research/240716_check_bookinfo2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 요약\n",
    "- 추가 전처리\n",
    "    - `00 형식의 연도를 숫자로 변환\n",
    "    - 특수문자 '／':'/' 로 변환\n",
    "    - 저자명에서 '000 외 지음'으로 되어있는 경우 '000 외'\n",
    "- mecab을 이용한 기본 설정 tokenize\n",
    "    - 이후 품사를 제한해보는 것도 필요\n",
    "- padding & integer encoding\n",
    "    - BName, BName_sub, Category에 적용\n",
    "    - maxlen은 각각 30,25,5로 ths를 정함\n",
    "\n",
    "- 이 과정에서 찾은 전처리 오류들은 code/process_bookinfo에 반영 완료\n",
    "    - 반영된 버전이 0.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib_inline.backend_inline\n",
    "\n",
    "# 그래프의 폰트 출력을 선명하게 (svg, retina 등이 있음)\n",
    "matplotlib_inline.backend_inline.set_matplotlib_formats(\"png2x\")\n",
    "# 테마 설정: \"default\", \"classic\", \"dark_background\", \"fivethirtyeight\", \"seaborn\"\n",
    "mpl.style.use(\"default\")\n",
    "# 이미지가 레이아웃 안으로 들어오도록 함\n",
    "# https://matplotlib.org/stable/users/explain/axes/constrainedlayout_guide.html\n",
    "mpl.rcParams.update({\"figure.constrained_layout.use\": True})\n",
    "\n",
    "#font, line, marker 등의 배율 설정: paper, notebook, talk, poster\n",
    "sns.set_context(\"paper\") \n",
    "#배색 설정: tab10, Set2, Accent, husl\n",
    "sns.set_palette(\"Set2\") \n",
    "#눈금, 배경, 격자 설정: ticks, white, whitegrid, dark, darkgrid\n",
    "# withegrid: 눈금을 그리고, 각 축의 눈금을 제거\n",
    "sns.set_style(\"whitegrid\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 로컬에서 \n",
    "\n",
    "plt.rc(\"font\", family = \"D2Coding\")\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, natsort, re\n",
    "from tqdm import tqdm\n",
    "import time, random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "PRJCT_PATH = '/home/doeun/code/AI/ESTSOFT2024/workspace/2.project_text/aladin_usedbook/'\n",
    "save_dir = 'processed/'\n",
    "file_name = f'bookinfo_ver0.5.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>BName</th>\n",
       "      <th>BName_sub</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Author</th>\n",
       "      <th>Author_mul</th>\n",
       "      <th>Publshr</th>\n",
       "      <th>Pdate</th>\n",
       "      <th>RglPrice</th>\n",
       "      <th>SlsPrice</th>\n",
       "      <th>SalesPoint</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sorce</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>322.0</td>\n",
       "      <td>민병철 생활영어 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54947</td>\n",
       "      <td>민병철</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>1979-10-01</td>\n",
       "      <td>5500</td>\n",
       "      <td>4950</td>\n",
       "      <td>177.0</td>\n",
       "      <td>외국어</td>\n",
       "      <td>2001년1월3주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>767.0</td>\n",
       "      <td>행복에의 초대</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168173</td>\n",
       "      <td>IVP(한국기독학생회출판부) 편집부</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>1979-12-01</td>\n",
       "      <td>2800</td>\n",
       "      <td>2660</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>종교/역학</td>\n",
       "      <td>2005년2월1주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>449.0</td>\n",
       "      <td>훈련으로 되는 제자</td>\n",
       "      <td>NaN</td>\n",
       "      <td>458277</td>\n",
       "      <td>월터 A. 헨릭슨</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>1980-12-01</td>\n",
       "      <td>4500</td>\n",
       "      <td>4050</td>\n",
       "      <td>552.0</td>\n",
       "      <td>종교/역학</td>\n",
       "      <td>2005년2월1주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>490.0</td>\n",
       "      <td>전태일 평전</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128759</td>\n",
       "      <td>조영래</td>\n",
       "      <td>False</td>\n",
       "      <td>돌베개</td>\n",
       "      <td>1983-06-01</td>\n",
       "      <td>6000</td>\n",
       "      <td>6000</td>\n",
       "      <td>409.0</td>\n",
       "      <td>사회과학</td>\n",
       "      <td>2001년1월3주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>300.0</td>\n",
       "      <td>제자의 삶</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131488</td>\n",
       "      <td>프랜시스 M.코스그로브</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>1984-07-01</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>종교/역학</td>\n",
       "      <td>2005년2월1주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9323</th>\n",
       "      <td>68.0</td>\n",
       "      <td>장송의 프리렌 12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>342597187</td>\n",
       "      <td>야마다 카네히토</td>\n",
       "      <td>True</td>\n",
       "      <td>학산문화사(만화)</td>\n",
       "      <td>2024-07-25</td>\n",
       "      <td>6000</td>\n",
       "      <td>5400</td>\n",
       "      <td>10530.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2024년7월2주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9324</th>\n",
       "      <td>138.0</td>\n",
       "      <td>약사의 혼잣말 13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>342597589</td>\n",
       "      <td>네코쿠라게</td>\n",
       "      <td>True</td>\n",
       "      <td>학산문화사(만화)</td>\n",
       "      <td>2024-07-25</td>\n",
       "      <td>6000</td>\n",
       "      <td>5400</td>\n",
       "      <td>5870.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2024년7월2주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9325</th>\n",
       "      <td>129.0</td>\n",
       "      <td>타몬 군 지금 어느 쪽?! 7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>342597714</td>\n",
       "      <td>시와스 유키</td>\n",
       "      <td>True</td>\n",
       "      <td>학산문화사(만화)</td>\n",
       "      <td>2024-07-25</td>\n",
       "      <td>6000</td>\n",
       "      <td>5400</td>\n",
       "      <td>6370.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2024년7월2주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9326</th>\n",
       "      <td>209.0</td>\n",
       "      <td>천재 의사 시건방 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>342388828</td>\n",
       "      <td>강효미</td>\n",
       "      <td>True</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2024-07-31</td>\n",
       "      <td>13000</td>\n",
