의 빛</th>\n",
       "      <th>히트맨</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 193 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name        1917  21 브릿지: 테러 셧다운  7번방의 선물  82년생 김지영  가버나움  가장 따뜻한 색, 블루  \\\n",
       "date                                                                      \n",
       "2019-12-01   NaN             NaN      NaN      6.69   NaN           NaN   \n",
       "2019-12-02   NaN             NaN      NaN      6.69   NaN           NaN   \n",
       "2019-12-03   NaN             NaN      NaN      6.69   NaN           NaN   \n",
       "2019-12-04   NaN             NaN      NaN      6.68   NaN           NaN   \n",
       "2019-12-05   NaN             NaN      NaN      6.68   NaN           NaN   \n",
       "\n",
       "name        가장 보통의 연애  감쪽같은 그녀  걸어도 걸어도  겟 아웃  ...  하이큐!! 승자와 패자  \\\n",
       "date                                           ...                 \n",
       "2019-12-01       7.83      NaN      NaN   NaN  ...           NaN   \n",
       "2019-12-02       7.83      NaN      NaN   NaN  ...           NaN   \n",
       "2019-12-03        NaN      NaN      NaN   NaN  ...           NaN   \n",
       "2019-12-04        NaN      NaN      NaN   NaN  ...           NaN   \n",
       "2019-12-05        NaN      NaN      NaN   NaN  ...           NaN   \n",
       "\n",
       "name        하이큐!! 재능과 센스  항거:유관순 이야기  해리 포터와 아즈카반의 죄수  해치지않아  해피 엔드  핵소 고지  \\\n",
       "date                                                                         \n",
       "2019-12-01           NaN         NaN              NaN    NaN   8.27    NaN   \n",
       "2019-12-02           NaN         NaN              NaN    NaN   8.27    NaN   \n",
       "2019-12-03           NaN         NaN              NaN    NaN   8.27    NaN   \n",
       "2019-12-04           NaN         NaN              NaN    NaN   8.27    NaN   \n",
       "2019-12-05           NaN         NaN              NaN    NaN   8.27    NaN   \n",
       "\n",
       "name        행동하는 양심 김대중  환상의 빛  히트맨  \n",
       "date                                 \n",
