Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23.4\n",
      "1.14.5\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import plotly\n",
    "import seaborn as sns\n",
    "import folium\n",
    "import json\n",
    "\n",
    "\n",
    "print(pd.__version__)\n",
    "print(np.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_sample = pd.read_csv(\"macngrams_finaldata_181005.csv\", encoding = 'utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>id</th>\n",
       "      <th>tv</th>\n",
       "      <th>title</th>\n",
       "      <th>epi</th>\n",
       "      <th>dates</th>\n",
       "      <th>links</th>\n",
       "      <th>synop</th>\n",
       "      <th>vod_key</th>\n",
       "      <th>image</th>\n",
       "      <th>class</th>\n",
       "      <th>awards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>SBS</td>\n",
       "      <td>그것이 알고싶다</td>\n",
       "      <td>되살아난 연쇄살인의 공포 - 부녀자 연쇄 피습 사건의 진실</td>\n",
       "      <td>2004-07-03</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/unansweredqu...</td>\n",
       "      <td>제 목  되살아난 연쇄살인의 공포  부녀자 연쇄 피습 사건의 진실  방송일시  2...</td>\n",
       "      <td>'범죄', '흉기'</td>\n",
       "      <td>//img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR4...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>SBS</td>\n",
       "      <td>그것이 알고싶다</td>\n",
       "      <td>2004 행복 신드롬 - 당신은 웰빙하고 계십니까</td>\n",
       "      <td>2004-07-10</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/unansweredqu...</td>\n",
       "      <td>2004 행복신드롬   당신은 웰빙하고 계십니까   웰빙은 즐거운 변화   늘어...</td>\n",
       "      <td>'웰빙', '관심', '건강', '사회'</td>\n",
       "      <td>//img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR5...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>SBS</td>\n",
       "      <td>그것이 알고싶다</td>\n",
       "      <td>500회 특집 노블레스 오블리주, 가진 자의 의무</td>\n",
       "      <td>2004-07-17</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/unansweredqu...</td>\n",
       "      <td>지금 우리는 국민소득 2만 달러 달성과 선진국 진입의 중요한 기로에 서 있다. 자원...</td>\n",
       "      <td>'공개', '사회', '통합', '지도층', '상속', '오블리주', '미국', '...</td>\n",
       "      <td>//img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR4...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>SBS</td>\n",
       "      <td>그것이 알고싶다</td>\n",
       "      <td>살인마는 바로 곁에 있었다</td>\n",
       "      <td>2004-07-24</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/unansweredqu...</td>\n",
       "      <td>21명 연쇄살인 피의자  유영철의 진실은 무엇인가  무고한 시민 21명을 살해한 ...</td>\n",
       "      <td>'연쇄살인', '유영철', '부유층', '상대', '여성', '연쇄살인범', '특성'</td>\n",
       "      <td>//img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR7...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>SBS</td>\n",
       "      <td>그것이 알고싶다</td>\n",
       "      <td>외면당한 어린 모성 - 10대 미혼모, 아직도 버려져야 하는가</td>\n",
       "      <td>2004-07-31</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/unansweredqu...</td>\n",
       "      <td>누구도 축복해 주지 않는 출산의 공포 임신이라는 말에 연락을 끊어버린 남자친구  ...</td>\n",
       "      <td>'출산', '임신', '아이', '엄마', '성교육', '책임', '10대', '미혼모'</td>\n",
       "      <td>//img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR3...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   tv     title                                 epi       dates  \\\n",
       "0   1  SBS  그것이 알고싶다    되살아난 연쇄살인의 공포 - 부녀자 연쇄 피습 사건의 진실  2004-07-03   \n",
       "1   2  SBS  그것이 알고싶다         2004 행복 신드롬 - 당신은 웰빙하고 계십니까  2004-07-10   \n",
       "2   3  SBS  그것이 알고싶다         500회 특집 노블레스 오블리주, 가진 자의 의무  2004-07-17   \n",
       "3   4  SBS  그것이 알고싶다                      살인마는 바로 곁에 있었다  2004-07-24   \n",
       "4   5  SBS  그것이 알고싶다  외면당한 어린 모성 - 10대 미혼모, 아직도 버려져야 하는가  2004-07-31   \n",
       "\n",
       "                                               links  \\\n",
       "0  http://programs.sbs.co.kr/culture/unansweredqu...   \n",
       "1  http://programs.sbs.co.kr/culture/unansweredqu...   \n",
       "2  http://programs.sbs.co.kr/culture/unansweredqu...   \n",
       "3  http://programs.sbs.co.kr/culture/unansweredqu...   \n",
       "4  http://programs.sbs.co.kr/culture/unansweredqu...   \n",
       "\n",
       "                                               synop  \\\n",
       "0   제 목  되살아난 연쇄살인의 공포  부녀자 연쇄 피습 사건의 진실  방송일시  2...   \n",
       "1    2004 행복신드롬   당신은 웰빙하고 계십니까   웰빙은 즐거운 변화   늘어...   \n",
       "2  지금 우리는 국민소득 2만 달러 달성과 선진국 진입의 중요한 기로에 서 있다. 자원...   \n",
       "3   21명 연쇄살인 피의자  유영철의 진실은 무엇인가  무고한 시민 21명을 살해한 ...   \n",
       "4   누구도 축복해 주지 않는 출산의 공포 임신이라는 말에 연락을 끊어버린 남자친구  ...   \n",
       "\n",
       "                                             vod_key  \\\n",
       "0                                         '범죄', '흉기'   \n",
       "1                             '웰빙', '관심', '건강', '사회'   \n",
       "2  '공개', '사회', '통합', '지도층', '상속', '오블리주', '미국', '...   \n",
       "3    '연쇄살인', '유영철', '부유층', '상대', '여성', '연쇄살인범', '특성'   \n",
       "4  '출산', '임신', '아이', '엄마', '성교육', '책임', '10대', '미혼모'   \n",
       "\n",
       "                                               image class  awards  \n",
       "0  //img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR4...    기타       0  \n",
       "1  //img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR5...    기타       0  \n",
       "2  //img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR4...    기타       0  \n",
       "3  //img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR7...    기타       0  \n",
       "4  //img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR3...    기타       0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' 4차 산업혁명 시대 대학 교육은 어떻게 변화하고 어떤 역할을 수행해야 할 것인가  미네르바 대학  P테크  난양공대  카이스트 등 변화를 준비하며 인재들을 키우고 있는 대학들을 살펴본다.     n인공지능 AI   빅데이터 등 IT기반 과학기술이 급속도로 발전하면서 4차 산업혁명시대가 예고되었다.   n세계 각국은 물론 글로벌 기업들은 4차 산업혁명 시대를 준비하기 위해 발 빠르게 움직이고 있는데    n4차 산업혁명 준비로 빠지지 않는 것이 바로 교육개혁. 교육선진국은 미래 인재 양성이라는 목표를 가지고 교육과정개편은 물론 과학기술을 주도할 수 있는 코딩 교육 등 다양한 교육개혁들이 추진하고 있다. 이에  4차 산업혁명시대  교육대혁명 에서는 4차 산업혁명이 가져올 우리의 미래 삶을 예측해보고 10년 후 20년 후 미래 인간의 역할을 살펴보고자 한다. 인공지능과 공존하며 살아가야 할 우리 인간들이 가져야 할 역량은 무엇인지  과학기술발전보다 앞서가야 할 교육은 어떻게 변해야 할지 EBS 다큐프라임  4차 산업혁명 시대  교육대혁명 3부작에서 그 해답을 제시한다.  n  xa0  n      n제2부. 9월 19일 화  밤 9시 50분  xa0  xa0  n  xa0  n평가의 틀을 깨라  n  ufeff  xa0  n시험성적으로 등수를 매기고 기준의 잣대를 세우는 한국사회의 교육개혁이 필요하다 어려서부터 시험이라는 입시제도의 틀 안에 갇혀 정답만을 골라내는 것은 굉장히 심각한 문제가 아닐 수 없다. 더욱 더 나은 창의력 있는 미래인재를 길러주기 위한 해답은 문제의 정답을 맞힌 개수가 아니라 최종적 평가 관문인 시험문제를 바꾸는 것이 핵심  그 과정을 함께 들여다본다.  n  xa0  n기존의 지식교육이라고 하면 선생님의 일방적인 가르침  평가를 위해 치루는 시험일 것이다. 4차 산업혁명 시대에서는 수업방식이나 교육제도를 변화시키려는 노력이 필요하다. 이에 새로운 교육제도인 IB 교육을 도입  실행하면서 선생님의 일방적인 수업이 아닌 교사와 학생이 함께 소통하는 형식으로 학생 중심의 활동이 이뤄지는 쓰쿠바 대학을 만나본다. 한국사회의 교육 역시 변화에 노력을 조금씩 기울이고 있는데.... 수업의 시작이 학급회의가 아닌 몸의 긴장을 풀기 위한 5차원 건강 체조이자 자기 경영서를 학습하는 일  저학년부터 고학년까지 토론을 통해 의견을 나누는 방식 등 점차 성장해나가는 중이다. 미래인재 양성과 교육의 변화에 있어 학생은 수업을 통해 배운 원리를 이해하고  어떻게 적용해야 하는지 알며  교육자는 학생이 수업에 대한 전체적인 이해도와 자기표현을 잘 하는지에 대해 살펴보는 형태가 곧 교육개혁이 되고 미래인재를 키우는 셈이 되는 것이다. 학생과 선생님이 함께 탐구하는 탐구형 수업  학생 혼자서 지식을 습득하는 방법  학생과 학생 간에 토의를 통한 자율생산적인 학습방법을 거쳐 교육혁신이 가능해지는 것이 바로 학교와 학생 모두 win win 할 수 있는 방법   xa0   n  xa0  n우리는 지식과 정보를 수용하는 것에 그치지 않고  학문적인 부분을 바탕으로 자신의 가능성을 고려하며  상대를 받아들이는 자세를 갖춰야 한다. 타인과 상호작용하며 상대방의 입장과 관점을 배우고 깨달음의 과정을 얻는 것이 교육개혁이자 미래 인재교육의 기본 덕목임을 잊지 않아야 할 것이다.  xa0  xa0  xa0  xa0  xa0  xa0  xa0변해가는 시대의 흐름에 따라 교육 또한 변화되어야 하는 4차 산업혁명 시대  교육의 질적인 향상만을 높이기보다  스스로 문제를 발견하고 해결하는 능력을 키워주는 것이 우리의 역할임을 기억하며   시험 이라는 국한된 교육제도를 벗어나 새로운 변화를 꿈꿔보고자 한다.  n  '"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_sample[final_sample.epi ==\"<4차 산업혁명시대 교육대혁명> 3부 대학, 변해야 산다\"].synop.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from dateutil.relativedelta import relativedelta\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "\n",
    "final_sample['dates'] = final_sample['dates'].apply(lambda x: datetime.strptime(x, '%Y.%m.%d'))\n",
    "final_sample.sort_values(by='dates', inplace=True, ascending=False)\n",
    "final_sample = final_sample.reset_index(drop=True)\n",
    "\n",
    "# final_sample['dates'] = datetime.datetime.strptime(final_sample['dates'], \"%Y.%m.%d\").strftime(\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MBC 스페셜', 'SBS 스페셜', 'KBS 스페셜', '다큐프라임'], dtype=object)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_sample['title'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tv</th>\n",
       "      <th>title</th>\n",
       "      <th>epi</th>\n",
       "      <th>dates</th>\n",
       "      <th>links</th>\n",
       "      <th>synop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MBC</td>\n",
       "      <td>MBC 스페셜</td>\n",
       "      <td>생존 체력, 약골 40대 저질체력 탈출기</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>“마흔 살, ‘생존 체력’이 필요하다”  \\r\\r\\r\\r\\r\\r\\n  ‘불혹’의 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS 스페셜</td>\n",
       "      <td>복흥 O.B.A 소녀들은 자란다</td>\n",
       "      <td>2018-09-16</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>복흥 O.B.A소녀들은 자란다# 사춘기 자녀를 둔 부모들에게아이를 키우는 부모라면 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KBS</td>\n",
       "      <td>KBS 스페셜</td>\n",
       "      <td>남북은 없다 - 여우와 두루미</td>\n",
       "      <td>2018-09-13</td>\n",
       "      <td>http://program.kbs.co.kr/1tv/culture/kspecial/...</td>\n",
       "      <td>[남북은 없다 여우와 두루미] 소백산맥의 여우가 개성까지 달리고, 두루미가 북한 안...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EBS</td>\n",
       "      <td>다큐프라임</td>\n",
       "      <td>비밀의 땅 파미르  - 2부 비밀의 땅, 숨겨진 강</td>\n",
       "      <td>2018-09-11</td>\n",
       "      <td>http://www.ebs.co.kr/tv/show?courseId=BP0PAPB0...</td>\n",
       "      <td>\"아시아의 고산지대인 파미르, 텐샨,히말라야 의 자연과 사람들의 삶을 알아본다. 중...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EBS</td>\n",
       "      <td>다큐프라임</td>\n",
       "      <td>비밀의 땅 파미르 - 1부 세계의 지붕</td>\n",
       "      <td>2018-09-10</td>\n",
       "      <td>http://www.ebs.co.kr/tv/show?courseId=BP0PAPB0...</td>\n",
       "      <td>\"세계의 지붕 파미르에 서식하는 다양한 야생동물들의 생태소개 1부에서는 파미르에 서...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tv    title                           epi      dates  \\\n",
       "0  MBC  MBC 스페셜        생존 체력, 약골 40대 저질체력 탈출기 2018-10-01   \n",
       "1  SBS  SBS 스페셜             복흥 O.B.A 소녀들은 자란다 2018-09-16   \n",
       "2  KBS  KBS 스페셜              남북은 없다 - 여우와 두루미 2018-09-13   \n",
       "3  EBS    다큐프라임  비밀의 땅 파미르  - 2부 비밀의 땅, 숨겨진 강 2018-09-11   \n",
       "4  EBS    다큐프라임         비밀의 땅 파미르 - 1부 세계의 지붕 2018-09-10   \n",
       "\n",
       "                                               links  \\\n",
       "0  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "1  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "2  http://program.kbs.co.kr/1tv/culture/kspecial/...   \n",
       "3  http://www.ebs.co.kr/tv/show?courseId=BP0PAPB0...   \n",
       "4  http://www.ebs.co.kr/tv/show?courseId=BP0PAPB0...   \n",
       "\n",
       "                                               synop  \n",
       "0   “마흔 살, ‘생존 체력’이 필요하다”  \\r\\r\\r\\r\\r\\r\\n  ‘불혹’의 ...  \n",
       "1  복흥 O.B.A소녀들은 자란다# 사춘기 자녀를 둔 부모들에게아이를 키우는 부모라면 ...  \n",