       "      <td>11700</td>\n",
       "      <td>8190.0</td>\n",
       "      <td>어린이</td>\n",
       "      <td>2024년7월2주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9327</th>\n",
       "      <td>1.0</td>\n",
       "      <td>전지적 루이&amp;후이 시점</td>\n",
       "      <td>(양장본)</td>\n",
       "      <td>342594262</td>\n",
       "      <td>송영관</td>\n",
       "      <td>True</td>\n",
       "      <td>위즈덤하우스</td>\n",
       "      <td>2024-07-31</td>\n",
       "      <td>25000</td>\n",
       "      <td>22500</td>\n",
       "      <td>89930.0</td>\n",
       "      <td>에세이</td>\n",
       "      <td>2024년7월2주</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9328 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rank             BName BName_sub     ItemId               Author  \\\n",
       "0     322.0        민병철 생활영어 1       NaN      54947                  민병철   \n",
       "1     767.0           행복에의 초대       NaN     168173  IVP(한국기독학생회출판부) 편집부   \n",
       "2     449.0        훈련으로 되는 제자       NaN     458277            월터 A. 헨릭슨   \n",
       "3     490.0            전태일 평전       NaN     128759                  조영래   \n",
       "4     300.0             제자의 삶       NaN     131488         프랜시스 M.코스그로브   \n",
       "...     ...               ...       ...        ...                  ...   \n",
       "9323   68.0        장송의 프리렌 12       NaN  342597187             야마다 카네히토   \n",
       "9324  138.0        약사의 혼잣말 13       NaN  342597589                네코쿠라게   \n",
       "9325  129.0  타몬 군 지금 어느 쪽?! 7       NaN  342597714               시와스 유키   \n",
       "9326  209.0       천재 의사 시건방 1       NaN  342388828                  강효미   \n",
       "9327    1.0      전지적 루이&후이 시점     (양장본)  342594262                  송영관   \n",
       "\n",
       "      Author_mul    Publshr       Pdate  RglPrice  SlsPrice  SalesPoint  \\\n",
       "0          False     기타 출판사  1979-10-01      5500      4950       177.0   \n",
       "1          False     기타 출판사  1979-12-01      2800      2660      1960.0   \n",
       "2          False     기타 출판사  1980-12-01      4500      4050       552.0   \n",
       "3          False        돌베개  1983-06-01      6000      6000       409.0   \n",
       "4          False     기타 출판사  1984-07-01     12000     12000      1110.0   \n",
       "...          ...        ...         ...       ...       ...         ...   \n",
       "9323        True  학산문화사(만화)  2024-07-25      6000      5400     10530.0   \n",
       "9324        True  학산문화사(만화)  2024-07-25      6000      5400      5870.0   \n",
       "9325        True  학산문화사(만화)  2024-07-25      6000      5400      6370.0   \n",
       "9326        True     기타 출판사  2024-07-31     13000     11700      8190.0   \n",
       "9327        True     위즈덤하우스  2024-07-31     25000     22500     89930.0   \n",
       "\n",
       "     Category      Sorce  \n",
       "0         외국어  2001년1월3주  \n",
       "1       종교/역학  2005년2월1주  \n",
       "2       종교/역학  2005년2월1주  \n",
       "3        사회과학  2001년1월3주  \n",
       "4       종교/역학  2005년2월1주  \n",
       "...       ...        ...  \n",
       "9323       만화  2024년7월2주  \n",
       "9324       만화  2024년7월2주  \n",
       "9325       만화  2024년7월2주  \n",
       "9326      어린이  2024년7월2주  \n",
       "9327      에세이  2024년7월2주  \n",
       "\n",
       "[9328 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = os.path.join(PRJCT_PATH,save_dir,file_name)\n",
    "bookinfo = pd.read_csv(file_path)\n",
    "bookinfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"'07\", \"'08\", \"'09\"]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pat = r'\\'\\d\\d(?!\\d)'\n",
    "def detect_pattern(pat,text):\n",
    "    return re.findall(pat,text)\n",
    "\n",
    "text = \"'070에서 '07까지 '08-'09 \"\n",
    "detect_pattern(pat,text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"'070에서 2007까지 2008-2009 \""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def change_num2year(text):\n",
    "    pat = r'\\'\\d\\d(?!\\d)'\n",
    "    temp = sorted([m.start() for m in re.finditer(pat,text)])\n",
    "    rslt = list(text)\n",
    "    for i in temp[::-1]:\n",
    "        t = text[i+1:i+3]\n",
    "        digit = int(t)\n",
    "        if digit > 59 : rslt[i] = '19'\n",
    "        else : rslt[i] = '20'\n",
    "    return ''.join(rslt)\n",
    "\n",
    "change_num2year(text) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_by_dict(text,chr_dict):\n",
    "    for key,val in chr_dict.items():\n",
    "        text = text.replace(key,val)\n",
    "    return text\n",
    "\n",
    "special_chr = {\n",
    "  '&#xFF3C;' : '\\\\',\n",
    "  '／':'/',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "bookinfo['BName'] = bookinfo['BName'].apply(lambda x : replace_by_dict(x,special_chr))\n",
    "cond = ~bookinfo.BName_sub.isna()\n",
    "bookinfo.loc[cond,'BName_sub']=bookinfo.loc[cond,'BName_sub'].apply(lambda x : replace_by_dict(x,special_chr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "bookinfo.BName = bookinfo.BName.apply(change_num2year)\n",
    "cond = ~bookinfo.BName_sub.isna()\n",
    "bookinfo.loc[cond,'BName_sub']=bookinfo.loc[cond,'BName_sub'].apply(change_num2year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = bookinfo.Author == '박지원 외'\n",
    "np.sum(cond)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>BName</th>\n",
       "      <th>BName_sub</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Author</th>\n",
       "      <th>Author_mul</th>\n",
       "      <th>Publshr</th>\n",
       "      <th>Pdate</th>\n",
       "      <th>RglPrice</th>\n",
       "      <th>SlsPrice</th>\n",
       "      <th>SalesPoint</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sorce</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>435.0</td>\n",
       "      <td>호질.양반전.허생전 외</td>\n",
       "      <td>NaN</td>\n",
       "      <td>261957</td>\n",
       "      <td>박지원 외</td>\n",
       "      <td>True</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2000-12-25</td>\n",
       "      <td>6000</td>\n",
       "      <td>5400</td>\n",
       "      <td>2328.0</td>\n",
       "      <td>소설/시/희곡</td>\n",