       "2019-12-01          NaN    NaN  NaN  \n",
       "2019-12-02          NaN    NaN  NaN  \n",
       "2019-12-03         2.83    NaN  NaN  \n",
       "2019-12-04         3.19    NaN  NaN  \n",
       "2019-12-05         3.15    NaN  NaN  \n",
       "\n",
       "[5 rows x 193 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_pivot.columns = movie_pivot.columns.droplevel()\n",
    "movie_pivot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아무튼 이제 시간에 따른 평점 시각화를 해봅시다. '82년생 김지영', '벌새', '메기', '기생충'의 평점을 확인해볼게요. 기생충은 평점이 좋다고 생각되는 영화라 다른 영화와 평점을 비교하기 위해 넣어봤습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import platform\n",
    "from matplotlib import font_manager, rc\n",
    "\n",
    "path = \"c:/Windows/Fonts/malgun.ttf\"\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": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lJKRoZPbI4NCPoVlDlWBtD3xmpj5d+ygpIbywvb98v/Ye2duiY6zft15Pf/K0/rX9X0pNTNUFQy/QD0b8QEOyhrSmyg0qLSvV/M/m69lPntW1RdeqT9c++v4J39cZ/c6Qz+rfb5qenK6c1JyIHR8AAMQHAnGcOS7tOE0cOFETB06UJFW7alW76hbtY+fhncGhH8W7ivXsJ8/qydVPRrSeyb5kjcgeERxrPSZ3jHp37a0qV6U1e9aoZHdJ8Or6pv2bWnWMXl176dqTr9XFx1+sbindIlp/yf+twI9H/ViXjrxURVuK9NSap3Tvint174p7G92md9fewZswT8o9SSf0OEFdErtEvG4AAKD9EIjjnM98DV6tbEqf9D7qk95HU/KmSJIqqiq09qu1+nz/5xGpU2V1pdbtW6fiXcV6Ye0L+uvqv0qSenTpoYNlB1W+uTw4PyZ3jM4fcr56p/du0Rjsbind9I3e31CiL/qnaKIvUWcPPFtnDzxba/eu1bp96xost+fIHpXsLlHJ7hIt+nyRf1tL1PAew2uF5P4Z/WUWvfHmAAAgsgjEHpCUkKRROaM0KmdUxPddUV2hz776TMW7irVq9yrt27lP5407T6NzRqtvet8OFwyH9xiu4T2GN1tu95HdKt5VHLwS/tL6lzRn7RxJ/jHcwUf55YzRqNxRykzObHRf5VXlWrN3jVbvWa3uXbprTI7/antHa7tI2Xt0r0p2lWjdvnUt/nYkVjaUbtBnxZ/VW+4zn4ZkDdHonNHKTs1u8X6rXbU2lW5S8e5i7Tq8q0XbpiSkaET2CJ2YfaLSktJafGwA8BICMdokyZekkdkjNTJ7pCSpqKhIBYMKYlupdpCTmqOzBpylswacJUmqqq7S+tL1wWEqJbtL9Oa2N+Xk/+GXQd0GBYeWnNDjBG09sFXFu4tVsqtEa/auUUV1Rb39h4bqnl17RuQpJ7sqdmnz/s1t3k8k7Svbp5LdJcG223pwa6yr1DofNr26b3rfWo9z7J7SvV4ZJ6fP938ebItVu1fpQMWBNlUrwRI0NGuoxuSO0eic0RqZPVKpialt2mdHFI/nfm5abpv7oqK6QtsPbg/+rYmWnNQcPlihUyMQAxGQ4EvQsO7DNKz7MF0y7BJJ0sHyg1q1Z1Uw3Pxz2z/10vqXgtukJqZqZPZIXTryUo3JGaMTs0/U3rK9/kC9q0