       "2  [남북은 없다 여우와 두루미] 소백산맥의 여우가 개성까지 달리고, 두루미가 북한 안...  \n",
       "3  \"아시아의 고산지대인 파미르, 텐샨,히말라야 의 자연과 사람들의 삶을 알아본다. 중...  \n",
       "4  \"세계의 지붕 파미르에 서식하는 다양한 야생동물들의 생태소개 1부에서는 파미르에 서...  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_sample.to_csv(\"final_sample.csv\", mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import json\n",
    "\n",
    "final_sample.to_csv(\"final_sample.csv\", mode='w')\n",
    "data = []\n",
    "with open('final_sample.csv', encoding = 'utf-8') as f:\n",
    "    for row in csv.DictReader(f):\n",
    "        data.append(row)\n",
    "\n",
    "json_data = json.dumps(data, ensure_ascii=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"\": \"0\", \"tv\": \"MBC\", \"title\": \"MBC 스페셜\", \"epi\": \"생존 체력, 약골 40대 저질체력 탈출기\", \"dates\": \"2018-10-01\", \"links\": \"http://www.imbc.com/broad/tv/culture/mbcspecial/vod/index.html\", \"synop\": \" “마흔 살, ‘생존 체력’이 필요하다”  \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  ‘불혹’의 나이이자 ‘생애 전환기’. 신체적, 정신적으로 삶이 달라지는 터닝포인트 40\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n세. 정신없는 일상에 치이다 보면 어느새 체력이 방전되고 나이를 실감하게 된다. 그\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n동안 너무 바빠서, 혹은 체력의 중요성을 깨닫지 못해 운동을 외면하고 살아온 40대 \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n중년들과 운동으로 삶을 구원받은 트레이너와의 운명적 만남! 최소한의 시간, 오로\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n지 맨몸으로 기초 체력을 키우는  운동을 배워 본다. 40대들의 저질 체력 극복 프로\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n젝트. “생존 체력 - 약골 40대 저질체력 탈출기” \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n ■ 약골 40대, 운동이 답이다!  \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  코미디 쇼의 최강자로 전성기를 누비던 강성범씨는 어느새 침대 위에서 꼼짝도 않\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n는 영락없는 아재다. 40대 중반이 되면서 체력의 중요성을 모르는 것은 아니지만 몸\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n을 움직이기는 생각보다 어렵다. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  육아 입문 5년 차, 퇴근 후 직접 두 딸과 놀아주는 딸바보 임청묵씨는 어느 날부터\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n인가 딸들이 깨우지 못하면 일어날 수 없을 만큼 체력이 떨어졌다. 딸들에게 자전거\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n를 태워 줄 때에도 한 번 이상은 무리인 그의 나이는 올해 마흔. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  원래는 진짜 (체력이) 좋았어요. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  몸에 근력도 많은 편이고 해서 (체력이) 좋은 편이었는데 \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  40이 들어가면서부터 계속 안 좋다고 하더라고요.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  - ‘생존 체력 운동’ 도전자 임청묵씨 아내 인터뷰 中-  \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n운동을 하는 게 정신에는 더 해가 될 거 같은 느낌. 강박관념, 압박감. 운동하러 갈 \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n때에 걱정. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n‘아 오늘 쉬고 싶은데...’ 이런 것들을 이겨내고 가야 된다는 게 싫은 거예요. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n- ‘생존 체력 운동’ 도전자 강성범 인터뷰 中-\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  프리랜서 통역 전문가이자 대학 강사인 서지연씨는 자타가 공인하는 체력의 아이\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n콘이었다. 하루 네 시간의 출퇴근도 너끈히 소화하던 커리어 우먼. 이제는 집에 돌아\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n오면 소파와 일체가 되어 버리는 그녀도 내년이면 마흔이다. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  학창시절부터 체육시간이 두려웠던 김보라씨는 모태 저질 체력이다. 그녀의 인생\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n에서 등산 횟수는 단 한 번! 한 정거장 거리도 반드시 대중교통을 이용하는 귀차니스\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n트 그녀도 중년의 시간으로 서서히 접어들고 있다. 위기에 빠진 저질 체력 40대를 갱\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n생시키기 위한 긴급 처방인 ‘생존 체력 운동’! 과연 이 운동은 그들을 구원해 줄 수 있\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n을까. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n ■ 헬스클럽과 운동기구 없이! 일상을 버티는 힘, 생존 체력!  \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n     \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n 국민체력 테스트를 통해 측정한 그들의 현재 체력은 모두 ‘등급 외’ 판정!!   \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  저질체력인 4인방에게 ‘생존 체력 운동’ 전문가인 아주라 트레이너가 왔다. 지금 있\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n는 그곳에서 운동기구 없이 바로 운동을 시작할 수 있다. 필요한 건 단 하루 15분의 \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n시간! 하루 15분의 시간으로 그들의 체력은 변화할 수 있을까? \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  아기 엄마들한테는 아이를 잘 볼 수 있는 체력이 가장 먼저 필요하겠죠. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  무거운 무게를 들고 강하게 펀치를 하는 것보다 더 중요한 것은 \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  허리가 아프지 않게 아이를 안을 수 있는 체력. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  직장인이라면 회의에서 견디고 야근에서 버틸 수 있는 체력. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  수험생이라면 공부를 하기 위해\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  책상 위에서 허리가 아프지 않게 버틸 수 있는 체력. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  절실한 것들은 다를 거예요. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  그런 절실한 것들을 실행할 수 있는 가장 기본적인 밑바닥의 체력들을 \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  전 ‘생존 체력’이라고 부르고 싶어요. \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n  - ‘생존 체력 운동’ 전문가 아주라 인터뷰 中-  \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n 생존 체력 운동은 스퀏, 플랭크, 푸시업, 버피의 네 가지 동작으로 이루어진\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n다. 식스\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n팩, 애플 히프를 만들기 위한 것이 아닌 일상을 더 활기차게 살기 위해 최소한의 투\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n자(하루 15분)로 최적의 효과(체력 향상)를 내는 것이 운동의 핵심이다. 여느 헬스클\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n럽에서와 같이 정해진 운동 프로그램에 끼워 맞추지 않는 ‘생존 체력 운동’은 저마다 \\\\n\\\\n\\\\n\\\\n\\\\n\\\\n다른 일상에 자연스럽게 스며드는데... 한 달 후, 과연 그들의 삶은 어떻게 변했을까?\"}, {\"\": \"1\", \"tv\": \"SBS\", \"title\": \"SBS 스페셜\", \"epi\": \"복흥 O.B.A 소녀들은 자란다\", \"dates\": \"2018-09-16\", \"links\": \"http://programs.sbs.co.kr/culture/sbsspecial/vod/53591/22000296526\", \"synop\": \"복흥 O.B.A소녀들은 자란다# 사춘기 자녀를 둔 부모들에게아이를 키우는 부모라면 자녀와 말이 통하지 않아 답답해 한 적이 한 번 쯤은 있을 것이다. 자녀가 사춘기에 접어들었다면 어쩌면 아이와 이야기를 나누는 매 순간이 마치 도심 교통체증처럼 꽉 막혀버렸다고 느낀 적도 많을 것이다. 하지만 어느 순간 문득, 부모 앞에서 입을 닫아버린 내 아이가 어떤 생각과 어떤 고민을 하며 살고 있을까 하고 궁금해진다. 걱정도 되고 궁금하기도 하고... 그래서 또 다시 대화를 시도해 보지만 사춘기 아이는 도무지 속내를 털어놓을 생각이 없다. 내 아이는 도대체 무슨 생각을 하며 살고 있을까?여기, ‘질풍노도의 시기를 보내고 있는 내 아이가 과연 잘 자라고 있는 걸까?’ 라는 의문을 가진 부모들에게 전하고 싶은, 평범하지만 조금은 특별한 ‘소녀들의 이야기’가 있다.# 4인 4색! 14살 시골 소녀들을 더 들어가야 도착할 수 있는 복흥면, 이곳에 전교생 23명의 작은 중학교에 다니는 4명의 1학년 소녀들이 살고 있다.밝고 활달한 성격을 가진 ‘걸크러시’ 정현이, 말투도 행동도 느리지만 공부를 제일 잘 하는 ‘늘보’ 수아, 뭐든지 잘 하고 싶은 욕심이 강한 ‘승부욕 여신’ 수련이, 서울에서 전학 온 지 6개월 밖에 되지 않은 ‘아직은 서울소녀’ 아영이... 성격도 다르고 살아온 환경도 제각각이지만 서로를 ‘베스트프렌드’ 라고 말하며 항상 붙어 다니는 이 소녀들은 어떤 생각과 고민을 하며 살아가고 있을까?# 어른들은 모르는 10대 소녀들의 진짜 이야기썸인지 연애인지... 마음을 고백해온 중3 오빠와의 관계를 고민하고, 공부를 잘 하면 원하는 꿈을 이루고 행복해 질 수 있을지도 모른다고 생각하기도 한다.때론 엉뚱하고 때론 진지하게... 흘러가는 구름을 보며 삶과 행복을 고민하기도 하는...한번 터지면 누구도 막을 수 없는 웃음 폭탄을 장착한 엉뚱발랄 소녀 사총사가 들려주는, 어른들은 절대 알 수 없는 10대 소녀들만의 이야기# 어른들의 정답과 아이들이 생각하는 정답은 다를지도 모른다.어른들은 살아온 세월만큼 인생의 정답을 잘 안다고 생각할지도 모르겠다. 하지만 그것을 아이들에게 강요하는 순간 아이들은 맞지 않는 옷을 입은 것처럼 답답해할 것이다.그렇게 된다면 이 아이들은 과연 행복한 걸까?시간을 두고 기다려주면 아이들은 스스로 인생의 행복을 찾기 위해 노력할 거라고 믿는다.이번 주 일요일,  <복흥O.B.A 소녀들은 자란다>를 통해 아이들이 자신만의 정답을 찾아가는 과정을 따라가다 보면 어느새 내 아이를 어떻게 바라봐야할지 생각하게 될 것이다.\"}, {\"\": \"2\", \"tv\": \"KBS\", \"title\": \"KBS 스페셜\", \"epi\": \"남북은 없다 - 여우와 두루미\", \"dates\": \"2018-09-13\", \"links\": \"http://program.kbs.co.kr/1tv/culture/kspecial/pc/http://program.kbs.co.kr/1tv/culture/kspecial/pc/index.html?source=episode&sname=vod&stype=vod&program_code=T2016-0065&program_id=PS-2018086563-01-000&section_code=05&broadcast_complete_yn=N&local_station_code=00&site_id=5441\", \"synop\": \"[남북은 없다 여우와 두루미] 소백산맥의 여우가 개성까지 달리고, 두루미가 북한 안변을 지나 전남 순천까지 내려와 겨울을 난 까닭은 무엇일까? 한반도 생태 역사의 비밀이 펼쳐진다.\"}, {\"\": \"3\", \"tv\": \"EBS\", \"title\": \"다큐프라임\", \"epi\": \"비밀의 땅 파미르  - 2부 비밀의 땅, 숨겨진 강\", \"dates\": \"2018-09-11\", \"links\": \"http://www.ebs.co.kr/tv/show?courseId=BP0PAPB0000000005&stepId=01BP0PAPB0000000005&lectId=10225466\", \"synop\": \"\\\\\"아시아의 고산지대인 파미르, 텐샨,히말라야 의 자연과 사람들의 삶을 알아본다. 중앙아시아의 거대한 호수 ‘아랄해’, 지금은 거의 말라버려 20세기 최악의 환경재앙이라 불리는 곳입니다. 2부는 이 아랄해로 들어오는 두 물줄기 ‘아무다리아강’과 ‘시르다리아강’을 따라 그 발원지를 찾아가는 여정입니다.   <주요 에피소드> 아무다리아강의 발원지, 파미르고원 대륙빙하 중 최대 규모인 파미르의 페드첸코 빙하에서부터 녹아 흐르는 물은 판즈강을 거쳐 아무다리아 강이 된다. 타지키스탄과 아프가니스탄의 국경을 이루는 강인 판즈강을 따라 가면 쉽게 볼 수 없는 아프가니스탄 사람들의 모습을 볼 수 있다. 또 빙하부터 습지대까지 식생의 변화와, 환경의 변화를 살펴보고 강에 기대어 살아가는 사람들의 삶을 소개한다.   시르다리아강의 발원지는 텐샨산맥 파미르 고원에 비해 고도가 낮은 텐샨산맥은 아름다운 풍경을 자랑한다. 초록으로 뒤덮인 산과 색색의 꽃들은 화려한 텐샨을 만든다. 시르다리야강의 발원지인 텐샨의 빙하는 어떤 모습인지 소개한다. 자일로(여름 초목지)에 유르트를 짓고 양을 치는 키르기 유목민, 텐샨산맥의 야생동물을 만난다.  \\\\\"[\\'\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n단 한 번도 공개된 적 없는 비밀의 땅, 세계의 지붕 파미르 \\\\\\\\n\\\\\\\\n\\\\\\\\n거대산맥들이 만들어낸 해발 4,000m의 고지\\\\\\\\n세계 최초, 하나의 먹이를 놓고 벌이는 최상위 포식자들의 신경전\\\\\\\\n총 제작기간 2년, 끊임없는 추적과 오랜 잠복, 제작진의 집요함이 만들어낸 대작 \\\\\\\\n\\\\\\\\xa0\\\\\\\\n\\\\\\\\n파미르고원, 한 번쯤 들어본 적 있는 익숙한 이름이지만 국내에는 거의 소개된 적 없는 비밀의 땅이다. 파미르고원은 히말라야산맥을 비롯해 텐샨, 카라코람, 쿤룬, 힌두쿠시 산맥이 모이는 곳으로 평균 해발고도가 4000m에 달한다. 수많은 산악인들이 오르는 히말라야와 달리 누구의 손길도 닿지 않은 미지의 땅이다. 는 삭막하고 거친 땅에 발을 붙이고 살아가는 생명들을 소개한다. 자연다큐멘터리의 명가 EBS 답게 쉽게 볼 수 없는 야생동물들의 생생한 모습 그대로 담아냈다. 무인 카메라를 설치하여 촬영한 영상에서는 늑대무리의 위계질서, 먹이를 뜯는 포식자의 면모를 볼 수 있다. 특히, BBC, NHK에서도 담아내지 못한 ‘눈표범과 늑대가 만나는 장면‘은 이 녀석들을 찍기 위한 제작진의 집요함이 어떠했는지를 반증하는 진귀한 영상이다. 사냥한 ’마르코폴로양‘을 지키는 눈표범과 대가없이 이 전리품을 빼앗고 싶어 하는 늑대, 둘의 신경전을 포착하여 촬영했다. 이 외에도 아이벡스(야생 염소), 마르코폴로(산양), 수염 수리 등 파미르에 서식하는 다양한 야생동물의 생태를 밀도 있게 다큐에 담아냈다. \\\\\\\\n\\\\\\\\xa0\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n본방송 : 2014. 06. 16~18 밤 9시 50분\\\\\\\\n재방송 : 2014. 06. 22 밤 8시 15분 (3부작 연속 방영)\\\\\\\\n\\\\\\\\xa0\\\\\\\\n제작진\\\\\\\\n-제작진 기획 추덕담\\\\\\\\n-연출 서준\\\\\\\\n\\\\\\\\xa0\\\\\\\\n\\\\\\\\n기획의도\\\\\\\\xa0\\\\\\\\n\\\\\\\\n세계의 지붕이라 불리는 파미르 고원, 한번쯤 들어본 적 있는 익숙한 이름이지만 국내에는 거의 소개된 바가 없다. 파미르는 텐샨, 카라코람 산맥, 쿤룬 산맥 등 험준한 고산들이 만나는 지점에 형성된 고원지대이다. 그만큼 높고 험하다. 하지만 세계의 또 다른 지붕 히말라야와는 다른 매력을 가지고 있는 지역이기도 하다. 파미르 고원이 위치해 있는 타지키스탄은 아프가니스탄, 파키스탄, 키르기스스탄, 중국 등의 나라들과 국경을 접하고 있어 언제나 불안한 정세에 시달리며, 쉽게 접근을 허락하지 않는 곳이다. 수많은 산악인이 오르고 도전하는 히말라야와는 달리 파미르는 누구의 손길도 닿지 않은 미지의 땅과도 같은 곳이다. 평균 해발 4000미터 이상의 고산에 둘러싸여 있지만 주변은 한없이 넓은 평원이 펼쳐진다. 땅바닥에 바짝 붙어 자라는 풀을 제하고는 초록빛을 볼 수 없는, 그마저도 먼지에 뒤덮여 잿빛인 메마른 땅이다. \\\\\\\\n\\\\\\\\xa0\\\\\\\\n\\\\\\\\n고원의 중심부에는 전 세계 대륙 빙하 중 최대 규모인 ‘페드첸코’ 빙하가 있다. 페드첸코 외에도 수많은 빙하는 파미르의 지형 형성에 매우 주요한 역할을 했다. 이러한 빙하에서 흘러나오는 물은 여러 강줄기를 만들며 중앙아시아 국가들의 주요 수원이 된다. 하지만 최근 몇 년 사이 해빙 속도가 급속도로 빨라지며 예측 불가능한 기후변화를 야기하고 있다. 여름철 갑작스럽게 내리는 눈이나 봄철 누그러들지 않는 추위는 황량함 속에서 살아가는 유목민들의 삶을 더욱 고단하게 만든다. \\\\\\\\n\\\\\\\\xa0\\\\\\\\n\\\\\\\\n<비밀의 땅, 파미르>은 인간과 야생의 적대적인 공존관계, 인간이 만들어내는 환경 문제에 대한 메시지를 던지고 잘 알려지지 않은 비밀의 땅 파미르고원을 중심으로 산과 빙하에 기대어 살아가는 유목민들, 야생 동물들의 생생한 모습을 시청자들에게 전하고자 한다. 특히 파미르 고원 내에서도 쉬이 접근하기 어려운 고산 호수 지역과 야생 동물만이 흔적을 남기는 오지 지역을 다니며 아름다운 파미르의 비경을 카메라에 담는다.\\\\\\\\ufeff\\\\\\\\ufeff\\\\\\\\ufeff\\\\\\\\ufeff\\\\\\\\ufeff\\\\\\\\ufeff\\\\\\\\n\\', \\'\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n2부 -\\\\\\\\xa0비밀의 땅, 숨겨진 강\\\\\\\\xa0\\\\\\\\n\\\\\\\\xa0\\\\\\\\n중앙아시아의 거대한 호수 ‘아랄해’, 지금은 거의 말라버려 20세기 최악의 환경재앙이라 불리는 곳입니다. 2부는 이 아랄해로 들어오는 두 물줄기 ‘아무다리아강’과 ‘시르다리아강’을 따라 그 발원지를 찾아가는 여정입니다.\\\\\\\\n\\\\\\\\xa0\\\\\\\\n\\\\\\\\n<주요 에피소드>\\\\\\\\n아무다리아강의 발원지, 파미르고원\\\\\\\\n대륙빙하 중 최대 규모인 파미르의 페드첸코 빙하에서부터 녹아 흐르는 물은 판즈강을 거쳐 아무다리아 강이 된다. 타지키스탄과 아프가니스탄의 국경을 이루는 강인 판즈강을 따라 가면 쉽게 볼 수 없는 아프가니스탄 사람들의 모습을 볼 수 있다. 