       "      <td>2005년2월1주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>742.0</td>\n",
       "      <td>우리 고전소설 한마당</td>\n",
       "      <td>NaN</td>\n",
       "      <td>448997</td>\n",
       "      <td>박지원 외</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2003-12-17</td>\n",
       "      <td>12000</td>\n",
       "      <td>10800</td>\n",
       "      <td>2681.0</td>\n",
       "      <td>청소년</td>\n",
       "      <td>2011년12월5주</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rank         BName BName_sub  ItemId Author  Author_mul Publshr  \\\n",
       "1002  435.0  호질.양반전.허생전 외       NaN  261957  박지원 외        True  기타 출판사   \n",
       "2020  742.0   우리 고전소설 한마당       NaN  448997  박지원 외       False  기타 출판사   \n",
       "\n",
       "           Pdate  RglPrice  SlsPrice  SalesPoint Category       Sorce  \n",
       "1002  2000-12-25      6000      5400      2328.0  소설/시/희곡   2005년2월1주  \n",
       "2020  2003-12-17     12000     10800      2681.0      청소년  2011년12월5주  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bookinfo[cond]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**filtered_240711**\n",
    "- 780.0,국내도서,달라이 라마 지구의 희망을 말한다,260777,9788989197065,,\"텐진 갸초 외 지음, 오정숙 옮김\",롱셀러,2000-12-25,8800,7920,440점,94.0,종교/역학,2001년1월3주\n",
    "- 435.0,국내도서,호질.양반전.허생전 외,261957,9788908032156,4810.0,\"박지원 외 지음, 이민수 옮김\",범우사,2000-12-25,6000,5400,300점,2328.0,소설/시/희곡,2005년2월1주\n",
    "- 742.0,국내도서,우리 고전소설 한마당,448997,9788976700476,43810.0,박지원 외 지음,혜문서관,2003-12-17,12000,10800,600점,2681.0,청소년,2011년12월5주\n",
    "- 177.0,국내도서,노빈손의 으랏차차 중국 대장정,448875,9788958071914,3810.0,\"강영숙 외 지음, 이우일 그림\",뜨인돌,2003-12-18,9500,8550,470점,7006.0,청소년,2005년2월1주\n",
    "- 775.0,국내도서,생명 생물의 과학,411741,9788970855165,,\"Purves 외 지음, 이광웅 외 옮김\",교보문고(교재),2003-12-20,38000,38000,\"1,140점\",609.0,과학,2005년2월1주"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 크롤링으로 저자 정보를 가져오게 되면 안 생길 문제지만, 일단 추가 전처리 진행\n",
    "000 외 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "422"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_author = bookinfo.Author\n",
    "temp = book_author.str.split(' ').apply(lambda x : x[-1])\n",
    "cond = temp == '외'\n",
    "np.sum(cond)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37      질 들뢰즈 외\n",
       "41        현진건 외\n",
       "60        김영길 외\n",
       "67        민선식 외\n",
       "117       칼 융 외\n",
       "         ...   \n",
       "9184      안일구 외\n",
       "9191      이재석 외\n",
       "9272      박현도 외\n",
       "9296     제이포럼 외\n",
       "9311      김지용 외\n",
       "Name: Author, Length: 422, dtype: object"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_author[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = book_author[cond]\n",
    "temp = temp.str.split(' ').apply(lambda x : ' '.join(x[:-1]))\n",
    "bookinfo.loc[cond,'Author'] = temp\n",
    "bookinfo.loc[cond,'Author_mul'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37      질 들뢰즈\n",
       "41        현진건\n",
       "60        김영길\n",
       "67        민선식\n",
       "117       칼 융\n",
       "        ...  \n",
       "9184      안일구\n",
       "9191      이재석\n",
       "9272      박현도\n",
       "9296     제이포럼\n",
       "9311      김지용\n",
       "Name: Author, Length: 422, dtype: object"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bookinfo.loc[cond,'Author']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "000 외 00인 or 000 외 00명 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pat = r'\\s\\d+[인명]$'\n",
    "text = '안녕하세 외 인'\n",
    "detect_pattern(pat,text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' 10인']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[' 10명']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text1 = '안녕하세 외 10인'\n",
    "text2 = '안녕하세 외 10명'\n",
    "text3 = '안녕하세 외 2310쌍'\n",
    "display(detect_pattern(pat,text1))\n",
    "display(detect_pattern(pat,text2))\n",
    "display(detect_pattern(pat,text3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text1 = '안녕하세 외 10인'\n",
    "text2 = '안녕하세 외 10명'\n",
    "text3 = '안녕하세 외 2310쌍'\n",
    "display(detect_pattern(pat,text1) == True)\n",
    "display(detect_pattern(pat,text2) == True)\n",
    "display(detect_pattern(pat,text3) == True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text1 = '안녕하세 외 10인'\n",
    "text2 = '안녕하세 외 10명'\n",
    "text3 = '안녕하세 외 2310쌍'\n",
    "display('hello' if detect_pattern(pat,text1) else '')\n",
    "display('hello' if detect_pattern(pat,text2) else '')\n",
    "display('hello' if detect_pattern(pat,text3) else '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text1 = '안녕하세 외10인'\n",
    "text2 = '안녕하세 외 10명 그리고 고양이'\n",
    "text3 = '안녕하세 외 2310쌍17명'\n",
    "display(detect_pattern(pat,text1))\n",
    "display(detect_pattern(pat,text2))\n",
    "display(detect_pattern(pat,text3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pat1 = r'\\s\\d+[인명]$'\n",
    "pat2 = r'외\\s\\d+[인명]$'\n",
    "text1 = '안녕하세 외 10인'\n",
    "text2 = '안녕하세 외 10명'\n",
    "text3 = '안녕하세 2310명'\n",
    "display('hello' if detect_pattern(pat2,text1) else '')\n",
    "display('hello' if detect_pattern(pat2,text2) else '')\n",
    "display('hello' if detect_pattern(pat2,text3) else '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond1 = bookinfo.Author.apply(lambda x : True if detect_pattern(pat1,x) else False)\n",
    "cond2 = bookinfo.Author.apply(lambda x : True if detect_pattern(pat2,x) else False)\n",
    "\n",
    "np.sum(cond1 & (~cond2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = bookinfo.Author.apply(lambda x : re.sub(pat1,'',x))\n",
    "temp[cond1]\n",
    "\n",
    "bookinfo['Author'] = temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_author = bookinfo.Author\n",
    "temp = book_author.str.split(' ').apply(lambda x : x[-1])\n",
    "cond = temp == '외'\n",
    "temp = book_author[cond]\n",
    "temp = temp.str.split(' ').apply(lambda x : ' '.join(x[:-1]))\n",
    "bookinfo.loc[cond,'Author'] = temp\n",
    "bookinfo.loc[cond,'Author_mul'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1544        김호기\n",
       "3319         강풀\n",
       "3598        김어준\n",
       "4664        곽수인\n",
       "4857        박기혁\n",
       "5833    클라우스 슈밥\n",
       "6700        권기선\n",