TFu4u1dMvSyFd2fuR3GQnHpR6nk447SVOHTw1+cEj2Jce6WmFZ9o9lOvOMM+stL68u19q9a4PPO/9gxwd6deOrze7PZz4dn3W8pgyaotE5o3VS7knql9GvRR+KDlQc0Krdx86/1za+phc+faFF76vTibNzP8H8fzdqPvyOzh2tvMy8RofJOee04/COWh+8P97zscqqyqJeV5/5gh+saj7cD+o2KOrHBdqLxfKni/Pz893y5ctjcuyioiIVFBTE5NidGe3aOOecth7cqk/3fqq+GX01NGtos2Ok9x31Xz3dV7YvInX4ZM0nOmHECRHZV6SkJaXpxOwT1atrr1hXpdVact7vOLRDq/as0uGKww2u79W1V1SGOdQMv1j71dqIPcu7I4m3c7/m24CSXf77Eg5WHJQkZSRn+IebNfDhZ8+RPdp5xP8I0GRfskZmj9To3NE6Puv4qN9vsfnA5uAH9QPl/m8t0pPSlaUsZWRk1CuflpSmUdmjgs/Wj8X/34cqDtX6ULjj8I4WbZ+UkKQTup8QfA8DMwe2+J6eSOPf2LYxsxXOufyG1nGFGGgnZqb+Gf3VP6N/2NtkdcnShH4TIlaHjC0ZKhhSELH9oeV6du2pnl17tvtxfebT4KzBGpw1uN2PHQ/i+dyvdtXaWLox+E3C7sO7Gyw3JGuIRuWM0km5J2l49+FhP/oykqpdtTbt3+QPx7uKtXrLauWk1X8c5d6yvbWecHRc6nEakztGw3pE//n4Ww9s1Ue7PtL6feuDQ0nyMvM0MHNgi75lOVR5SAs3LtTznz4vyf9hZUzOGI3MHtnoB9ZTe52qMblj2v4m0O4IxAAAxFDNzZdDsobowuMvjHV1muQznwZ3G6zB3Qbru0O/q6Kyxq9Y1jzhqOYRnCW7S7R48+Ko1zEzOVOjc0dr8sDJGpM7RqNyRrX60Z1V1VX+Dys1jzLdXay3S95udMz2r0/+NYG4gyIQAwCAiAt9wtEPR/xQkv+pOtG+ATDZlxyxp/Qk+BI0tPtQDe0+VBcdf5Ek/42MjT0BJxI/foXYIBADAIB2kZzQMW6UbUq0h3wgNmI7OhwAAACIMQIxAAAAPI1ADAAAAE8jEAMAAMDTCMQAAADwNAIxAAAAPI1ADAAAAE8jEAMAAMDTCMQAAADwNAIxAAAAPI1ADAAAAE8jEAMAAMDTCMQAAADwNAIxAAAAPI1ADAAAAE8jEAMAAMDTCMQAAADwNAIxAAAAPI1ADAAAAE8jEAMAAMDTCMQAAADwNAIxAAAAPI1ADAAAAE8jEAMAAMDTCMQAAADwNAIxAAAAPI1ADAAAAE8jEAMAAMDTwgrEZjbAzBaaWZGZvW9mFzZQJsHMZpnZvwKvCZGvLgAAABBZiWGWmyHpD865t80sW9I7ZrbAOedCykyXtM45d7WZ9ZC0wMwmOucqIl1pAAAAIFLCHTKRIGlrYPqgpA11wrAkTZP0oCQ55/ZKekXSlEhUEgAAAIgWq59rGyhkNkjSPZLelHSWpBudc6vrlHnTOTc+ZP5iSQOcc3+qU266/FeT1bNnz5PnzJnT5jfRGgcPHlR6enpMjt2Z0a7xjf6JDto1/tFH0UG7ti/au20KCwtXOOfyG1oX7pCJH0h6TdJfJf1T0l1mdrVzblsz29VL28652ZJmS1J+fr4rKCgIswqRVVRUpFgduzOjXeMb/RMdtGv8o4+ig3ZtX7R39DQ7ZMLMRkga4px7xDl31Dm3XNIcSZfUKerMLDlkfpikdZGrKgAAABB54Ywh3i9ptJllSJKZpUiaKmmlmd0cUm6epCsDZTIlnS3p9chWFwAAAIisZodMOOe2mdmdkl4zs3L5Q/SjgdV5IUVnSnrIzN6VVCXpOp4wAQAAgHgX1hhi59x8SfMbWLUspEyFpCsiVC8AAACgXfBLdQAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8LbG5AmaWIOmNOot7OudG1Ck3R1JvSS6waIVz7tcRqSUAAAAQJc0GYudclaSCmnkz6ydpRgNFe0k6xzl3NGK1AwAAAKLMnHPNlwrdwOz/SprnnHunzvIiSVOaC8RmNl3SdEnq2bPnyXPmzGnR8SPl4MGDSk9Pj8mxOzPaNb7RP9FBu8Y/+ig6aNf2RXu3TWFh4QrnXH5D61oUiM0sQ9LzzrlzG1hXJGmFpJGSkiT9wTm3tKn95efnu+XLl4d9/EgqKipSQUFBTI7dmdGu8Y3+iQ7aNf7RR9FBu7Yv2rttzKzRQNzskIk6rpD0WCPr7pa00Tm31sx6SVpoZpOcc3tbeAwAAACg3YQdiAM3150v6eyG1jvnXguZ3m5m70rKk0QgBgAAQNxqyWPXLpG0IHCTncxstJndXLPSzL5uZr7AdG9J4yStiWRlAQAAgEhryZCJn0q6KGS+h/xXgGucKOn2wJXkckn/4Zw70uYaAgAAAFEUdiB2zk2uM79M0rKQ+cclPR65qgEAAADRxy/VAQAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPA0AjEAAAA8jUAMAAAATyMQAwAAwNMIxAAAAPC0xFhXAAAAoCOqqKjQ1q1bdfTo0XY5Xrdu3bRmzZp2OVZH1qVLF/Xr109JSUlhb0MgBgAAaIWtW7cqIyNDeXl5MrOoH+/AgQPKyMiI+nE6Muec9uzZo61bt2rQoEFhb8eQCQAAgFY4evSosrOz2yUMIzxmpuzs7BZftScQAwAAtBJhOP60pk8IxAAAAPA0AjEAAABaZM+ePdq7d2+sqxExBGIAAIAOavPmzTrvvPNUUFCgU089VfPnzw+ue+KJJ3T22WeroKBAkydP1oYNGyRJxcXFevHFFxvc38UXXyznXK1lCxYs0JNPPilJWrZsmWbMmKElS5ZowYIFzdavsrJS1dXVkqQ///nPWrx4cXBdzfK68w8//LCeeeaZ4PKdO3fqZz/7mc4//3w98cQTweUPPPCAioqKmq1DOHjKBAAAQAd1ww036JZbbtHpp5+uPXv26LTTTtMFF1wgM1Pv3r31+uuvKyEhQW+//bZuuukmPf/889q/f7+2bdsW3MfatWt19dVXy+fz6Ysvvqg3Bvf999/X2LFjJUmJiYlKTk5WUlKSEhPrx8jVq1fr6quvVkpKiqqrq/X555/rvvvu07nnnqukpCSlpKQEy06aNElJSUn6+OOPNWjQICUmJmrmzJlKTk6Wz3fsmu1//ud/6rrrrtOIESN0yy23qGvXrlqzZo3eeeedYL3aikAMAADQRr9/+WOt/mJ/RPc5sk+mbv3OiU2WqaqqUr9+/SRJ6enpGjx4cDDQnnPOOcFyAwcOVHl5eXA+NHAOHz5cb7zxhiTp0ksvrbX/w4cPa/78+dq5c6emTp2qhIQEVVdXyzlXax/BOo8cWeuq7aWXXqqzzjoreMzQsF1zzAsvvFBz5swJhuXly5fX2mdmZqZOPfVUSdL06dP1+uuv63e/+51mzpxZ7ypzaxGIAQAAOqgZM2bo2muv1fjx47VkyRL98Y9/rFemtLRUV155pW677bbgsqqqquD0zp079cgjjygxMVGbN2+ute21116r66+/XsuWLdPixYuVlpamWbNmKTU1VTfccEOTdbv99ts1ceLEWleF6zp8+LCKioq0du1ajRkzRlL9p0R069ZNc+fO1fHHH6+HHnpIP/7xj1VYWKgvvvhCDz/8cJN1CBeBGAAAoI2au5IbLc8884ymTJmiH/3oR5owYYJ+85vfaNasWerbt68kacWKFbrjjjs0Y8YMjRw5MrjdrFmz9NJLL8nMNGjQID366KOSVOuX8O6991716NFDP/nJT/SDH/xA119/vS644AJNnz5dgwcP1oEDBxqs08qVK3XHHXfojDPO0OWXXx5cXllZWW988p133qm77rpLt99+u1544QVJ9QPxPffco4ULF+q9997TzTffrAEDBmjp0qW6//776+2vtZoNxGaWIOmNOot7OudGNFDuAUmnBBb9yjn3z4jUEgAAALWsWbNG69ev1y233CJJys/P17Rp0zR37lxdc801eumll/Tyyy/r2WefVZcuXYLbnXbaafrggw+Umppab9hDQkJCcPraa6+Vz+fT+vXrNXv2bEZwBMAAABVkSURBVG3atEm33Xab+vbtq1GjRunMM8+sV6e77rpLL7/8sjIyMjR37lzNmzcvuG7btm2aPXt2cP6hhx5SQkKCrrrqKu3evVu33HKLbrvttnoh18w0adIkzZo1S7/85S916NAh5eXladq0aZowYULbGjGg2UDsnKuSVBBSqX6SZjRQdLqkdc65q82sh6QFZjbROVcRkZoCAAAgKDMzUyUlJcGfdC4rK9Pzzz+vX/7yl6qoqNBjjz2mBQsWNBh6Dx48qKVLl+rb3/52rXXdu3cPTvt8PlVUVOhHP/qR7rvvPo0aNUoJCQnauHGjbrrpJvXp00e5ubm1tr/xxhv1m9/8RldccYXuueceZWVlBdc9/PDDwau/K1euVGJion7/+99Lkv7rv/5Ljz32mD755BM552oN6ZCkn//85yosLNRjjz2mrl27avPmzfrZz36moUOHqn///m1uy9YMmbhG/ivBdU2TNFmSnHN7zewVSVMkvdz66gEAAKAhffv21c0336wpU6YoOTlZ1dXVuuKKK3TmmWdq9erVWrlyZfCGNsl/093f//53SdKBAwe0fPnyeoH4nnvuqTVfUVGhyspKDR48WKmpqZKkYcOGqX///tq4cWO9pzw0dKNdQ8aOHVtv25/+9KeS/DfVVVZW1lpXWlqqM844IxjYhw4dqt69e6usrCys4zXHWjL2wswyJD3vnDu3gXVvOufGh8xfLGmAc+5PdcpNl/9qsnr27HnynDlzWlv3Njl48KDS09NjcuzOjHaNb/RPdNCu8Y8+ig6vt2u3bt00dOjQdjteVVVVrSENbbF+/Xp997vf1aBBg5SUlBS8GlxWVqbHH39cffr0CZZ99dVXaz3R4dChQ/rmN7+p22+/vcFHr0nSVVddpU2bNik5OTl4VXjr1q3685//3Owwh6efflrl5eW1xh9//vnnuvnmm7V/v/9JHj6fTxdddJH+/d//vcF9rFu3TqWlpbWWFRYWrnDO5TdUvqWB+FpJW5xzcxtY11Ag7u+c+3Nj+8vPz3d1H63RXoqKilRQUBCTY3dmtGt8o3+ig3aNf/RRdHi9XdesWaMRI0Y0XzBCaoZGRFOkQvfGjRs1aNCgWst27dqltLQ0de3atcltq6urw77S3JiG+sbMGg3EYQ+ZCNw0d76ksxsp4sws2TlX85C7YZJKwt0/AAAAYitSV6DrhmFJ9cYbN6atYbg1WnLESyQtCNxkJzMbbWY3h6yfJ+nKwLpM+YPz65GqKAAAABANLbmp7qeSLgqZ7yEpL2R+pqSHzOxdSVWSruMJEwAAAIh3YQdi59zkOvPLJC0Lma+QdEXkqgYAAABEX/sP0gAAAEDEVVdXB3/UYt++fdqxY0eMa9RxEIgBAAA6qO985zvB6TfffFMzZvh/O23p0qV67rnnapXdtm1bcPpb3/qWJGnixIntUMv4RyAGAADooI4eParCwkJNnDhR11xzjZKTkyVJqampSkpKqlX2qquuCk7XPD+45sc2vK41v1QHAACAOJCRkaEXX3xRZqalS5dq8eLFuvXWW7V582Z9/etfD5bbv3+/KiqOPeugJhC35PcoOjMCMQAAQFu9epO0PcI/v9BrtHTu3U0WKS0tVWFhoRISErRnzx5ddtll+tWvfqVFixZpw4YNwXJLly7V+vXrg/M1z/qt+RU5ryMQAwAAdFBvvPFGg8u7d++uAQMGBOf/8pe/aMqUKVq5cqXGjh2rI0eOqKCgQIcOHWqvqsY1AjEAAEBbNXMlN5peffVVvffee0pMTJTP51N1dbVKSkr07W9/W5I0c+ZMTZo0SRdccIF++tOfauHChcrKytLChQuDZbyOQAwAANCBrV69Wm+99VZw+EN1dbW+/PJLfetb39KuXbu0Zs0azZw5U2amK6+8UmvXrmXscB0EYgAAgA5sw4YNeuSRR5SXl9fg+gcffDA4feGFF0qSqqqq2qNqHQaBGAAAoAOrrq7W5ZdfroyMDJmZKisrVVFRoUmTJun6669vcJs777xTklRZWdmeVY1bBGIAAIAO7KGHHqq3rLq6uslthgwZIkmaN29