또 빙하부터 습지대까지 식생의 변화와, 환경의 변화를 살펴보고 강에 기대어 살아가는 사람들의 삶을 소개한다. \\\\\\\\n\\\\\\\\xa0\\\\\\\\n\\\\\\\\n시르다리아강의 발원지는 텐샨산맥 \\\\\\\\n파미르 고원에 비해 고도가 낮은 텐샨산맥은 아름다운 풍경을 자랑한다. 초록으로 뒤덮인 산과 색색의 꽃들은 화려한 텐샨을 만든다. 시르다리야강의 발원지인 텐샨의 빙하는 어떤 모습인지 소개한다. 자일로(여름 초목지)에 유르트를 짓고 양을 치는 키르기 유목민, 텐샨산맥의 야생동물을 만난다.\\\\\\\\xa0\\\\\\\\n\\', \\'\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n3부 -\\\\\\\\xa0고산의 사람들\\\\\\\\n\\\\\\\\xa0\\\\\\\\n파미르 고원은 크게 두 부분으로 나누어 볼 수 있습니다. 저지대와 고지대. 두 지역은 고도만 다른 것이 아니라 고도에 따른 식생, 사는 동물, 심지어 사는 사람들의 인종도 다릅니다. 3부에서는 저지대의 타직족 사람들의 삶과 고지대의 키르기족 사람들의 삶의 모습을 소개합니다. \\\\\\\\n\\\\\\\\xa0\\\\\\\\n\\\\\\\\n<주요 에피소드>\\\\\\\\n저지대의 사람들\\\\\\\\n파미르고원으로 향하는 길은 황량한 파미르의 고지대와 달리 꽃과 풀이 가득하다. 저지대의 계곡을 따라가면 마을을 형성하고 살아가는 사람들을 만날 수 있다. 이들은 타지키스탄어를 쓰는 타지크족이다. 저지대는 비교적 물이 풍부하여 과실나무가 많고 밀밭과 감자밭 등 농사를 지을 수도 있다. 이 곳 사람들의 주식은 빵으로 자신들이 농사지은 밀을 이용해 빵을 만들어 먹는다. 고지대에 비해 풍요롭고 풍부해 보이는 사람들의 삶도 겨울이 되면 고단하기는 마찬가지, 가축을 노리는 눈표범과 늑대의 위협으로부터 자유롭지 못하다. \\\\\\\\n\\\\\\\\xa0\\\\\\\\n\\\\\\\\n고지대의 사람들\\\\\\\\n물도, 전기도, 나무도 없는 파미르의 고지대. 그 무엇 하나 풍족하지 못한 이곳에서 사는 사람들의 삶은 팍팍하다. 여성들은 가축을 몰고 나간 남편들을 기다리는 것이 일, 남성들은 야생동물의 위협에서 가축을 지키며 하루 종일 산지를 돌아다니는 것이 일이다. 아이들은 그들 나름대로 사는 법을 배운다. 파미르의 황량함만큼이나 단순하고도 팍팍한 이들의 삶을 보여준다. 더불어 파미르 지역에 언제부터 사람들이 살기 시작했는지, 어떤 사람들이 거쳐 갔는지 파미르의 역사를 간략하게 소개한다.\\\\\\\\n\\']\"}, {\"\": \"4\", \"tv\": \"EBS\", \"title\": \"다큐프라임\", \"epi\": \"비밀의 땅 파미르 - 1부 세계의 지붕\", \"dates\": \"2018-09-10\", \"links\": \"http://www.ebs.co.kr/tv/show?courseId=BP0PAPB0000000005&stepId=01BP0PAPB0000000005&lectId=10225287\", \"synop\": \"\\\\\"세계의 지붕 파미르에 서식하는 다양한 야생동물들의 생태소개 1부에서는 파미르에 서식하는 다양한 야생동물과 파미르의 지역생태를 소개합니다. 그중에서도 특히 최고 포식자인 늑대를 중심으로 거친 생존의 현장을 보여줍니다. 늑대 이외에도 눈표범, 마르코폴로 양, 아이벡스, 수염수리 등 다양한 동물들이 어떤 관계를 맺으며 황량한 파미르에서 살아가는지를 소개합니다.   <주요 에피소드> 늑대와 인간 : 늑대의 먹이는 야생동물이지만 겨울이 오면 가축을 공격한다. 양, 염소, 야크와 같은 가축을 키우며 유목생활을 하는 파미르인들에게는 두려움과 원망의 대상이다. 유목민과 늑대의 이야기를 통해 이들의 적대적인 공존관계를 보여주고자 한다.   세계 최초 최상위 포식자들의 만남 눈표범과 늑대가 하나의 먹이를 두고 신경전을 벌인다. 그 어디에서도 보기 어려운 진귀한 광경으로 생생한 야생의 숨결을 느낄 수 있다. ??\\\\\"[\\'\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n\\\\\\\\n단 한 번도 공개된 적 없는 비밀의 땅, 세계의 지붕 파미르 \\\\\\\\n\\\\\\\\n\\\\\\\\n거대산맥들이 만들어낸 해발 4,000m의 고지\\\\\\\\n세계 최초, 하나의 먹이를 놓고 벌이는 최상위 포식자들의 신경전\\\\\\\\n총 제작기간 2년, 끊임없는 추적과 오랜 잠복, 제작진의 집요함이 만들어낸 대작 \\\\\\\\n\\\\\\\\xa0\\\\\\\\n\\\\\\\\n파미르고원, 한 번쯤 들어본 적 있는 익숙한 이름이지만 국내에는 거의 소개된 적 없는 비밀의 땅이다. 파미르고원은 히말라야산맥을 비롯해 텐샨, 카라코람, 쿤룬, 힌두쿠시 산맥이 모이는 곳으로 평균 해발고도가 4000m에 달한다. 수많은 산악인들이 오르는 히말라야와 달리 누구의 손길도 닿지 않은 미지의 땅이다. 는 삭막하고 거친 땅에 발을 붙이고 살아가는 생명들을 소개한다. 자연다큐멘터리의 명가 EBS 답게 쉽게 볼 수 없는 야생동물들의 생생한 모습 그대로 담아냈다. 무인 카메라를 설치하여 촬영한 영상에서는 늑대무리의 위계질서, 먹이를 뜯는 포식자의 면모를 볼 수 있'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_data[:10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "Html_file= open(\"crawling_finalsample_dahyun.json\",\"w\")\n",
    "Html_file.write(json_data)\n",
    "Html_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tv', 'title', 'epi', 'dates', 'links', 'synop']"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "header = list(final_sample.columns.unique())\n",
    "header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-84-0a57fa80ba1f>, line 20)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-84-0a57fa80ba1f>\"\u001b[0;36m, line \u001b[0;32m20\u001b[0m\n\u001b[0;31m    jsonfile.write('\\n')t\u001b[0m\n\u001b[0m                        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "import json\n",
    "\n",
    "\n",
    "\n",
    "csvfile = open('crawling_finalsample_dahyun.csv', 'r')\n",
    "jsonfile = open('crawling_finalsample_dahyun.json', 'w')\n",
    "\n",
    "\n",
    "\n",
    "fieldnames = header\n",
    "\n",
    "reader = csv.DictReader(csvfile, fieldnames)\n",
    "\n",
    "for row in reader:\n",
    "\n",
    "    json.dump(row, jsonfile, ensure_ascii=False)\n",
    "\n",
    "    jsonfile.write('\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.TextIOWrapper name='crawling_finalsample_dahyun.json' mode='w' encoding='UTF-8'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jsonfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.read_csv(\"data/final_data2.csv\", encoding = 'utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2646 entries, 0 to 2645\n",
      "Data columns (total 9 columns):\n",
      "Unnamed: 0    2646 non-null int64\n",
      "big           755 non-null object\n",
      "dates         1891 non-null object\n",
      "epi           2646 non-null object\n",
      "links         1891 non-null object\n",
      "synop         1888 non-null object\n",
      "tag           671 non-null object\n",
      "title         1891 non-null object\n",
      "tv            1891 non-null object\n",
      "dtypes: int64(1), object(8)\n",
      "memory usage: 186.1+ KB\n"
     ]
    }
   ],
   "source": [
    "final_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>Unnamed: 0</th>\n",
       "      <th>big</th>\n",
       "      <th>dates</th>\n",
       "      <th>epi</th>\n",
       "      <th>links</th>\n",
       "      <th>synop</th>\n",
       "      <th>tag</th>\n",
       "      <th>title</th>\n",
       "      <th>tv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.10.01</td>\n",
       "      <td>생존 체력, 약골 40대 저질체력 탈출기</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>“마흔 살, ‘생존 체력’이 필요하다”  \\r\\r\\r\\r\\r\\r\\r\\n  ‘불혹’...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MBC 스페셜</td>\n",
       "      <td>MBC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.09.10</td>\n",
       "      <td>당신, 독을 먹고 있나요?</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>■   \\r\\r\\r\\r\\r\\r\\r\\n스위트 드림(Sweet Dream), 허니문(...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MBC 스페셜</td>\n",
       "      <td>MBC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.09.03</td>\n",
       "      <td>방송의 날 특집6개월 후 만납시다: 북한 결핵병원 이야기</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>■   \\r\\r\\r\\r\\r\\r\\r\\n2018년, 한반도가 급변하기 시작했다. 11...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MBC 스페셜</td>\n",
       "      <td>MBC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.08.20</td>\n",
       "      <td>이산가족 상봉 특집 &lt;옥류관 서울 1호점&gt; 3부 이산</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>역사적인 4.27 남북정상회담을 계기로 8월 20일부터 26일까지 2015년 이후 ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MBC 스페셜</td>\n",
       "      <td>MBC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.08.13</td>\n",
       "      <td>도시x자연다큐멘터리Ⅰ 고냥이</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>■   \\r\\r\\r\\r\\r\\r\\r\\n1인 가구의 증가와 고령화 사회 속에서 반려동...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MBC 스페셜</td>\n",
       "      <td>MBC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  big       dates                              epi  \\\n",
       "0           0  NaN  2018.10.01           생존 체력, 약골 40대 저질체력 탈출기   \n",
       "1           1  NaN  2018.09.10                   당신, 독을 먹고 있나요?   \n",
       "2           2  NaN  2018.09.03  방송의 날 특집6개월 후 만납시다: 북한 결핵병원 이야기   \n",
       "3           3  NaN  2018.08.20    이산가족 상봉 특집 <옥류관 서울 1호점> 3부 이산   \n",
       "4           4  NaN  2018.08.13                  도시x자연다큐멘터리Ⅰ 고냥이   \n",
       "\n",
       "                                               links  \\\n",
       "0  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "1  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "2  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "3  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "4  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "\n",
       "                                               synop  tag    title   tv  \n",
       "0   “마흔 살, ‘생존 체력’이 필요하다”  \\r\\r\\r\\r\\r\\r\\r\\n  ‘불혹’...  NaN  MBC 스페셜  MBC  \n",
       "1   ■   \\r\\r\\r\\r\\r\\r\\r\\n스위트 드림(Sweet Dream), 허니문(...  NaN  MBC 스페셜  MBC  \n",
       "2   ■   \\r\\r\\r\\r\\r\\r\\r\\n2018년, 한반도가 급변하기 시작했다. 11...  NaN  MBC 스페셜  MBC  \n",
       "3  역사적인 4.27 남북정상회담을 계기로 8월 20일부터 26일까지 2015년 이후 ...  NaN  MBC 스페셜  MBC  \n",
       "4   ■   \\r\\r\\r\\r\\r\\r\\r\\n1인 가구의 증가와 고령화 사회 속에서 반려동...  NaN  MBC 스페셜  MBC  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "big_sample = ['자연/생태','인류/문명','인문','과학','교육','가정','정치/사회','역사','육아','학교교육','예술/대중문화','의학','경제/경영']\n",
    "big = []\n",
    "\n",
    "for i in range(len(final_data)):\n",
    "    big.append(big_sample[i%13])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data['big'] = big"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>■   \\r\\r\\r\\r\\r\\r\\r\\n2018년, 한반도가 급변하기 시작했다. 11...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MBC 스페셜</td>\n",
       "      <td>MBC</td>\n",
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       "      <td>과학</td>\n",
       "      <td>2018.08.20</td>\n",
       "      <td>이산가족 상봉 특집 &lt;옥류관 서울 1호점&gt; 3부 이산</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>역사적인 4.27 남북정상회담을 계기로 8월 20일부터 26일까지 2015년 이후 ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MBC 스페셜</td>\n",
       "      <td>MBC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>교육</td>\n",
       "      <td>2018.08.13</td>\n",
       "      <td>도시x자연다큐멘터리Ⅰ 고냥이</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>■   \\r\\r\\r\\r\\r\\r\\r\\n1인 가구의 증가와 고령화 사회 속에서 반려동...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MBC 스페셜</td>\n",
       "      <td>MBC</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0    big       dates                              epi  \\\n",
       "0           0  자연/생태  2018.10.01           생존 체력, 약골 40대 저질체력 탈출기   \n",
       "1           1  인류/문명  2018.09.10                   당신, 독을 먹고 있나요?   \n",
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       "3           3     과학  2018.08.20    이산가족 상봉 특집 <옥류관 서울 1호점> 3부 이산   \n",
       "4           4     교육  2018.08.13                  도시x자연다큐멘터리Ⅰ 고냥이   \n",
       "\n",
       "                                               links  \\\n",
       "0  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "1  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "2  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "3  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "4  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "\n",
       "                                               synop  tag    title   tv  \n",
       "0   “마흔 살, ‘생존 체력’이 필요하다”  \\r\\r\\r\\r\\r\\r\\r\\n  ‘불혹’...  NaN  MBC 스페셜  MBC  \n",
       "1   ■   \\r\\r\\r\\r\\r\\r\\r\\n스위트 드림(Sweet Dream), 허니문(...  NaN  MBC 스페셜  MBC  \n",
       "2   ■   \\r\\r\\r\\r\\r\\r\\r\\n2018년, 한반도가 급변하기 시작했다. 11...  NaN  MBC 스페셜  MBC  \n",
       "3  역사적인 4.27 남북정상회담을 계기로 8월 20일부터 26일까지 2015년 이후 ...  NaN  MBC 스페셜  MBC  \n",
       "4   ■   \\r\\r\\r\\r\\r\\r\\r\\n1인 가구의 증가와 고령화 사회 속에서 반려동...  NaN  MBC 스페셜  MBC  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data.columns = ['index','big','dates','epi','links','synop','tag','title','tv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>■   \\r\\r\\r\\r\\r\\r\\r\\n2018년, 한반도가 급변하기 시작했다. 11...</td>\n",