       "Name: Author, dtype: object"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bookinfo.loc[cond1,'Author']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 기타 : 장 자끄 상뻬 글\n",
    "- 815.0,국내도서,얼굴 빨개지는 아이,194628,9788932902456,3860.0,\"장 자끄 상뻬 글 그림, 김호영 옮김\",열린책들,1999-05-30,8500,7650,420점,5262.0,에세이,2005년2월1주\n",
    "- 현재 사이트에는 지은이로 되어있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "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>Rank</th>\n",
       "      <th>BName</th>\n",
       "      <th>BName_sub</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Author</th>\n",
       "      <th>Author_mul</th>\n",
       "      <th>Publshr</th>\n",
       "      <th>Pdate</th>\n",
       "      <th>RglPrice</th>\n",
       "      <th>SlsPrice</th>\n",
       "      <th>SalesPoint</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sorce</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>815.0</td>\n",
       "      <td>얼굴 빨개지는 아이</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194628</td>\n",
       "      <td>장 자끄 상뻬 글</td>\n",
       "      <td>True</td>\n",
       "      <td>열린책들</td>\n",
       "      <td>1999-05-30</td>\n",
       "      <td>8500</td>\n",
       "      <td>7650</td>\n",
       "      <td>5262.0</td>\n",
       "      <td>에세이</td>\n",
       "      <td>2005년2월1주</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rank       BName BName_sub  ItemId     Author  Author_mul Publshr  \\\n",
       "284  815.0  얼굴 빨개지는 아이       NaN  194628  장 자끄 상뻬 글        True    열린책들   \n",
       "\n",
       "          Pdate  RglPrice  SlsPrice  SalesPoint Category      Sorce  \n",
       "284  1999-05-30      8500      7650      5262.0      에세이  2005년2월1주  "
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = bookinfo.Author == '장 자끄 상뻬 글'\n",
    "bookinfo[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "roles = [\n",
    "'글', '시', '역', '외',\n",
    "'제공',\n",
    "'소설',\n",
    "'극본',\n",
    "'강의',\n",
    "'사진',\n",
    "'구술',\n",
    "'정리',\n",
    "'엮음',\n",
    "'부록',\n",
    "'편저',\n",
    "'감수',\n",
    "'교열',\n",
    "'그림',\n",
    "'역주',\n",
    "'주해',\n",
    "'판화',\n",
    "'지음',\n",
    "'연출',\n",
    "'감역',\n",
    "'서문',\n",
    "'자문',\n",
    "'옮김',\n",
    "'편집',\n",
    "'평역',\n",
    "'만화',\n",
    "'사회',\n",
    "'추천',\n",
    "'해제',\n",
    "'각본',\n",
    "'저자',\n",
    "'각색',\n",
    "'원안',\n",
    "'역자',\n",
    "'영역',\n",
    "'해설',\n",
    "'구성',\n",
    "'요리',\n",
    "'기획',\n",
    "'원작',\n",
    "#'머리',\n",
    "'지도',\n",
    "'작곡','대담','편곡',\n",
    "'디자인','스토리','디렉터','풀어씀','총편집','엮고지음',\n",
    "'옮겨엮음',\n",
    "'일러스트',\n",
    "'자원집필',\n",
    "'책임편집',\n",
    "'소설구성',\n",
    "'시나리오',\n",
    "'글.사진',\n",
    "'콘티구성',\n",
    "'편찬책임',\n",
    "'글.그림',\n",
    "'글.삽화',\n",
    "'기획.제작',\n",
    "'동영상강의',\n",
    "'엮음.사진',\n",
    "#'관리위원회',\n",
    "'기획.채록',\n",
    "'편집·해설',\n",
    "'사진.캘리그라피'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_author1(content:str):\n",
    "    ele0 = content.split(',')[0].strip()\n",
    "    temp = ele0.split(' ')\n",
    "    if temp[-1] not in roles: return ele0\n",
    "    else : return ' '.join(temp[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = bookinfo['Author'].apply(extract_author1)\n",
    "cond = temp == bookinfo.Author\n",
    "np.sum(~cond)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "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>Rank</th>\n",
       "      <th>BName</th>\n",
       "      <th>BName_sub</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Author</th>\n",
       "      <th>Author_mul</th>\n",
       "      <th>Publshr</th>\n",
       "      <th>Pdate</th>\n",
       "      <th>RglPrice</th>\n",
       "      <th>SlsPrice</th>\n",
       "      <th>SalesPoint</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sorce</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>887.0</td>\n",
       "      <td>오행은 뭘까</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107339</td>\n",
       "      <td>전창선.어윤형</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>1994-06-01</td>\n",
       "      <td>7500</td>\n",
       "      <td>6750</td>\n",
       "      <td>991.0</td>\n",
       "      <td>인문학</td>\n",
       "      <td>2001년1월3주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>815.0</td>\n",
       "      <td>얼굴 빨개지는 아이</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194628</td>\n",
       "      <td>장 자끄 상뻬 글</td>\n",
       "      <td>True</td>\n",
       "      <td>열린책들</td>\n",
       "      <td>1999-05-30</td>\n",
       "      <td>8500</td>\n",
       "      <td>7650</td>\n",
       "      <td>5262.0</td>\n",
       "      <td>에세이</td>\n",
       "      <td>2005년2월1주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>450.0</td>\n",
       "      <td>박떡배와 오성과 한음</td>\n",
       "      <td>NaN</td>\n",
       "      <td>251722</td>\n",
       "      <td>박수동 글</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2000-10-10</td>\n",
       "      <td>8000</td>\n",
       "      <td>7200</td>\n",
       "      <td>2050.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2001년1월3주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>941.0</td>\n",
       "      <td>신의 나라 인간 나라</td>\n",
       "      <td>NaN</td>\n",
       "      <td>384590</td>\n",
       "      <td>이원복 글</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2002-10-28</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>6881.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2005년2월1주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>928.0</td>\n",
       "      <td>노빈손의 무인도 완전정복</td>\n",
       "      <td>NaN</td>\n",
       "      <td>474160</td>\n",
       "      <td>이우일 글</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2004-03-16</td>\n",
       "      <td>13000</td>\n",
       "      <td>11700</td>\n",
       "      <td>2953.0</td>\n",
       "      <td>청소년</td>\n",
       "      <td>2005년2월1주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2272</th>\n",
       "      <td>628.0</td>\n",
       "      <td>국경 없는 마을</td>\n",
       "      <td>NaN</td>\n",
       "      <td>527668</td>\n",
       "      <td>박채란 글</td>\n",