eVOrU0RCIAQAAOpmax6o1Jy0tLco16Rj4pToAAAB4GoEYAAAAnkYgBgAAgKcRiAEAAOBpBGIAAAB4GoEYAACgE7n88st14MABTZw4sd66ysrK4I9yvPDCC5o9e3Z7Vy8u8dg1AACADurnP/+51qxZIzNTjx499NxzzykhIUEpKSnKzMzU9u3bddlllykpKUnOOZWWlmrKlCn67W9/q+TkZCUmEgUlAjEAAECHtW7dOi1evFiSNHXq1OByM1NycrJ69eqlRYsWBZc/88wzSkhIkCQlJSUFp72OQAwAANBG//P+/+iTvZ9EdJ8n9DhBN556Y5NlQq/w7t27V5MnT9bHH38sM2vw6u+zzz6rp59+WpI/NIf7Ax6dHa0AAADQQYUG2vT0dL3++us699xzJfkDb6h58+Zp/PjxyszMDK6vW8aruEIMAADQRs1dyY2W8vLyWtM1Idc5V6vc3LlztWzZMt1///3BZVVVVfXKeRVXiAEAADqoIUOGqLCwUGeddVbwaq+ZqaysTM45rV69WlOnTtWmTZt0//3317oiXF1drerq6lhVPa5whRgAAKCDmjlzZr1lZqaKigqVl5crLy9PDz74oHJzc+uVq6ioIBAHEIgBAAA6kSuuuEKpqak6fPiw0tLSlJaW1mC50OEWXkcgBgAA6EROOeUUSdLChQubLDdt2rT2qE6HwBhiAAAAeBqBGAAAAJ5GIAYAAICnEYgBAADgaQRiAAAAeBqBGAAAwMM+/fTTRtd55TnFBGIAAIBOorKyUlVVVZKkF154QbNnz661/sILLwxOn3feeZKka665JrjMOSfnnObMmaNJkyapsLDQE88r9uRziL8sPaK9R6v1ZemRsLfplpqktOSO21yVVdXa8tURDcrp2qb9VFc77ThwtNH1LW3XBDMdl9mlTXVqjHNO2/c3Xlcvamn/IDy0a/zrSH2UlOBTTnpKWGUPlVVq/9GKKNeocR2pXSOpe1qyuiQlxLoakqTt27frsssuU1JSkpxzKi0t1ZQpU/Tb3/5WycnJSkw8ll0+/fRTlZWVBedTU1Nr/VeS/vrXv+rJJ59USkqK1q1bp169eul73/ueDh06pKeeekq9evVqvzfXjjpuwmuDb969RNVOUtGSsLfxmTSsZ4bGDcjS2P5ZGjegu4bmpsvns+Y3joEd+4/qw81f6cMt+/Th5n0q2VqqKue06r/PUXJi678YOFReqdPuaqbdWtCuvTK76N2bJ7a6Pk2pdmq+rl7Ugv5BC9Cu8a+D9NG4AVmaf/U3wyr7wvIt+u+XV0e5Rs3oIO0aSY9clq9JI3vGuhqSpF69emnRokXB+WeeeUYJCf6wnpSUFJyWpFmzZmnVqlU6dOiQunbtKp+vfh647LLLNHnyZD399NMaNWqU9u3bpwkTJujMM89UdnZ29N9QjHgyEN910Wh98slaDR8+PKzyTtKXpUe1css+LSz+Us++v0WSlJ6SqCG5XeMuFO8oPaovSv1XRpMSTCP7dNO/ndJf4wZkycm1ad8piQm6+6LRja5fuzb8dpWk1OTofcI2qcm6elFL+wfhoV3jX0fqo+wwrw5L0ulDc2L6d64jtWskjeidUW/Z9jvvVNmaTyJ6nJQRJ6jXzTe3aJtnn31WTz/9tCTJzIKh95///KdKSko0e/Zs/eIXv9Cjjz6qzz77TGeddVatq8jPPfecPvjgA02bNk3jxo2TJL377rt6/PHHdfHFF2v06M7576onA/G/nTJARYc2qODUAS3etrraaeOeQ/pw8z6t3PKVPt9zOAo1bJt+eT10Rf8sjR2QpZG9MyP6tU5yok/Tmmi3osOta9do8Pmsybp6UTz1T2dCu8a/ztpHw3pmaFjP+uGsvXTWdu2o5s2bp/HjxyszM1OSPxCbmTZv3qwbb7xRCxYs0HHHHaedO3dqyZIl6t+/v/72t78FxxK//vrreuihh5SamqqPP/5YVVVVwVBdVVWlJUuW6I477tAZZ5wRy7cZFZ4MxG3h85mG5KZrSG66Ljm5X6yrAwAA4kBLr+RG2ty5c7Vs2TLdf//9wWVVVVVyzmnAgAF68803