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       "      <td>MBC</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>MBC</td>\n",
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       "      <td>■   \\r\\r\\r\\r\\r\\r\\r\\n1인 가구의 증가와 고령화 사회 속에서 반려동...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>MBC</td>\n",
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      "text/plain": [
       "   index    big       dates                              epi  \\\n",
       "0      0  자연/생태  2018.10.01           생존 체력, 약골 40대 저질체력 탈출기   \n",
       "1      1  인류/문명  2018.09.10                   당신, 독을 먹고 있나요?   \n",
       "2      2     인문  2018.09.03  방송의 날 특집6개월 후 만납시다: 북한 결핵병원 이야기   \n",
       "3      3     과학  2018.08.20    이산가족 상봉 특집 <옥류관 서울 1호점> 3부 이산   \n",
       "4      4     교육  2018.08.13                  도시x자연다큐멘터리Ⅰ 고냥이   \n",
       "\n",
       "                                               links  \\\n",
       "0  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "1  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "2  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "3  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "4  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "\n",
       "                                               synop  tag    title   tv  \n",
       "0   “마흔 살, ‘생존 체력’이 필요하다”  \\r\\r\\r\\r\\r\\r\\r\\n  ‘불혹’...  NaN  MBC 스페셜  MBC  \n",
       "1   ■   \\r\\r\\r\\r\\r\\r\\r\\n스위트 드림(Sweet Dream), 허니문(...  NaN  MBC 스페셜  MBC  \n",
       "2   ■   \\r\\r\\r\\r\\r\\r\\r\\n2018년, 한반도가 급변하기 시작했다. 11...  NaN  MBC 스페셜  MBC  \n",
       "3  역사적인 4.27 남북정상회담을 계기로 8월 20일부터 26일까지 2015년 이후 ...  NaN  MBC 스페셜  MBC  \n",
       "4   ■   \\r\\r\\r\\r\\r\\r\\r\\n1인 가구의 증가와 고령화 사회 속에서 반려동...  NaN  MBC 스페셜  MBC  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data.to_csv(\"data/final_data.csv\", mode='w', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import json\n",
    "\n",
    "data = []\n",
    "with open('data/final_data.csv', encoding = 'utf-8') as f:\n",
    "    for row in csv.DictReader(f):\n",
    "        data.append(row)\n",
    "\n",
    "json_data = json.dumps(data, ensure_ascii=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "Html_file= open(\"data/final_data.json\",\"w\")\n",
    "Html_file.write(json_data)\n",
    "Html_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 여기서부터!!!!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = pd.read_csv(\"1005finaldata.csv\", encoding = 'utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>id</th>\n",
       "      <th>tv</th>\n",
       "      <th>title</th>\n",
       "      <th>epi</th>\n",
       "      <th>dates</th>\n",
       "      <th>links</th>\n",
       "      <th>synop</th>\n",
       "      <th>vod_key</th>\n",
       "      <th>image</th>\n",
       "      <th>class</th>\n",
       "      <th>awards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2742</th>\n",
       "      <td>2742</td>\n",
       "      <td>MBC</td>\n",
       "      <td>MBC스페셜</td>\n",
       "      <td>청춘다큐 다시, 스물 – 뉴 논스톱 편 1부</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>조인성  박경림에  누나가 사람 하나 살린 거야   18년 전  뉴 논스톱 에서 많...</td>\n",
       "      <td>'연기', '종영', '멤버들'</td>\n",
       "      <td>http://img.imbc.com/adams/Content/201810/13183...</td>\n",
       "      <td>예술/대중문화</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2743</th>\n",
       "      <td>2739</td>\n",
       "      <td>MBC</td>\n",
       "      <td>PD수첩</td>\n",
       "      <td>그 날  의사는 없었다</td>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/pd/vod/</td>\n",
       "      <td>2년 전 전북대 병원에서 골든타임을 놓쳐 사망한 2세 아이로만 알려져 있는 민건이...</td>\n",
       "      <td>'방치', '병원', '사망', '민건이', '할머니', '치료', '수술', '학...</td>\n",
       "      <td>http://img.imbc.com/adams/Content/201810/13183...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2744</th>\n",
       "      <td>2747</td>\n",
       "      <td>KBS</td>\n",
       "      <td>KBS스페셜</td>\n",
       "      <td>마이크로 사파리 집</td>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>https://goo.gl/TwKhw2</td>\n",
       "      <td>눈높이를 낮추고 마이크로의 세계로 보면 뒤뜰은 우주가 되고 만물은 서로 연결된다. ...</td>\n",
       "      <td>'뒤뜰', '노간주나무', '씨앗', '꽃가루', '큰멋쟁이나비', '밑들이벌', ...</td>\n",
       "      <td>http://d2fc09gk1936lv.cloudfront.net/kbs/390x2...</td>\n",
       "      <td>자연</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2745</th>\n",
       "      <td>2745</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS스페셜</td>\n",
       "      <td>인생단어를 찾아서</td>\n",
       "      <td>2018-10-07</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>생각하는 대로 살지 않으면 사는 대로 생각하게 된다   폴 부르제  프랑스 작가 ...</td>\n",
       "      <td>다큐</td>\n",
       "      <td>http://img2.sbs.co.kr/img/seditor/VD/2018/10/0...</td>\n",
       "      <td>인문</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2746</th>\n",
       "      <td>2743</td>\n",
       "      <td>MBC</td>\n",
       "      <td>MBC스페셜</td>\n",
       "      <td>청춘다큐 다시, 스물 - 뉴 논스톱 편 2부</td>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>조인성   뉴 논스톱  동창회에서  이민우 보니 21살의 나로 돌아간 것 같아  종...</td>\n",
       "      <td>'동창회', '이민우', '조인성', '작품', '동료들'</td>\n",
       "      <td>http://img.imbc.com/adams/Content/201810/13183...</td>\n",
       "      <td>예술/대중문화</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id   tv   title                       epi       dates  \\\n",
       "2742  2742  MBC  MBC스페셜  청춘다큐 다시, 스물 – 뉴 논스톱 편 1부  2018-10-01   \n",
       "2743  2739  MBC    PD수첩              그 날  의사는 없었다  2018-10-02   \n",
       "2744  2747  KBS  KBS스페셜                마이크로 사파리 집  2018-10-04   \n",
       "2745  2745  SBS  SBS스페셜                 인생단어를 찾아서  2018-10-07   \n",
       "2746  2743  MBC  MBC스페셜  청춘다큐 다시, 스물 - 뉴 논스톱 편 2부  2018-10-08   \n",
       "\n",
       "                                                  links  \\\n",
       "2742  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "2743       http://www.imbc.com/broad/tv/culture/pd/vod/   \n",
       "2744                              https://goo.gl/TwKhw2   \n",
       "2745  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "2746  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "\n",
       "                                                  synop  \\\n",
       "2742  조인성  박경림에  누나가 사람 하나 살린 거야   18년 전  뉴 논스톱 에서 많...   \n",
       "2743   2년 전 전북대 병원에서 골든타임을 놓쳐 사망한 2세 아이로만 알려져 있는 민건이...   \n",
       "2744  눈높이를 낮추고 마이크로의 세계로 보면 뒤뜰은 우주가 되고 만물은 서로 연결된다. ...   \n",
       "2745   생각하는 대로 살지 않으면 사는 대로 생각하게 된다   폴 부르제  프랑스 작가 ...   \n",
       "2746  조인성   뉴 논스톱  동창회에서  이민우 보니 21살의 나로 돌아간 것 같아  종...   \n",
       "\n",
       "                                                vod_key  \\\n",
       "2742                                  '연기', '종영', '멤버들'   \n",
       "2743  '방치', '병원', '사망', '민건이', '할머니', '치료', '수술', '학...   \n",
       "2744  '뒤뜰', '노간주나무', '씨앗', '꽃가루', '큰멋쟁이나비', '밑들이벌', ...   \n",
       "2745                                                 다큐   \n",
       "2746                   '동창회', '이민우', '조인성', '작품', '동료들'   \n",
       "\n",
       "                                                  image    class  awards  \n",
       "2742  http://img.imbc.com/adams/Content/201810/13183...  예술/대중문화       0  \n",
       "2743  http://img.imbc.com/adams/Content/201810/13183...       기타       0  \n",
       "2744  http://d2fc09gk1936lv.cloudfront.net/kbs/390x2...       자연       0  \n",
       "2745  http://img2.sbs.co.kr/img/seditor/VD/2018/10/0...       인문       0  \n",
       "2746  http://img.imbc.com/adams/Content/201810/13183...  예술/대중문화       0  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id         0\n",
       "tv         0\n",
       "title      0\n",
       "epi        0\n",
       "dates      0\n",
       "links      0\n",
       "synop      0\n",
       "vod_key    0\n",
       "image      0\n",
       "class      0\n",
       "awards     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_reco = pd.read_csv(\"Recommender_docu.csv\", encoding = 'utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = sample_data.sort_values(by=['dates', 'epi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>id</th>\n",
       "      <th>tv</th>\n",
       "      <th>title</th>\n",
       "      <th>epi</th>\n",
       "      <th>dates</th>\n",
       "      <th>links</th>\n",
       "      <th>synop</th>\n",
       "      <th>vod_key</th>\n",
       "      <th>image</th>\n",
       "      <th>class</th>\n",
       "      <th>awards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2742</th>\n",
       "      <td>2742</td>\n",
       "      <td>MBC</td>\n",
       "      <td>MBC스페셜</td>\n",
       "      <td>청춘다큐 다시, 스물 – 뉴 논스톱 편 1부</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>조인성  박경림에  누나가 사람 하나 살린 거야   18년 전  뉴 논스톱 에서 많...</td>\n",
       "      <td>'연기', '종영', '멤버들'</td>\n",
       "      <td>http://img.imbc.com/adams/Content/201810/13183...</td>\n",
       "      <td>예술/대중문화</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2743</th>\n",
       "      <td>2739</td>\n",
       "      <td>MBC</td>\n",
       "      <td>PD수첩</td>\n",
       "      <td>그 날  의사는 없었다</td>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/pd/vod/</td>\n",
       "      <td>2년 전 전북대 병원에서 골든타임을 놓쳐 사망한 2세 아이로만 알려져 있는 민건이...</td>\n",
       "      <td>'방치', '병원', '사망', '민건이', '할머니', '치료', '수술', '학...</td>\n",
       "      <td>http://img.imbc.com/adams/Content/201810/13183...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2744</th>\n",
       "      <td>2747</td>\n",
       "      <td>KBS</td>\n",
       "      <td>KBS스페셜</td>\n",
       "      <td>마이크로 사파리 집</td>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>https://goo.gl/TwKhw2</td>\n",
       "      <td>눈높이를 낮추고 마이크로의 세계로 보면 뒤뜰은 우주가 되고 만물은 서로 연결된다. ...</td>\n",
       "      <td>'뒤뜰', '노간주나무', '씨앗', '꽃가루', '큰멋쟁이나비', '밑들이벌', ...</td>\n",
       "      <td>http://d2fc09gk1936lv.cloudfront.net/kbs/390x2...</td>\n",
       "      <td>자연</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2745</th>\n",
       "      <td>2745</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS스페셜</td>\n",
       "      <td>인생단어를 찾아서</td>\n",
       "      <td>2018-10-07</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>생각하는 대로 살지 않으면 사는 대로 생각하게 된다   폴 부르제  프랑스 작가 ...</td>\n",
       "      <td>다큐</td>\n",
       "      <td>http://img2.sbs.co.kr/img/seditor/VD/2018/10/0...</td>\n",
       "      <td>인문</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2746</th>\n",
       "      <td>2743</td>\n",
       "      <td>MBC</td>\n",
       "      <td>MBC스페셜</td>\n",
       "      <td>청춘다큐 다시, 스물 - 뉴 논스톱 편 2부</td>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>조인성   뉴 논스톱  동창회에서  이민우 보니 21살의 나로 돌아간 것 같아  종...</td>\n",
       "      <td>'동창회', '이민우', '조인성', '작품', '동료들'</td>\n",
       "      <td>http://img.imbc.com/adams/Content/201810/13183...</td>\n",
       "      <td>예술/대중문화</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id   tv   title                       epi       dates  \\\n",