       "      <td>True</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2004-11-20</td>\n",
       "      <td>10900</td>\n",
       "      <td>9810</td>\n",
       "      <td>7082.0</td>\n",
       "      <td>사회과학</td>\n",
       "      <td>2005년2월1주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3231</th>\n",
       "      <td>915.0</td>\n",
       "      <td>유럽에서 꼭 가봐야 할 여행지 100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11980516</td>\n",
       "      <td>정보상 글</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2011-06-17</td>\n",
       "      <td>17800</td>\n",
       "      <td>16020</td>\n",
       "      <td>1825.0</td>\n",
       "      <td>여행</td>\n",
       "      <td>2013년4월4주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3414</th>\n",
       "      <td>174.0</td>\n",
       "      <td>어쿠스틱 라이프 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14285791</td>\n",
       "      <td>난다 글</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2011-11-26</td>\n",
       "      <td>11500</td>\n",
       "      <td>10350</td>\n",
       "      <td>2598.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2011년12월5주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534</th>\n",
       "      <td>469.0</td>\n",
       "      <td>두 번의 결혼식과 한 번의 장례식 Vol. 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14300064</td>\n",
       "      <td>박희정 글</td>\n",
       "      <td>True</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2011-12-22</td>\n",
       "      <td>8000</td>\n",
       "      <td>7200</td>\n",
       "      <td>502.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2011년12월5주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3746</th>\n",
       "      <td>894.0</td>\n",
       "      <td>의사는 수술받지 않는다</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21208170</td>\n",
       "      <td>김현정 글</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2012-11-17</td>\n",
       "      <td>15000</td>\n",
       "      <td>13500</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>건강/취미</td>\n",
       "      <td>2013년4월4주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3758</th>\n",
       "      <td>727.0</td>\n",
       "      <td>역전! 야매요리 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21247271</td>\n",
       "      <td>정다정 글</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2012-11-30</td>\n",
       "      <td>11000</td>\n",
       "      <td>9900</td>\n",
       "      <td>792.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2013년4월4주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3902</th>\n",
       "      <td>589.0</td>\n",
       "      <td>결혼해도 똑같네 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24017617</td>\n",
       "      <td>네온비 글</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2013-03-08</td>\n",
       "      <td>13500</td>\n",
       "      <td>12150</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2013년4월4주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3959</th>\n",
       "      <td>713.0</td>\n",
       "      <td>두 번의 결혼식과 한 번의 장례식 Vol. 3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24977708</td>\n",
       "      <td>박희정 글</td>\n",
       "      <td>True</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2013-03-20</td>\n",
       "      <td>8000</td>\n",
       "      <td>7200</td>\n",
       "      <td>324.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2013년4월4주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4087</th>\n",
       "      <td>157.0</td>\n",
       "      <td>역전! 야매요리 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25545860</td>\n",
       "      <td>정다정 글</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2013-04-08</td>\n",
       "      <td>11000</td>\n",
       "      <td>9900</td>\n",
       "      <td>409.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2013년4월4주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4214</th>\n",
       "      <td>310.0</td>\n",
       "      <td>두 번의 결혼식과 한 번의 장례식 Vol. 4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25917671</td>\n",
       "      <td>박희정 글</td>\n",
       "      <td>True</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2013-04-19</td>\n",
       "      <td>8000</td>\n",
       "      <td>7200</td>\n",
       "      <td>320.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2013년4월4주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4599</th>\n",
       "      <td>861.0</td>\n",
       "      <td>어쿠스틱 라이프 9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71098738</td>\n",
       "      <td>난다 글</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>13500</td>\n",
       "      <td>12150</td>\n",
       "      <td>2493.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2016년7월3주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5492</th>\n",
       "      <td>245.0</td>\n",
       "      <td>어쿠스틱 라이프 10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84658645</td>\n",
       "      <td>난다 글</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>14000</td>\n",
       "      <td>12600</td>\n",
       "      <td>2684.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2016년7월3주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5551</th>\n",
       "      <td>713.0</td>\n",
       "      <td>치즈 인 더 트랩 시즌 3 세트</td>\n",
       "      <td>(2)</td>\n",
       "      <td>86456650</td>\n",
       "      <td>순끼 글</td>\n",
       "      <td>False</td>\n",
       "      <td>기타 출판사</td>\n",
       "      <td>2016-06-22</td>\n",
       "      <td>33000</td>\n",
       "      <td>29700</td>\n",
       "      <td>667.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2016년7월3주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5604</th>\n",
       "      <td>834.0</td>\n",
       "      <td>치하야후루 28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86505800</td>\n",
       "      <td>스에츠구 유키 글</td>\n",
       "      <td>False</td>\n",
       "      <td>학산문화사(만화)</td>\n",
       "      <td>2016-06-28</td>\n",
       "      <td>4500</td>\n",
       "      <td>4050</td>\n",
       "      <td>374.0</td>\n",
       "      <td>만화</td>\n",