VVlZqVmzZmnZsmX66quvdPToUT366KOaMWOGJGnSpEk655xzgtvffffdGjt2rKZMmSLp2A13nRFPmQAAAOigVq9eralTp2rTpk26//77ZXZsGGd1dXXwsWk+n0833XSTEhMTNWvWLL3yyit68cUXVV5erieeeCJY5siRI/rJT36iSZMmadGiRZoxY4bOOOMM3XrrrbWGYHQ2XCEGAADooPLy8vTggw8qNze33rqKiopazxHevXu3Bg0aFLw5Ljc3V6eccor+93//N1jmrbfeks/nq7VMkgoKCrRv3z5lZWVF6Z3EFoEYAACgg0pLS1NaWlqD6+o+P/juu+/WDTfcoFtvvVVJSUmqqKhQenq6HnjggWCZCRMm6KWXXtI555yj5ORkVVT4H+t33nnnddowLBGIAQAAOqVp06bVmu/Tp4+eeuqpJrdJSUmpFZC9onMOBAEAAADCRCAGAACApxGIAQAAWqmzPoasI2tNnxCIAQAAWqFLly7as2cPoTiOOOe0Z88edenSpUXbcVMdAABAK/Tr109bt27Vrl272uV4R48ebXHQ86IuXbqoX7+W/XgagRgAAKAVkpKSNGjQoHY7XlFRkcaNG9dux/MShkwAAADA0wjEAAAA8DQCMQAAADzNYnlnpJntkvR5jA6fI2l3jI7dmdGu8Y3+iQ7aNf7RR9FBu7Yv2rttBjrnchtaEdNAHEtmttw5lx/renQ2tGt8o3+ig3aNf/RRdNCu7Yv2jh6GTAAAAMDTCMQAAADwNC8H4tmxrkAnRbvGN/onOmjX+EcfRQft2r5o7yjx7BhiAAAAQPL2FWIAAACAQAwAAABvi1kgNrPTzexlM1tqZm+b2TmB5cPNbJmZvW9mL5pZep3tfGb2qpmdUGf5L8zsrcC2t5tZs+/NzPqb2Zt1lvUzsyfNbImZvWdm1zWzj65m9lygvm+Z2cgGyvzGzK5qrj6R0FnaNWS775vZ3XWWrTWzopDXj8LZV7xoTR+Z2Y/NbHHg/S4ys8Eh6843s+WB1x/COP50M1thZh+Y2c8aWF+v/xrZT4KZzTKzfwVeExooU6//oqWztGtI+dPMbE6dZcvqnPvXh7u/eBCvfWRmWWb2YKBe75jZvWZmTeyHc7/28SPSriHbxfW5H8ftPS1Qr8WBehU0s5+4Oo9jzjkXk5ekCZIyAtPdJRUHpl+TNDgw/V1J/zdkm/MkrZR0UNIJIcvPkPS0JF9g/reSrmrm+A9K+lTSu3WWj5A0KDDtC9RnZBP7+R9JFwWmB0l6PWRdX0nvStrVXH1o13r7SZD0tqQvJN1dZ927TdUh3l+t7KNzJCUEpk+X9HxgOkfSUkldAvN/knReE8ceKWm+JAu8XpA0qrn+a2Rf/yHpV4HpHpL+ISmpuf6jXcN6L/MkbZI0h3M/+n0k/9/qk0LKzpb0Lc799m3XjnLux3F7nxlyjOMkvdfM+4ir8zjWr5hXINDwJuk9SaMlza6z7q0Gyj+h2sHtOkkXh8wnSFoS5rGb/J9M0iOSxjax/p068w9LGl1n2Y/VToG4s7RrSLmCuv8zxtMfxvbuo8DyvpIWBKZ/LukHIet6SXqxiePdK+n0kPlvSLq3pf0XKLNMUkrI/E2SvtNc/9GuYb+HPMVxKOisfRRYd4ukC5rYF+d+FNo1pFyHOffjtb3lD93PNFP3uD2PY/GK+Rhi838Ff4+kxyQNlbS2TpG9Zta9md0US5pqZolmliTp1/J/impr3b4nKdk5t7KR9d1V/ycUP5P/fcRUR27XMOSY2WNm9oaZzTWzvLbWKRZa00dm1k3S/yfptsCiWts557ZL6tnEYesepy3na4JzrixC+4qYTtCuTUkxswcCX7kuNLOTonCMqIvnPgp8bXyapL83sS/O/WMi2a5NibtzP97a28xSA0MyVsp/5fjBZt5CXJ7HsRLTQGxmx8n/lfw/nHOz5f+k5Roo2tCyYyudWyT/0IQl8n+VUCL/15OtrVeSmd0l6QRJlzdVtJG6NVnfaOsE7dqc30u63jk3Uf4/Rn9pw75iojV9ZGYny38V/wbn3Ac1ixvYrql+bWn5lupw534