       "2742  2742  MBC  MBC스페셜  청춘다큐 다시, 스물 – 뉴 논스톱 편 1부  2018-10-01   \n",
       "2743  2739  MBC    PD수첩              그 날  의사는 없었다  2018-10-02   \n",
       "2744  2747  KBS  KBS스페셜                마이크로 사파리 집  2018-10-04   \n",
       "2745  2745  SBS  SBS스페셜                 인생단어를 찾아서  2018-10-07   \n",
       "2746  2743  MBC  MBC스페셜  청춘다큐 다시, 스물 - 뉴 논스톱 편 2부  2018-10-08   \n",
       "\n",
       "                                                  links  \\\n",
       "2742  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "2743       http://www.imbc.com/broad/tv/culture/pd/vod/   \n",
       "2744                              https://goo.gl/TwKhw2   \n",
       "2745  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "2746  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "\n",
       "                                                  synop  \\\n",
       "2742  조인성  박경림에  누나가 사람 하나 살린 거야   18년 전  뉴 논스톱 에서 많...   \n",
       "2743   2년 전 전북대 병원에서 골든타임을 놓쳐 사망한 2세 아이로만 알려져 있는 민건이...   \n",
       "2744  눈높이를 낮추고 마이크로의 세계로 보면 뒤뜰은 우주가 되고 만물은 서로 연결된다. ...   \n",
       "2745   생각하는 대로 살지 않으면 사는 대로 생각하게 된다   폴 부르제  프랑스 작가 ...   \n",
       "2746  조인성   뉴 논스톱  동창회에서  이민우 보니 21살의 나로 돌아간 것 같아  종...   \n",
       "\n",
       "                                                vod_key  \\\n",
       "2742                                  '연기', '종영', '멤버들'   \n",
       "2743  '방치', '병원', '사망', '민건이', '할머니', '치료', '수술', '학...   \n",
       "2744  '뒤뜰', '노간주나무', '씨앗', '꽃가루', '큰멋쟁이나비', '밑들이벌', ...   \n",
       "2745                                                 다큐   \n",
       "2746                   '동창회', '이민우', '조인성', '작품', '동료들'   \n",
       "\n",
       "                                                  image    class  awards  \n",
       "2742  http://img.imbc.com/adams/Content/201810/13183...  예술/대중문화       0  \n",
       "2743  http://img.imbc.com/adams/Content/201810/13183...       기타       0  \n",
       "2744  http://d2fc09gk1936lv.cloudfront.net/kbs/390x2...       자연       0  \n",
       "2745  http://img2.sbs.co.kr/img/seditor/VD/2018/10/0...       인문       0  \n",
       "2746  http://img.imbc.com/adams/Content/201810/13183...  예술/대중문화       0  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_id = list(range(2748)[1:2748])\n",
    "sample_data['id'] = temp_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>id</th>\n",
       "      <th>tv</th>\n",
       "      <th>title</th>\n",
       "      <th>epi</th>\n",
       "      <th>dates</th>\n",
       "      <th>links</th>\n",
       "      <th>synop</th>\n",
       "      <th>vod_key</th>\n",
       "      <th>image</th>\n",
       "      <th>class</th>\n",
       "      <th>awards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2743</th>\n",
       "      <td>2744</td>\n",
       "      <td>MBC</td>\n",
       "      <td>PD수첩</td>\n",
       "      <td>그 날  의사는 없었다</td>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/pd/vod/</td>\n",
       "      <td>2년 전 전북대 병원에서 골든타임을 놓쳐 사망한 2세 아이로만 알려져 있는 민건이...</td>\n",
       "      <td>'방치', '병원', '사망', '민건이', '할머니', '치료', '수술', '학...</td>\n",
       "      <td>http://img.imbc.com/adams/Content/201810/13183...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id   tv title           epi       dates  \\\n",
       "2743  2744  MBC  PD수첩  그 날  의사는 없었다  2018-10-02   \n",
       "\n",
       "                                             links  \\\n",
       "2743  http://www.imbc.com/broad/tv/culture/pd/vod/   \n",
       "\n",
       "                                                  synop  \\\n",
       "2743   2년 전 전북대 병원에서 골든타임을 놓쳐 사망한 2세 아이로만 알려져 있는 민건이...   \n",
       "\n",
       "                                                vod_key  \\\n",
       "2743  '방치', '병원', '사망', '민건이', '할머니', '치료', '수술', '학...   \n",
       "\n",
       "                                                  image class  awards  \n",
       "2743  http://img.imbc.com/adams/Content/201810/13183...    기타       0  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data[sample_data['id'] == 2744]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample_data.to_csv(\"sample_for_dayoung.csv\", mode='w', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2747, 11)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>id</th>\n",
       "      <th>reco1</th>\n",
       "      <th>reco2</th>\n",
       "      <th>reco3</th>\n",
       "      <th>reco4</th>\n",
       "      <th>reco5</th>\n",
       "      <th>reco6</th>\n",
       "      <th>reco7</th>\n",
       "      <th>reco8</th>\n",
       "      <th>reco9</th>\n",
       "      <th>reco10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>228</td>\n",
       "      <td>978</td>\n",
       "      <td>2242</td>\n",
       "      <td>641</td>\n",
       "      <td>357</td>\n",
       "      <td>1715</td>\n",
       "      <td>512</td>\n",
       "      <td>2160</td>\n",
       "      <td>2183</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1476</td>\n",
       "      <td>182</td>\n",
       "      <td>1112</td>\n",
       "      <td>1783</td>\n",
       "      <td>1245</td>\n",
       "      <td>135</td>\n",
       "      <td>836</td>\n",
       "      <td>89</td>\n",
       "      <td>1246</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1124</td>\n",
       "      <td>2426</td>\n",
       "      <td>713</td>\n",
       "      <td>284</td>\n",
       "      <td>1977</td>\n",
       "      <td>1549</td>\n",
       "      <td>1666</td>\n",
       "      <td>2047</td>\n",
       "      <td>2163</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2242</td>\n",
       "      <td>1918</td>\n",
       "      <td>207</td>\n",
       "      <td>2160</td>\n",
       "      <td>1034</td>\n",
       "      <td>1197</td>\n",
       "      <td>266</td>\n",
       "      <td>1</td>\n",
       "      <td>386</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>429</td>\n",
       "      <td>94</td>\n",
       "      <td>1005</td>\n",
       "      <td>1006</td>\n",
       "      <td>1007</td>\n",
       "      <td>224</td>\n",
       "      <td>245</td>\n",
       "      <td>533</td>\n",
       "      <td>1942</td>\n",
       "      <td>989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  reco1  reco2  reco3  reco4  reco5  reco6  reco7  reco8  reco9  reco10\n",
       "0   1    228    978   2242    641    357   1715    512   2160   2183       4\n",
       "1   2   1476    182   1112   1783   1245    135    836     89   1246     548\n",
       "2   3   1124   2426    713    284   1977   1549   1666   2047   2163    1985\n",
       "3   4   2242   1918    207   2160   1034   1197    266      1    386       9\n",
       "4   5    429     94   1005   1006   1007    224    245    533   1942     989"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_reco.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1124"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_reco.loc[2][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "recolist = []\n",
    "\n",
    "for i in range(len(sample_reco)):\n",
    "    temp = []\n",
    "    \n",
    "    for j in range(10):\n",
    "        num = j+1\n",
    "        temp.append(sample_reco.loc[i][num])   \n",
    "        \n",
    "    recolist.append(temp)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[228, 978, 2242, 641, 357, 1715, 512, 2160, 2183, 4],\n",
       " [1476, 182, 1112, 1783, 1245, 135, 836, 89, 1246, 548],\n",
       " [1124, 2426, 713, 284, 1977, 1549, 1666, 2047, 2163, 1985],\n",
       " [2242, 1918, 207, 2160, 1034, 1197, 266, 1, 386, 9],\n",
       " [429, 94, 1005, 1006, 1007, 224, 245, 533, 1942, 989]]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recolist[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'tv', 'title', 'epi', 'dates', 'links', 'synop', 'vod_key',\n",
       "       'image', 'class', 'awards'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.columns.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data['reco'] = recolist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>id</th>\n",
       "      <th>tv</th>\n",
       "      <th>title</th>\n",
       "      <th>epi</th>\n",
       "      <th>dates</th>\n",
       "      <th>links</th>\n",
       "      <th>synop</th>\n",
       "      <th>vod_key</th>\n",
       "      <th>image</th>\n",
       "      <th>class</th>\n",
       "      <th>awards</th>\n",
       "      <th>reco</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>SBS</td>\n",
       "      <td>그것이 알고싶다</td>\n",
       "      <td>되살아난 연쇄살인의 공포 - 부녀자 연쇄 피습 사건의 진실</td>\n",
       "      <td>2004-07-03</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/unansweredqu...</td>\n",
       "      <td>제 목  되살아난 연쇄살인의 공포  부녀자 연쇄 피습 사건의 진실  방송일시  2...</td>\n",
       "      <td>'범죄', '흉기'</td>\n",
       "      <td>//img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR4...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "      <td>[228, 978, 2242, 641, 357, 1715, 512, 2160, 21...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>SBS</td>\n",
       "      <td>그것이 알고싶다</td>\n",
       "      <td>2004 행복 신드롬 - 당신은 웰빙하고 계십니까</td>\n",
       "      <td>2004-07-10</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/unansweredqu...</td>\n",
       "      <td>2004 행복신드롬   당신은 웰빙하고 계십니까   웰빙은 즐거운 변화   늘어...</td>\n",
       "      <td>'웰빙', '관심', '건강', '사회'</td>\n",
       "      <td>//img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR5...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "      <td>[1476, 182, 1112, 1783, 1245, 135, 836, 89, 12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>SBS</td>\n",
       "      <td>그것이 알고싶다</td>\n",
       "      <td>500회 특집 노블레스 오블리주, 가진 자의 의무</td>\n",
       "      <td>2004-07-17</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/unansweredqu...</td>\n",
       "      <td>지금 우리는 국민소득 2만 달러 달성과 선진국 진입의 중요한 기로에 서 있다. 자원...</td>\n",
       "      <td>'공개', '사회', '통합', '지도층', '상속', '오블리주', '미국', '...</td>\n",
       "      <td>//img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR4...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "      <td>[1124, 2426, 713, 284, 1977, 1549, 1666, 2047,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>SBS</td>\n",
       "      <td>그것이 알고싶다</td>\n",
       "      <td>살인마는 바로 곁에 있었다</td>\n",
       "      <td>2004-07-24</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/unansweredqu...</td>\n",
       "      <td>21명 연쇄살인 피의자  유영철의 진실은 무엇인가  무고한 시민 21명을 살해한 ...</td>\n",
       "      <td>'연쇄살인', '유영철', '부유층', '상대', '여성', '연쇄살인범', '특성'</td>\n",
       "      <td>//img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR7...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "      <td>[2242, 1918, 207, 2160, 1034, 1197, 266, 1, 38...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>SBS</td>\n",
       "      <td>그것이 알고싶다</td>\n",
       "      <td>외면당한 어린 모성 - 10대 미혼모, 아직도 버려져야 하는가</td>\n",
       "      <td>2004-07-31</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/unansweredqu...</td>\n",