       "      <td>2016년7월3주</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rank                      BName BName_sub    ItemId     Author  \\\n",
       "59    887.0                     오행은 뭘까       NaN    107339   전창선.어윤형    \n",
       "284   815.0                 얼굴 빨개지는 아이       NaN    194628  장 자끄 상뻬 글   \n",
       "721   450.0                박떡배와 오성과 한음       NaN    251722      박수동 글   \n",
       "1864  941.0                신의 나라 인간 나라       NaN    384590      이원복 글   \n",
       "2071  928.0              노빈손의 무인도 완전정복       NaN    474160      이우일 글   \n",
       "2272  628.0                   국경 없는 마을       NaN    527668      박채란 글   \n",
       "3231  915.0       유럽에서 꼭 가봐야 할 여행지 100       NaN  11980516      정보상 글   \n",
       "3414  174.0                 어쿠스틱 라이프 2       NaN  14285791       난다 글   \n",
       "3534  469.0  두 번의 결혼식과 한 번의 장례식 Vol. 1       NaN  14300064      박희정 글   \n",
       "3746  894.0               의사는 수술받지 않는다       NaN  21208170      김현정 글   \n",
       "3758  727.0                 역전! 야매요리 1       NaN  21247271      정다정 글   \n",
       "3902  589.0                 결혼해도 똑같네 2       NaN  24017617      네온비 글   \n",
       "3959  713.0  두 번의 결혼식과 한 번의 장례식 Vol. 3       NaN  24977708      박희정 글   \n",
       "4087  157.0                 역전! 야매요리 2       NaN  25545860      정다정 글   \n",
       "4214  310.0  두 번의 결혼식과 한 번의 장례식 Vol. 4       NaN  25917671      박희정 글   \n",
       "4599  861.0                 어쿠스틱 라이프 9       NaN  71098738       난다 글   \n",
       "5492  245.0                어쿠스틱 라이프 10       NaN  84658645       난다 글   \n",
       "5551  713.0          치즈 인 더 트랩 시즌 3 세트       (2)  86456650       순끼 글   \n",
       "5604  834.0                   치하야후루 28       NaN  86505800  스에츠구 유키 글   \n",
       "\n",
       "      Author_mul    Publshr       Pdate  RglPrice  SlsPrice  SalesPoint  \\\n",
       "59         False     기타 출판사  1994-06-01      7500      6750       991.0   \n",
       "284         True       열린책들  1999-05-30      8500      7650      5262.0   \n",
       "721        False     기타 출판사  2000-10-10      8000      7200      2050.0   \n",
       "1864       False     기타 출판사  2002-10-28     10000      9000      6881.0   \n",
       "2071       False     기타 출판사  2004-03-16     13000     11700      2953.0   \n",
       "2272        True     기타 출판사  2004-11-20     10900      9810      7082.0   \n",
       "3231       False     기타 출판사  2011-06-17     17800     16020      1825.0   \n",
       "3414       False     기타 출판사  2011-11-26     11500     10350      2598.0   \n",
       "3534        True     기타 출판사  2011-12-22      8000      7200       502.0   \n",
       "3746       False     기타 출판사  2012-11-17     15000     13500      1688.0   \n",
       "3758       False     기타 출판사  2012-11-30     11000      9900       792.0   \n",
       "3902       False     기타 출판사  2013-03-08     13500     12150      1175.0   \n",
       "3959        True     기타 출판사  2013-03-20      8000      7200       324.0   \n",
       "4087       False     기타 출판사  2013-04-08     11000      9900       409.0   \n",
       "4214        True     기타 출판사  2013-04-19      8000      7200       320.0   \n",
       "4599       False     기타 출판사  2015-11-01     13500     12150      2493.0   \n",
       "5492       False     기타 출판사  2016-06-15     14000     12600      2684.0   \n",
       "5551       False     기타 출판사  2016-06-22     33000     29700       667.0   \n",
       "5604       False  학산문화사(만화)  2016-06-28      4500      4050       374.0   \n",
       "\n",
       "     Category       Sorce  \n",
       "59        인문학   2001년1월3주  \n",
       "284       에세이   2005년2월1주  \n",
       "721        만화   2001년1월3주  \n",
       "1864       만화   2005년2월1주  \n",
       "2071      청소년   2005년2월1주  \n",
       "2272     사회과학   2005년2월1주  \n",
       "3231       여행   2013년4월4주  \n",
       "3414       만화  2011년12월5주  \n",
       "3534       만화  2011년12월5주  \n",
       "3746    건강/취미   2013년4월4주  \n",
       "3758       만화   2013년4월4주  \n",
       "3902       만화   2013년4월4주  \n",
       "3959       만화   2013년4월4주  \n",
       "4087       만화   2013년4월4주  \n",
       "4214       만화   2013년4월4주  \n",
       "4599       만화   2016년7월3주  \n",
       "5492       만화   2016년7월3주  \n",
       "5551       만화   2016년7월3주  \n",
       "5604       만화   2016년7월3주  "
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bookinfo[~cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank               887.0\n",
       "BName             오행은 뭘까\n",
       "BName_sub            NaN\n",
       "ItemId            107339\n",
       "Author          전창선.어윤형 \n",
       "Author_mul         False\n",
       "Publshr           기타 출판사\n",
       "Pdate         1994-06-01\n",
       "RglPrice            7500\n",
       "SlsPrice            6750\n",
       "SalesPoint         991.0\n",
       "Category             인문학\n",
       "Sorce          2001년1월3주\n",
       "Name: 59, dtype: object"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bookinfo.loc[59]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'전창선.어윤형'"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_author1(bookinfo.loc[59,'Author'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59      전창선.어윤형\n",
       "284     장 자끄 상뻬\n",
       "721         박수동\n",
       "1864        이원복\n",
       "2071        이우일\n",
       "2272        박채란\n",
       "3231        정보상\n",
       "3414         난다\n",
       "3534        박희정\n",
       "3746        김현정\n",
       "3758        정다정\n",
       "3902        네온비\n",
       "3959        박희정\n",
       "4087        정다정\n",
       "4214        박희정\n",
       "4599         난다\n",
       "5492         난다\n",
       "5551         순끼\n",
       "5604    스에츠구 유키\n",
       "Name: Author, dtype: object"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[~cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from konlpy.tag import Mecab\n",
    "mecab = Mecab()\n",
    "tokenizer_basic = lambda x : mecab.morphs(x)\n",
    "\n",
    "# 출처: https://sikaleo.tistory.com/62 [SIKALEO:티스토리]\n",
    "def tokenizer_kor(raw, pos=[\"NNG\",\"NNP\",\"VV\",\"VA\"]):\n",
    "    from konlpy.tag import Mecab\n",
    "    m = Mecab()\n",