Hadcad0j6H+fcZEm/lPRkOGMy40m89pH5/UrSxZIucc5VNv9uwj521HXidq0RV+d+PLa3c+6Ic67AOTdWUr6k+8wsJ7x3FNaxO7XEWB04MKD8AUlXOue2BhZvkDS5TtFs59y+5vbnnPuT/GNvZGYnStpn/hvcZoUUO9c5d6SZeqVImiPpT865f4Qs/4Gk6YHZtc65K51zewP/U4QaJun15uobLZ2hXcOo019Dpt81s9TmtoknrekjM/uupO9I+r5z7mhImQ3yn3MfBMr1lrTDzHrIPxavxuXOuY0h5d8NLB8maV0z9T1L0u8Cs6XOue8Gpp2ZJTvnykP2VdLkm4+iTtSujXLOvRgy/ZmZbZN/DOPe5raNB3HeR49L+rtz7o8h9eXcb992bVQ8nftx3t6SJOfcl2b2vqQBZjZGHeA8jrlYjdWQ9P9L6tnA8iWSBgamp8j/ibBumScUMtY1sGxo4L9d5R/Ynh9mPere/PULNTGgvYHt71NgzI3844JeaaDMj9V+N9V1inYN2a5A9ccQh46fOlOBmxM6yqulfSQpSdLfFLi5sc42veS/ISM5MH+3/B9QGjv21+T/Kq3mhoyn1cDNjXX7r5F9XSvp54HpTElvKHBDRlP9R7uG/X7yVH8c5Wkh0yMkFcX6fO4MfSTpfEk/a8H74NyPQruG7DOuz/04bu8LFRgTLKm3/KG5S0c5j2P9itkVYvkHfD9X5xuPKyRdI+mpwFch2yT9JMz9/beZDZCUJmmGc255K+s1XtIlVvuRLjOdc3MbKf87SY+Z2W8klUlq9gpnlHWWdm3Kv5nZrfL/kflC/jtlO5KW9tHxksZKWhKyzUHn3Ledc9vN7CFJb5lZhaRFzrlXGzuwc+4DM/unpPclVUl63Dm3upXvY6akh8zs3cC+rnPOVbRyX5HQWdq1KQVmdpv8w932S/r3KBwjmuK1j8bL37bfC9lkrnNuZiO749wPiHC7NiWezv14be90SUvNrFLSEfkvxB1tZFdS/J3HMcVPNwMAAMDTYv6UCQAAACCWCMQAAADwNAIxAAAAPI1ADAAAAE8jEAMAAMDTCMQAAADwNAIxAAAAPI1ADAAAAE/7f1C9k88RVO8DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_col = ['82년생 김지영', '벌새', '메기', '기생충']\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(movie_pivot[target_col])\n",
    "plt.legend(target_col, loc='best')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "우와, 벌새가 평점을 엄청 높게 받았네요! 심지어 기생충보다 더 높아요ㅎㅎ 메기 평점도 높은 편인 것 같구요. 82년생 김지영을 다른 그래프들과 비교해보니 평점 테러를 당한게 확실히 느껴지네요ㅎㅎ \n",
    "\n",
    "### 공부를 끝내며 \n",
    "\n",
    "이렇게 우리는 이번주에 웹 크롤링을 해보았습니다. 이번 주는 정말 재미있었죠? 웹의 정보들을 가져올 수 있는 무척 재미있는 시간이었던 것 같네요. 하지만, 웹 크롤링은 직접 해보면 아마 잘 안될 가능성이 높아요. BeautifulSoup과 urllib을 사용해서 가져오는게 만능은 아닌데다, 이거를 가져오는게 생각보다 쉽지가 않거든요..ㅎㅎ 그럼 복습 잘 해보시고, 다른 것들도 가져와서 분석해보면 더욱 더 재미있겠죠?🐷 \n",
    "\n",
    "오늘이 중간고사 이전까지의 범위에요. 남은 2주간 중간고사들 잘 보시고, 스스로 해보는 나만의 프로젝트까지 완성해서 5/10까지 제출해주시면 됩니다! 너무 많은 내용을 담으려고 하지 마세요. 적당한 범위를 해서 할 수 있는 만큼만 담으시면 좋겠습니다. 거창하지 않아도 괜찮습니다. 할 수 있는 부분만 확실하게 한정해서 해야 내용이 산으로 가지 않아요~ 그럼 우리 2주 후에 만나요!🥰"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}