       "      <td>누구도 축복해 주지 않는 출산의 공포 임신이라는 말에 연락을 끊어버린 남자친구  ...</td>\n",
       "      <td>'출산', '임신', '아이', '엄마', '성교육', '책임', '10대', '미혼모'</td>\n",
       "      <td>//img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR3...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "      <td>[429, 94, 1005, 1006, 1007, 224, 245, 533, 194...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   tv     title                                 epi       dates  \\\n",
       "0   1  SBS  그것이 알고싶다    되살아난 연쇄살인의 공포 - 부녀자 연쇄 피습 사건의 진실  2004-07-03   \n",
       "1   2  SBS  그것이 알고싶다         2004 행복 신드롬 - 당신은 웰빙하고 계십니까  2004-07-10   \n",
       "2   3  SBS  그것이 알고싶다         500회 특집 노블레스 오블리주, 가진 자의 의무  2004-07-17   \n",
       "3   4  SBS  그것이 알고싶다                      살인마는 바로 곁에 있었다  2004-07-24   \n",
       "4   5  SBS  그것이 알고싶다  외면당한 어린 모성 - 10대 미혼모, 아직도 버려져야 하는가  2004-07-31   \n",
       "\n",
       "                                               links  \\\n",
       "0  http://programs.sbs.co.kr/culture/unansweredqu...   \n",
       "1  http://programs.sbs.co.kr/culture/unansweredqu...   \n",
       "2  http://programs.sbs.co.kr/culture/unansweredqu...   \n",
       "3  http://programs.sbs.co.kr/culture/unansweredqu...   \n",
       "4  http://programs.sbs.co.kr/culture/unansweredqu...   \n",
       "\n",
       "                                               synop  \\\n",
       "0   제 목  되살아난 연쇄살인의 공포  부녀자 연쇄 피습 사건의 진실  방송일시  2...   \n",
       "1    2004 행복신드롬   당신은 웰빙하고 계십니까   웰빙은 즐거운 변화   늘어...   \n",
       "2  지금 우리는 국민소득 2만 달러 달성과 선진국 진입의 중요한 기로에 서 있다. 자원...   \n",
       "3   21명 연쇄살인 피의자  유영철의 진실은 무엇인가  무고한 시민 21명을 살해한 ...   \n",
       "4   누구도 축복해 주지 않는 출산의 공포 임신이라는 말에 연락을 끊어버린 남자친구  ...   \n",
       "\n",
       "                                             vod_key  \\\n",
       "0                                         '범죄', '흉기'   \n",
       "1                             '웰빙', '관심', '건강', '사회'   \n",
       "2  '공개', '사회', '통합', '지도층', '상속', '오블리주', '미국', '...   \n",
       "3    '연쇄살인', '유영철', '부유층', '상대', '여성', '연쇄살인범', '특성'   \n",
       "4  '출산', '임신', '아이', '엄마', '성교육', '책임', '10대', '미혼모'   \n",
       "\n",
       "                                               image class  awards  \\\n",
       "0  //img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR4...    기타       0   \n",
       "1  //img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR5...    기타       0   \n",
       "2  //img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR4...    기타       0   \n",
       "3  //img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR7...    기타       0   \n",
       "4  //img2.sbs.co.kr/img/sbs_cms/SR/2015/12/08/SR3...    기타       0   \n",
       "\n",
       "                                                reco  \n",
       "0  [228, 978, 2242, 641, 357, 1715, 512, 2160, 21...  \n",
       "1  [1476, 182, 1112, 1783, 1245, 135, 836, 89, 12...  \n",
       "2  [1124, 2426, 713, 284, 1977, 1549, 1666, 2047,...  \n",
       "3  [2242, 1918, 207, 2160, 1034, 1197, 266, 1, 38...  \n",
       "4  [429, 94, 1005, 1006, 1007, 224, 245, 533, 194...  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data['vod_key'] = '[' + sample_data['vod_key'].astype(str) + ']'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>id</th>\n",
       "      <th>tv</th>\n",
       "      <th>title</th>\n",
       "      <th>epi</th>\n",
       "      <th>dates</th>\n",
       "      <th>links</th>\n",
       "      <th>synop</th>\n",
       "      <th>vod_key</th>\n",
       "      <th>image</th>\n",
       "      <th>class</th>\n",
       "      <th>awards</th>\n",
       "      <th>reco</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2742</th>\n",
       "      <td>2743</td>\n",
       "      <td>MBC</td>\n",
       "      <td>MBC스페셜</td>\n",
       "      <td>청춘다큐 다시, 스물 – 뉴 논스톱 편 1부</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>조인성  박경림에  누나가 사람 하나 살린 거야   18년 전  뉴 논스톱 에서 많...</td>\n",
       "      <td>['연기', '종영', '멤버들']</td>\n",
       "      <td>http://img.imbc.com/adams/Content/201810/13183...</td>\n",
       "      <td>예술/대중문화</td>\n",
       "      <td>0</td>\n",
       "      <td>[2747, 816, 2440, 2221, 421, 696, 1387, 460, 5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2743</th>\n",
       "      <td>2744</td>\n",
       "      <td>MBC</td>\n",
       "      <td>PD수첩</td>\n",
       "      <td>그 날  의사는 없었다</td>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/pd/vod/</td>\n",
       "      <td>2년 전 전북대 병원에서 골든타임을 놓쳐 사망한 2세 아이로만 알려져 있는 민건이...</td>\n",
       "      <td>['방치', '병원', '사망', '민건이', '할머니', '치료', '수술', '...</td>\n",
       "      <td>http://img.imbc.com/adams/Content/201810/13183...</td>\n",
       "      <td>기타</td>\n",
       "      <td>0</td>\n",
       "      <td>[2316, 2363, 1672, 1571, 2017, 1147, 1675, 178...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2744</th>\n",
       "      <td>2745</td>\n",
       "      <td>KBS</td>\n",
       "      <td>KBS스페셜</td>\n",
       "      <td>마이크로 사파리 집</td>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>https://goo.gl/TwKhw2</td>\n",
       "      <td>눈높이를 낮추고 마이크로의 세계로 보면 뒤뜰은 우주가 되고 만물은 서로 연결된다. ...</td>\n",
       "      <td>['뒤뜰', '노간주나무', '씨앗', '꽃가루', '큰멋쟁이나비', '밑들이벌',...</td>\n",
       "      <td>http://d2fc09gk1936lv.cloudfront.net/kbs/390x2...</td>\n",
       "      <td>자연</td>\n",
       "      <td>0</td>\n",
       "      <td>[2615, 420, 1478, 1625, 1609, 2428, 2614, 936,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2745</th>\n",
       "      <td>2746</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS스페셜</td>\n",
       "      <td>인생단어를 찾아서</td>\n",
       "      <td>2018-10-07</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>생각하는 대로 살지 않으면 사는 대로 생각하게 된다   폴 부르제  프랑스 작가 ...</td>\n",
       "      <td>[다큐]</td>\n",
       "      <td>http://img2.sbs.co.kr/img/seditor/VD/2018/10/0...</td>\n",
       "      <td>인문</td>\n",
       "      <td>0</td>\n",
       "      <td>[983, 2324, 1497, 1652, 592, 101, 1097, 293, 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2746</th>\n",
       "      <td>2747</td>\n",
       "      <td>MBC</td>\n",
       "      <td>MBC스페셜</td>\n",
       "      <td>청춘다큐 다시, 스물 - 뉴 논스톱 편 2부</td>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>http://www.imbc.com/broad/tv/culture/mbcspecia...</td>\n",
       "      <td>조인성   뉴 논스톱  동창회에서  이민우 보니 21살의 나로 돌아간 것 같아  종...</td>\n",
       "      <td>['동창회', '이민우', '조인성', '작품', '동료들']</td>\n",
       "      <td>http://img.imbc.com/adams/Content/201810/13183...</td>\n",
       "      <td>예술/대중문화</td>\n",
       "      <td>0</td>\n",
       "      <td>[2743, 524, 421, 960, 525, 653, 816, 810, 696,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id   tv   title                       epi       dates  \\\n",
       "2742  2743  MBC  MBC스페셜  청춘다큐 다시, 스물 – 뉴 논스톱 편 1부  2018-10-01   \n",
       "2743  2744  MBC    PD수첩              그 날  의사는 없었다  2018-10-02   \n",
       "2744  2745  KBS  KBS스페셜                마이크로 사파리 집  2018-10-04   \n",
       "2745  2746  SBS  SBS스페셜                 인생단어를 찾아서  2018-10-07   \n",
       "2746  2747  MBC  MBC스페셜  청춘다큐 다시, 스물 - 뉴 논스톱 편 2부  2018-10-08   \n",
       "\n",
       "                                                  links  \\\n",
       "2742  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "2743       http://www.imbc.com/broad/tv/culture/pd/vod/   \n",
       "2744                              https://goo.gl/TwKhw2   \n",
       "2745  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "2746  http://www.imbc.com/broad/tv/culture/mbcspecia...   \n",
       "\n",
       "                                                  synop  \\\n",
       "2742  조인성  박경림에  누나가 사람 하나 살린 거야   18년 전  뉴 논스톱 에서 많...   \n",
       "2743   2년 전 전북대 병원에서 골든타임을 놓쳐 사망한 2세 아이로만 알려져 있는 민건이...   \n",
       "2744  눈높이를 낮추고 마이크로의 세계로 보면 뒤뜰은 우주가 되고 만물은 서로 연결된다. ...   \n",
       "2745   생각하는 대로 살지 않으면 사는 대로 생각하게 된다   폴 부르제  프랑스 작가 ...   \n",
       "2746  조인성   뉴 논스톱  동창회에서  이민우 보니 21살의 나로 돌아간 것 같아  종...   \n",
       "\n",
       "                                                vod_key  \\\n",
       "2742                                ['연기', '종영', '멤버들']   \n",
       "2743  ['방치', '병원', '사망', '민건이', '할머니', '치료', '수술', '...   \n",
       "2744  ['뒤뜰', '노간주나무', '씨앗', '꽃가루', '큰멋쟁이나비', '밑들이벌',...   \n",
       "2745                                               [다큐]   \n",
       "2746                 ['동창회', '이민우', '조인성', '작품', '동료들']   \n",
       "\n",
       "                                                  image    class  awards  \\\n",
       "2742  http://img.imbc.com/adams/Content/201810/13183...  예술/대중문화       0   \n",
       "2743  http://img.imbc.com/adams/Content/201810/13183...       기타       0   \n",
       "2744  http://d2fc09gk1936lv.cloudfront.net/kbs/390x2...       자연       0   \n",
       "2745  http://img2.sbs.co.kr/img/seditor/VD/2018/10/0...       인문       0   \n",
       "2746  http://img.imbc.com/adams/Content/201810/13183...  예술/대중문화       0   \n",
       "\n",
       "                                                   reco  \n",
       "2742  [2747, 816, 2440, 2221, 421, 696, 1387, 460, 5...  \n",
       "2743  [2316, 2363, 1672, 1571, 2017, 1147, 1675, 178...  \n",
       "2744  [2615, 420, 1478, 1625, 1609, 2428, 2614, 936,...  \n",
       "2745  [983, 2324, 1497, 1652, 592, 101, 1097, 293, 3...  \n",
       "2746  [2743, 524, 421, 960, 525, 653, 816, 810, 696,...  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data.to_csv(\"macngrams_docu.csv\", mode='w', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import json\n",
    "\n",
    "data = []\n",
    "with open('macngrams_docu.csv', encoding = 'utf-8') as f:\n",
    "    for row in csv.DictReader(f):\n",
    "        data.append(row)\n",
    "\n",
    "json_data = json.dumps(data, ensure_ascii=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "Html_file= open(\"macngrams_docu.json\",\"w\")\n",
    "Html_file.write(json_data)\n",
    "Html_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data.to_csv(\"data/sample_data.csv\", mode='w', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import json\n",
    "\n",
    "data = []\n",
    "with open('data/sample_data.csv', encoding = 'utf-8') as f:\n",
    "    for row in csv.DictReader(f):\n",
    "        data.append(row)\n",
    "\n",
    "json_data = json.dumps(data, ensure_ascii=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "Html_file= open(\"data/sample_data.json\",\"w\")\n",
    "Html_file.write(json_data)\n",
    "Html_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KoreanTITGD1R\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-1c2830aa5132>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m patches, labels = plt.pie(sizes, explode=explode, colors=colors,\n\u001b[0;32m---> 32\u001b[0;31m         autopct='%1.1f%%', labels=labels, shadow=True, startangle=140)\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpatches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"best\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "# 폰트 설정\n",