    "    # 길이가 1 이하인 토근은 제외, 위에서 지정한 (mecab 사전에 따른) 토큰들만 특징으로 삼기, 불용어 100개 제외  \n",
    "    return [word for word, tag in m.pos(raw) if tag in pos]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_name,book_subname, category = bookinfo.BName, bookinfo.BName_sub, bookinfo.Category\n",
    "book_name = book_name.apply(tokenizer_basic)\n",
    "book_subname = book_subname.fillna('').apply(tokenizer_basic)\n",
    "category = category.apply(tokenizer_basic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Integer encoding\n",
    "- 빈도순으로 정렬하여 가장 많이 나온 token을 1로 시작해서, 양의 정수로 인코딩 \n",
    "- 길이 통일을 위한 ```'[PAD]'```는 0으로 encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84183"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import itertools\n",
    "tokens = np.array(list(itertools.chain(*book_name.values,*book_subname.values,*category.values)))\n",
    "len(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['!', '$', '%', ..., '老子', '漏泄', '六'], dtype='<U14'),\n",
       " array([190,   1,  15, ...,   1,   1,   1]))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "8541"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corpus = np.unique(tokens,return_counts=True)\n",
    "display(corpus)\n",
    "display(len(corpus[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>token</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>%</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&amp;</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8536</th>\n",
       "      <td>힙합</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8537</th>\n",
       "      <td>洛陽</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8538</th>\n",
       "      <td>老子</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8539</th>\n",
       "      <td>漏泄</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8540</th>\n",
       "      <td>六</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8541 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     token counts\n",
       "0        !    190\n",
       "1        $      1\n",
       "2        %     15\n",
       "3        &     48\n",
       "4        '     24\n",
       "...    ...    ...\n",
       "8536    힙합      2\n",
       "8537    洛陽      1\n",
       "8538    老子      1\n",
       "8539    漏泄      1\n",
       "8540     六      1\n",
       "\n",
       "[8541 rows x 2 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corpus = pd.DataFrame(corpus).T\n",
    "df_corpus = df_corpus.rename(columns={0:'token',1:'counts'})\n",
    "df_corpus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 보통 단어 개수를 32000개쯤 한다는 것 같은데(부정확) 9000개 정도면 크지 않은 것 같음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>token</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>07</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>08</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8535</th>\n",
       "      <td>힙</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8537</th>\n",
       "      <td>洛陽</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8538</th>\n",
       "      <td>老子</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8539</th>\n",
       "      <td>漏泄</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8540</th>\n",
       "      <td>六</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4413 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     token counts\n",
       "1        $      1\n",
       "16     001      1\n",
       "18     014      1\n",
       "20      07      1\n",
       "21      08      1\n",
       "...    ...    ...\n",
       "8535     힙      1\n",
       "8537    洛陽      1\n",
       "8538    老子      1\n",
       "8539    漏泄      1\n",
       "8540     六      1\n",
       "\n",
       "[4413 rows x 2 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = df_corpus.counts < 2\n",
    "df_corpus[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Rank</th>\n",
       "      <th>BName</th>\n",
       "      <th>BName_sub</th>\n",
       "      <th>ItemId</th>\n",
       "      <th>Author</th>\n",
       "      <th>Author_mul</th>\n",
       "      <th>Publshr</th>\n",
       "      <th>Pdate</th>\n",
       "      <th>RglPrice</th>\n",
       "      <th>SlsPrice</th>\n",
       "      <th>SalesPoint</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sorce</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Rank, BName, BName_sub, ItemId, Author, Author_mul, Publshr, Pdate, RglPrice, SlsPrice, SalesPoint, Category, Sorce]\n",
       "Index: []"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets = [\n",
    "\"／\"]\n",
    "detect_target = lambda x : len(set(x).intersection(set(targets))) > 0\n",
    "cond = book_name.apply(detect_target)\n",
    "bookinfo[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>counts</th>\n",
       "      <th>val</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>token</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>/</th>\n",
       "      <td>5608</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>의</th>\n",
       "      <td>2135</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>시</th>\n",
       "      <td>1857</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>소설</th>\n",
       "      <td>1769</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>희곡</th>\n",
       "      <td>1735</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사이키</th>\n",
       "      <td>1</td>\n",
       "      <td>8537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사이언스</th>\n",
       "      <td>1</td>\n",
       "      <td>8538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사이보그</th>\n",
       "      <td>1</td>\n",
       "      <td>8539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>사이다</th>\n",
       "      <td>1</td>\n",
       "      <td>8540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>六</th>\n",
       "      <td>1</td>\n",
       "      <td>8541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8541 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      counts   val\n",
       "token             \n",
       "/       5608     1\n",
       "의       2135     2\n",
       "시       1857     3\n",
       "소설      1769     4\n",
       "희곡      1735     5\n",