    "path = '/Library/Fonts/a타이틀고딕1.otf'\n",
    "font_name = fm.FontProperties(fname=path, size=50).get_name()\n",
    "print(font_name)\n",
    "plt.rc('font', family=font_name)\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "\n",
    "\n",
    "labels = ['인문','인류/문명','자연/생태','정치/사회', '과학', '가정',\n",
    "         '교육', '예술/대중문화', '의학', '역사', '학교교육', '경제/경영','육아' ]\n",
    "sizes = [343,302,249,164,134,123,121,99,97,82,68,68,37]\n",
    "\n",
    "\n",
    "explode = (0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)  # explode 1st slice\n",
    "colors = [\"#3288BD\",\n",
    "          \"#66C2A5\",\n",
    "          \"#ABDDA4\",\n",
    "          \"#E6F598\",\n",
    "          \"#FEE08B\",\n",
    "          \"#FDAE61\",\n",
    "          \"#F46D43\",\n",
    "          \"#D53E4F\"]\n",
    " \n",
    "# Plot\n",
    "plt.figure(figsize=(10,10))\n",
    "patches, labels = plt.pie(sizes, explode=explode, colors=colors,\n",
    "        autopct='%1.1f%%', labels=labels, shadow=True, startangle=140)\n",
    "plt.legend(patches, labels, loc=\"best\")\n",
    " \n",
    "plt.axis('equal')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# patches, texts = plt.pie(sizes, colors=colors, shadow=True, startangle=90)\n",
    "# plt.legend(patches, labels, loc=\"best\")\n",
    "# plt.axis('equal')\n",
    "# plt.tight_layout()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "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>id</th>\n",
       "      <th>tv</th>\n",
       "      <th>title</th>\n",
       "      <th>epi</th>\n",
       "      <th>dates</th>\n",
       "      <th>links</th>\n",
       "      <th>synop</th>\n",
       "      <th>vod_key</th>\n",
       "      <th>big</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS 스페셜</td>\n",
       "      <td>그랜드 슬램 대탐험  걸어서 지구 끝까지</td>\n",
       "      <td>2005-07-10</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>SBS 스페셜 1편  그랜드 슬램 대탐험  걸어서 지구 끝까지   기획  장광호...</td>\n",
       "      <td>['움직', '프로그램', '블리자드', '순간', '그랜드슬램']</td>\n",
       "      <td>자연/생태</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS 스페셜</td>\n",
       "      <td>꿈을 쏘다! - 박주영 박지성</td>\n",
       "      <td>2005-07-17</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>요즘 온 국민을 열광하게 만든 두 젊은이가 있다. 그라운드의 승부사인 박주영과 박지...</td>\n",
       "      <td>['젊은이', '그라운드', '박주영', '박지성', '선수', '실력', '축구'...</td>\n",
       "      <td>인류/문명</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS 스페셜</td>\n",
       "      <td>메이드 인 코리아 1부 - 광복 60년 한국인들의 위상</td>\n",
       "      <td>2005-07-24</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>한국에 있으면 잘 모르는데 해외에 있으면 한국인이 갖는 뭔가가 있다. 책임감  끈...</td>\n",
       "      <td>['한국', '세계', '대표', '최고', '이들', '글라이더', '모습', '...</td>\n",
       "      <td>인문</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS 스페셜</td>\n",
       "      <td>메이드 인 코리아 2부 - 한국천재들</td>\n",
       "      <td>2005-07-31</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>SBS 스페셜  메이드 인 코리아  2부    한국천재들 21세기형 천재  앞으로 ...</td>\n",
       "      <td>['천재', '천재들', '세계', 'MP3', '김영세', 'PET', '연구소']</td>\n",
       "      <td>과학</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS 스페셜</td>\n",
       "      <td>메이드 인 코리아 3부 - 한국인의 손</td>\n",
       "      <td>2005-08-07</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>한국인의 손  불가능의 벽을 넘어 바이오 혁명을 이루다 세계 최초로 인간 배아 줄...</td>\n",
       "      <td>['한국인', '세계']</td>\n",
       "      <td>교육</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   tv    title                             epi      dates  \\\n",
       "0   1  SBS  SBS 스페셜          그랜드 슬램 대탐험  걸어서 지구 끝까지 2005-07-10   \n",
       "1   2  SBS  SBS 스페셜                꿈을 쏘다! - 박주영 박지성 2005-07-17   \n",
       "2   3  SBS  SBS 스페셜  메이드 인 코리아 1부 - 광복 60년 한국인들의 위상 2005-07-24   \n",
       "3   4  SBS  SBS 스페셜            메이드 인 코리아 2부 - 한국천재들 2005-07-31   \n",
       "4   5  SBS  SBS 스페셜           메이드 인 코리아 3부 - 한국인의 손 2005-08-07   \n",
       "\n",
       "                                               links  \\\n",
       "0  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "1  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "2  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "3  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "4  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "\n",
       "                                               synop  \\\n",
       "0    SBS 스페셜 1편  그랜드 슬램 대탐험  걸어서 지구 끝까지   기획  장광호...   \n",
       "1  요즘 온 국민을 열광하게 만든 두 젊은이가 있다. 그라운드의 승부사인 박주영과 박지...   \n",
       "2   한국에 있으면 잘 모르는데 해외에 있으면 한국인이 갖는 뭔가가 있다. 책임감  끈...   \n",
       "3  SBS 스페셜  메이드 인 코리아  2부    한국천재들 21세기형 천재  앞으로 ...   \n",
       "4   한국인의 손  불가능의 벽을 넘어 바이오 혁명을 이루다 세계 최초로 인간 배아 줄...   \n",
       "\n",
       "                                             vod_key    big  \n",
       "0              ['움직', '프로그램', '블리자드', '순간', '그랜드슬램']  자연/생태  \n",
       "1  ['젊은이', '그라운드', '박주영', '박지성', '선수', '실력', '축구'...  인류/문명  \n",
       "2  ['한국', '세계', '대표', '최고', '이들', '글라이더', '모습', '...     인문  \n",
       "3    ['천재', '천재들', '세계', 'MP3', '김영세', 'PET', '연구소']     과학  \n",
       "4                                      ['한국인', '세계']     교육  "
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample_data['dates'] = sample_data['dates'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1890 entries, 0 to 1889\n",
      "Data columns (total 9 columns):\n",
      "id         1890 non-null int64\n",
      "tv         1890 non-null object\n",
      "title      1890 non-null object\n",
      "epi        1890 non-null object\n",
      "dates      1890 non-null datetime64[ns]\n",
      "links      1890 non-null object\n",
      "synop      1890 non-null object\n",
      "vod_key    1890 non-null object\n",
      "big        1890 non-null object\n",
      "dtypes: datetime64[ns](1), int64(1), object(7)\n",
      "memory usage: 133.0+ KB\n"
     ]
    }
   ],
   "source": [
    "sample_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [],
   "source": [
    "tempdata = sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "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>id</th>\n",
       "      <th>tv</th>\n",
       "      <th>title</th>\n",
       "      <th>epi</th>\n",
       "      <th>dates</th>\n",
       "      <th>links</th>\n",
       "      <th>synop</th>\n",
       "      <th>vod_key</th>\n",
       "      <th>big</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS 스페셜</td>\n",
       "      <td>그랜드 슬램 대탐험  걸어서 지구 끝까지</td>\n",
       "      <td>2005-07-10</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>SBS 스페셜 1편  그랜드 슬램 대탐험  걸어서 지구 끝까지   기획  장광호...</td>\n",
       "      <td>['움직', '프로그램', '블리자드', '순간', '그랜드슬램']</td>\n",
       "      <td>자연/생태</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS 스페셜</td>\n",
       "      <td>꿈을 쏘다! - 박주영 박지성</td>\n",
       "      <td>2005-07-17</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>요즘 온 국민을 열광하게 만든 두 젊은이가 있다. 그라운드의 승부사인 박주영과 박지...</td>\n",
       "      <td>['젊은이', '그라운드', '박주영', '박지성', '선수', '실력', '축구'...</td>\n",
       "      <td>인류/문명</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS 스페셜</td>\n",
       "      <td>메이드 인 코리아 1부 - 광복 60년 한국인들의 위상</td>\n",
       "      <td>2005-07-24</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>한국에 있으면 잘 모르는데 해외에 있으면 한국인이 갖는 뭔가가 있다. 책임감  끈...</td>\n",
       "      <td>['한국', '세계', '대표', '최고', '이들', '글라이더', '모습', '...</td>\n",
       "      <td>인문</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS 스페셜</td>\n",
       "      <td>메이드 인 코리아 2부 - 한국천재들</td>\n",
       "      <td>2005-07-31</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>SBS 스페셜  메이드 인 코리아  2부    한국천재들 21세기형 천재  앞으로 ...</td>\n",
       "      <td>['천재', '천재들', '세계', 'MP3', '김영세', 'PET', '연구소']</td>\n",
       "      <td>과학</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>SBS</td>\n",
       "      <td>SBS 스페셜</td>\n",
       "      <td>메이드 인 코리아 3부 - 한국인의 손</td>\n",
       "      <td>2005-08-07</td>\n",
       "      <td>http://programs.sbs.co.kr/culture/sbsspecial/v...</td>\n",
       "      <td>한국인의 손  불가능의 벽을 넘어 바이오 혁명을 이루다 세계 최초로 인간 배아 줄...</td>\n",
       "      <td>['한국인', '세계']</td>\n",
       "      <td>교육</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   tv    title                             epi      dates  \\\n",
       "0   1  SBS  SBS 스페셜          그랜드 슬램 대탐험  걸어서 지구 끝까지 2005-07-10   \n",
       "1   2  SBS  SBS 스페셜                꿈을 쏘다! - 박주영 박지성 2005-07-17   \n",
       "2   3  SBS  SBS 스페셜  메이드 인 코리아 1부 - 광복 60년 한국인들의 위상 2005-07-24   \n",
       "3   4  SBS  SBS 스페셜            메이드 인 코리아 2부 - 한국천재들 2005-07-31   \n",
       "4   5  SBS  SBS 스페셜           메이드 인 코리아 3부 - 한국인의 손 2005-08-07   \n",
       "\n",
       "                                               links  \\\n",
       "0  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "1  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "2  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "3  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "4  http://programs.sbs.co.kr/culture/sbsspecial/v...   \n",
       "\n",
       "                                               synop  \\\n",
       "0    SBS 스페셜 1편  그랜드 슬램 대탐험  걸어서 지구 끝까지   기획  장광호...   \n",
       "1  요즘 온 국민을 열광하게 만든 두 젊은이가 있다. 그라운드의 승부사인 박주영과 박지...   \n",
       "2   한국에 있으면 잘 모르는데 해외에 있으면 한국인이 갖는 뭔가가 있다. 책임감  끈...   \n",
       "3  SBS 스페셜  메이드 인 코리아  2부    한국천재들 21세기형 천재  앞으로 ...   \n",
       "4   한국인의 손  불가능의 벽을 넘어 바이오 혁명을 이루다 세계 최초로 인간 배아 줄...   \n",
       "\n",
       "                                             vod_key    big  year  \n",
       "0              ['움직', '프로그램', '블리자드', '순간', '그랜드슬램']  자연/생태  2005  \n",
       "1  ['젊은이', '그라운드', '박주영', '박지성', '선수', '실력', '축구'...  인류/문명  2005  \n",
       "2  ['한국', '세계', '대표', '최고', '이들', '글라이더', '모습', '...     인문  2005  \n",
       "3    ['천재', '천재들', '세계', 'MP3', '김영세', 'PET', '연구소']     과학  2005  \n",
       "4                                      ['한국인', '세계']     교육  2005  "
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempdata['year'] = sample_data['dates'].dt.year\n",
    "tempdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [],
   "source": [
    "yearby = tempdata.groupby(['year','big']).size().reset_index(name='counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>year</th>\n",
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       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>가정</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>경제/경영</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
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       "      <td>교육</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005</td>\n",
       "      <td>역사</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>예술/대중문화</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2005</td>\n",
       "      <td>육아</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2005</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>2</td>\n",
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       "      <th>9</th>\n",
       "      <td>2005</td>\n",
       "      <td>인문</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2005</td>\n",
       "      <td>자연/생태</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2005</td>\n",
       "      <td>정치/사회</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2005</td>\n",
       "      <td>학교교육</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year      big  counts\n",
       "0   2005       가정       2\n",
       "1   2005    경제/경영       1\n",
       "2   2005       과학       2\n",
       "3   2005       교육       2\n",
       "4   2005       역사       2\n",
       "5   2005  예술/대중문화       1\n",
       "6   2005       육아       2\n",
       "7   2005       의학       1\n",
       "8   2005    인류/문명       2\n",
       "9   2005       인문       2\n",
       "10  2005    자연/생태       2\n",
       "11  2005    정치/사회       2\n",
       "12  2005     학교교육       2"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearby.head(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "yearby_df = []\n",
    "\n",
    "big = list(yearby.big.unique())\n",
    "year = list(yearby.year.unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearby[(yearby['year'] == 2005) & (yearby['big'] == \"경제/경영\")].counts.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2],\n",
       " [3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2],\n",
       " [3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3],\n",
       " [6, 7, 7, 7, 6, 7, 6, 7, 7, 7, 7, 6, 7],\n",
       " [14, 13, 13, 13, 14, 13, 13, 13, 13, 13, 13, 14, 13],\n",
       " [13, 13, 13, 13, 12, 13, 13, 13, 13, 13, 13, 12, 13],\n",
       " [12, 12, 12, 12, 13, 12, 13, 12, 12, 12, 12, 13, 12],\n",
       " [13, 13, 13, 13, 12, 13, 12, 13, 13, 13, 13, 13, 13],\n",
       " [13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 12, 13],\n",
       " [13, 13, 13, 13, 14, 13, 14, 13, 13, 13, 13, 14, 13],\n",
       " [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13],\n",
       " [14, 15, 14, 14, 14, 15, 14, 15, 14, 14, 14, 14, 14],\n",
       " [13, 13, 13, 13, 13, 13, 13, 13, 14, 13, 14, 13, 13],\n",
       " [14, 14, 15, 15, 14, 14, 14, 14, 14, 15, 14, 14, 14]]"
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# final_df = []\n",
    "temp_list = []\n",
    "count_f = []\n",
    "\n",
    "for y in year:\n",
    "    for b in big:\n",
    "        temp_list.append(yearby[(yearby['year'] == y) & (yearby['big'] == b)].counts.iloc[0])\n",
    "    count_f.append(temp_list)\n",
    "    temp_list = []\n",
    "    \n",
    "count_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "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>가정</th>\n",
       "      <th>경제/경영</th>\n",
       "      <th>과학</th>\n",
       "      <th>교육</th>\n",
       "      <th>역사</th>\n",
       "      <th>예술/대중문화</th>\n",
       "      <th>육아</th>\n",
       "      <th>의학</th>\n",
       "      <th>인류/문명</th>\n",
       "      <th>인문</th>\n",
       "      <th>자연/생태</th>\n",
       "      <th>정치/사회</th>\n",
       "      <th>학교교육</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      가정  경제/경영  과학  교육  역사  예술/대중문화  육아  의학  인류/문명  인문  자연/생태  정치/사회  학교교육\n",
       "2005   2      1   2   2   2        1   2   1      2   2      2      2     2\n",
       "2006   3      3   3   3   3        3   2   3      3   3      3      3     2\n",
       "2007   3      3   3   3   3        3   4   3      3   3      3      3     3\n",
       "2008   6      7   7   7   6        7   6   7      7   7      7      6     7\n",
       "2009  14     13  13  13  14       13  13  13     13  13     13     14    13\n",
       "2010  13     13  13  13  12       13  13  13     13  13     13     12    13\n",
       "2011  12     12  12  12  13       12  13  12     12  12     12     13    12\n",
       "2012  13     13  13  13  12       13  12  13     13  13     13     13    13\n",
       "2013  13     13  13  13  13       13  13  13     13  13     13     12    13\n",
       "2014  13     13  13  13  14       13  14  13     13  13     13     14    13\n",
       "2015  12     12  12  12  12       12  12  12     12  12     12     12    13\n",
       "2016  14     15  14  14  14       15  14  15     14  14     14     14    14\n",
       "2017  13     13  13  13  13       13  13  13     14  13     14     13    13\n",
       "2018  14     14  15  15  14       14  14  14     14  15     14     14    14"
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearby_df = pd.DataFrame(count_f)\n",
    "yearby_df.index = year\n",
    "yearby_df.columns = big\n",
    "yearby_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(yearby_df, marker='o')\n",
    "plt.axis(xmin=2003, ymin=0)\n",
    "\n",
    "plt.title('연도별 카테고리 변화')\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('counts')\n",
    "plt.legend(yearby_df.columns.tolist(),loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>index</th>\n",
       "      <th>reco1</th>\n",
       "      <th>reco2</th>\n",
       "      <th>reco3</th>\n",
       "      <th>reco4</th>\n",
       "      <th>reco5</th>\n",
       "      <th>reco6</th>\n",
       "      <th>reco7</th>\n",
       "      <th>reco8</th>\n",
       "      <th>reco9</th>\n",
       "      <th>reco10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>380</td>\n",
       "      <td>784</td>\n",
       "      <td>382</td>\n",
       "      <td>783</td>\n",
       "      <td>384</td>\n",
       "      <td>383</td>\n",
       "      <td>780</td>\n",
       "      <td>785</td>\n",
       "      <td>379</td>\n",
       "      <td>779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>334</td>\n",
       "      <td>1462</td>\n",
       "      <td>233</td>\n",
       "      <td>952</td>\n",
       "      <td>1152</td>\n",
       "      <td>967</td>\n",
       "      <td>820</td>\n",
       "      <td>970</td>\n",
       "      <td>177</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>151</td>\n",
       "      <td>7</td>\n",
       "      <td>155</td>\n",
       "      <td>293</td>\n",
       "      <td>29</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>1527</td>\n",
       "      <td>1128</td>\n",
       "      <td>1131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>475</td>\n",
       "      <td>7</td>\n",
       "      <td>691</td>\n",
       "      <td>363</td>\n",
       "      <td>293</td>\n",
       "      <td>5</td>\n",
       "      <td>155</td>\n",
       "      <td>515</td>\n",
       "      <td>152</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>527</td>\n",
       "      <td>1163</td>\n",
       "      <td>366</td>\n",
       "      <td>1162</td>\n",
       "      <td>1160</td>\n",
       "      <td>54</td>\n",
       "      <td>367</td>\n",
       "      <td>360</td>\n",
       "      <td>531</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  reco1  reco2  reco3  reco4  reco5  reco6  reco7  reco8  reco9  \\\n",
       "0      1    380    784    382    783    384    383    780    785    379   \n",
       "1      2    334   1462    233    952   1152    967    820    970    177   \n",
       "2      3    151      7    155    293     29     54      6   1527   1128   \n",
       "3      4    475      7    691    363    293      5    155    515    152   \n",
       "4      5    527   1163    366   1162   1160     54    367    360    531   \n",
       "\n",
       "   reco10  \n",
       "0     779  \n",
       "1     391  \n",
       "2    1131  \n",
       "3     154  \n",
       "4     364  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommend = pd.read_csv(\"data/recommendation_result.csv\", encoding = 'utf-8')\n",
    "recommend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "jsondata = []\n",
    "    \n",
    "for i in range(len(recommend)):\n",
    "   \n",
    "    for_json= []\n",
    "\n",
    "    for_json.append('{')\n",
    "    for_json.append('\"index\" : \"{}\",'.format(recommend.loc[i]['index']))\n",
    "    \n",
    "    reco_list = []\n",
    "    reco_list.append(\"{}\".format(recommend.loc[i]['reco1']))\n",
    "    reco_list.append(\"{}\".format(recommend.loc[i]['reco2']))\n",
    "    reco_list.append(\"{}\".format(recommend.loc[i]['reco3']))\n",
    "    reco_list.append(\"{}\".format(recommend.loc[i]['reco4']))\n",
    "    reco_list.append(\"{}\".format(recommend.loc[i]['reco5']))\n",
    "    reco_list.append(\"{}\".format(recommend.loc[i]['reco6']))\n",
    "    reco_list.append(\"{}\".format(recommend.loc[i]['reco7']))\n",
    "    reco_list.append(\"{}\".format(recommend.loc[i]['reco8']))\n",
    "    reco_list.append(\"{}\".format(recommend.loc[i]['reco9']))\n",
    "    reco_list.append(\"{}\".format(recommend.loc[i]['reco10']))\n",
    "    \n",
    "    for_json.append('\"reco\" : \"{}\",'.format(reco_list))\n",
    "    for_json.append('}')\n",
    "    \n",
    "    for_json = ''.join(for_json)\n",
    "    \n",
    "    jsondata.append(for_json)\n",
    "\n",
    "jsondata = ','.join(jsondata)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"index\" : \"1\",\"reco\" : \"[\\'380\\', \\'784\\', \\'382\\', \\'783\\', \\'384\\', \\'383\\', \\'780\\', \\'785\\', \\'379\\', \\'779\\']\",},{'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jsondata[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"index\" : \"1\",\"reco\" : \"[\\'380\\', \\'784\\', \\'382\\', \\'783\\', \\'384\\', \\'383\\', \\'780\\', \\'785\\', \\'379\\', \\'779\\']\",},{\"index\" : \"2\",\"reco\" : \"[\\'334\\', \\'1462\\', \\'233\\', \\'952\\', \\'1152\\', \\'967\\', \\'820\\', \\'970\\', \\'177\\', \\'391\\']\",},{\"index\" : \"3\",\"reco\" : \"[\\'151\\', \\'7\\', \\'155\\', \\'293\\', \\'29\\', \\'54\\', \\'6\\', \\'1527\\', \\'1128\\', \\'1131\\']\",},{\"i'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template = \"\"\"[{{ content }}]\"\"\"\n",
    "template = template.replace('{{ content }}', jsondata)\n",
    "\n",
    "template[:300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Html_file = open(\"data/recommendation_result.json\",\"w\")\n",
    "Html_file.write(template)\n",
    "Html_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"index\" : \"1\",\"reco\" : \"[\\'380\\', \\'784\\', \\'382\\', \\'783\\', \\'384\\', \\'383\\', \\'780\\', \\'785\\', \\'379\\', \\'779\\']\",},{\"index\" : \"2\",\"reco\" : \"[\\'334\\', \\'1462\\', \\'233\\', \\'952\\', \\'1152\\', \\'967\\', \\'820\\', \\'970\\', \\'177\\', \\'391\\']\",},{\"index\" : \"3\",\"reco\" : \"[\\'151\\', \\'7\\', \\'155\\', \\'293\\', \\'29\\', \\'54\\', \\'6\\', \\'1527\\', \\'1128\\', \\'1131\\']\",},{\"i'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_data=open(\"data/recommendation_result.json\").read()\n",
    "json_data[:300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
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