       "...      ...   ...\n",
       "사이키        1  8537\n",
       "사이언스       1  8538\n",
       "사이보그       1  8539\n",
       "사이다        1  8540\n",
       "六          1  8541\n",
       "\n",
       "[8541 rows x 2 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df_corpus.sort_values(by='counts',ascending=False)\n",
    "temp['val'] = np.arange(len(temp))+1\n",
    "temp = temp.set_index('token')\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'/': 1,\n",
       " '의': 2,\n",
       " '시': 3,\n",
       " '소설': 4,\n",
       " '희곡': 5,\n",
       " '는': 6,\n",
       " '만화': 7,\n",
       " '(': 8,\n",
       " ')': 9,\n",
       " '수험서': 10,\n",
       " '자격증': 11,\n",
       " '1': 12,\n",
       " '은': 13,\n",
       " '2': 14,\n",
       " '인문학': 15,\n",
       " '경제': 16,\n",
       " '경영': 17,\n",
       " '에세이': 18,\n",
       " '하': 19,\n",
       " '외국어': 20,\n",
       " '한': 21,\n",
       " '과학': 22,\n",
       " '이': 23,\n",
       " '자기': 24,\n",
       " '을': 25,\n",
       " '계발': 26,\n",
       " '-': 27,\n",
       " ',': 28,\n",
       " '3': 29,\n",
       " '+': 30,\n",
       " '다': 31,\n",
       " '사회': 32,\n",
       " '권': 33,\n",
       " '를': 34,\n",
       " '좋': 35,\n",
       " '나': 36,\n",
       " '가': 37,\n",
       " '컴퓨터': 38,\n",
       " '기': 39,\n",
       " '에': 40,\n",
       " '부모': 41,\n",
       " '전': 42,\n",
       " '종교': 43,\n",
       " '역학': 44,\n",
       " '.': 45,\n",
       " ':': 46,\n",
       " '모바일': 47,\n",
       " '해': 48,\n",
       " '역사': 49,\n",
       " '들': 50,\n",
       " '청소년': 51,\n",
       " '지': 52,\n",
       " '고': 53,\n",
       " '급': 54,\n",
       " '예술': 55,\n",
       " '4': 56,\n",
       " '건강': 57,\n",
       " '문화': 58,\n",
       " '대중': 59,\n",
       " '커스': 60,\n",
       " '영어': 61,\n",
       " '기출': 62,\n",
       " '과': 63,\n",
       " '취미': 64,\n",
       " '요리': 65,\n",
       " '2016': 66,\n",
       " '!': 67,\n",
       " '문제집': 68,\n",
       " '살림': 69,\n",
       " '와': 70,\n",
       " '로': 71,\n",
       " '능력': 72,\n",
       " '5': 73,\n",
       " '있': 74,\n",
       " '2019': 75,\n",
       " '제': 76,\n",
       " '토익': 77,\n",
       " '일': 78,\n",
       " '으로': 79,\n",
       " '7': 80,\n",
       " '내': 81,\n",
       " '읽': 82,\n",
       " '세트': 83,\n",
       " '실전': 84,\n",
       " '게': 85,\n",
       " '아이': 86,\n",
       " '도': 87,\n",
       " '않': 88,\n",
       " '말': 89,\n",
       " '이야기': 90,\n",
       " '만': 91,\n",
       " '시험': 92,\n",
       " '10': 93,\n",
       " '어': 94,\n",
       " '편': 95,\n",
       " '에서': 96,\n",
       " '필기': 97,\n",
       " '여행': 98,\n",
       " '모의고사': 99,\n",
       " '것': 100,\n",
       " '2022': 101,\n",
       " '적': 102,\n",
       " '6': 103,\n",
       " '판': 104,\n",
       " '사람': 105,\n",
       " '공부': 106,\n",
       " '세계': 107,\n",
       " '위한': 108,\n",
       " '사랑': 109,\n",
       " '8': 110,\n",
       " '국어': 111,\n",
       " '아': 112,\n",
       " '우리': 113,\n",
       " '국사': 114,\n",
       " '?': 115,\n",
       " '년': 116,\n",
       " '별': 117,\n",
       " '책': 118,\n",
       " '집': 119,\n",
       " '서': 120,\n",
       " '교재': 121,\n",
       " '살': 122,\n",
       " '법': 123,\n",
       " '되': 124,\n",
       " '신': 125,\n",
       " '없': 126,\n",
       " '에게': 127,\n",
       " '라': 128,\n",
       " '특별': 129,\n",
       " '인생': 130,\n",
       " '노트': 131,\n",
       " '주': 132,\n",
       " '어린이': 133,\n",
       " '2021': 134,\n",
       " '부': 135,\n",
       " '9': 136,\n",
       " '에디션': 137,\n",
       " '100': 138,\n",
       " '기본': 139,\n",
       " '스': 140,\n",
       " '가지': 141,\n",
       " '엄마': 142,\n",
       " '상': 143,\n",
       " '공무원': 144,\n",
       " '투자': 145,\n",
       " '보': 146,\n",
       " '할': 147,\n",
       " '사': 148,\n",
       " '끝내': 149,\n",
       " '당신': 150,\n",
       " '남자': 151,\n",
       " '활용': 152,\n",
       " '그': 153,\n",
       " '대학': 154,\n",
       " '세상': 155,\n",
       " '나공': 156,\n",
       " '었': 157,\n",
       " '문제': 158,\n",
       " '전문': 159,\n",
       " '리': 160,\n",
       " '서적': 161,\n",
       " '1000': 162,\n",
       " '양장': 163,\n",
       " '알': 164,\n",
       " '최신': 165,\n",
       " '대비': 166,\n",
       " '너': 167,\n",
       " '싶': 168,\n",
       " '수': 169,\n",
       " '~': 170,\n",
       " '답': 171,\n",
       " '한다': 172,\n",
       " 'TOEIC': 173,\n",
       " '생각': 174,\n",
       " '인': 175,\n",
       " '는가': 176,\n",
       " '모든': 177,\n",
       " '한국사': 178,\n",
       " '자': 179,\n",
       " '기사': 180,\n",
       " '검정': 181,\n",
       " '면': 182,\n",
       " '네': 183,\n",
       " '2025': 184,\n",
       " '한국': 185,\n",
       " '시간': 186,\n",
       " '마음': 187,\n",
       " 'Reading': 188,\n",
       " '실기': 189,\n",
       " '윌': 190,\n",
       " '돈': 191,\n",
       " '리딩': 192,\n",
       " '쓰': 193,\n",
       " 'Hackers': 194,\n",
       " '행정법': 195,\n",
       " '부자': 196,\n",
       " '개': 197,\n",
       " '12': 198,\n",
       " '정리': 199,\n",
       " '에듀': 200,\n",
       " '커버': 201,\n",
       " '따라': 202,\n",
       " '꽃': 203,\n",
       " '시작': 204,\n",
       " '일본어': 205,\n",
       " '기출문제': 206,\n",
       " '2024': 207,\n",
       " '번': 208,\n",
       " '해설집': 209,\n",
       " '인간': 210,\n",
       " '행정학': 211,\n",
       " '힘': 212,\n",
       " '분': 213,\n",
       " '삶': 214,\n",
       " '주식': 215,\n",
       " '&': 216,\n",
       " '비밀': 217,\n",
       " '대': 218,\n",
       " '행복': 219,\n",
       " 'Listening': 220,\n",
       " '2017': 221,\n",
       " '님': 222,\n",
       " '습니다': 223,\n",
       " '기초': 224,\n",
       " '핵심': 225,\n",
       " '부동산': 226,\n",
       " '인가': 227,\n",
       " '완성': 228,\n",
       " '테이프': 229,\n",
       " '왜': 230,\n",
       " '여자': 231,\n",
       " 'NCS': 232,\n",
       " '했': 233,\n",
       " '여': 234,\n",
       " '교과서': 235,\n",
       " 'RC': 236,\n",
       " '합격': 237,\n",
       " '0': 238,\n",
       " '배우': 239,\n",
       " '북': 240,\n",
       " '세': 241,\n",
       " '그림': 242,\n",
       " '호': 243,\n",
       " '형': 244,\n",
       " '어떻게': 245,\n",
       " '총론': 246,\n",
       " '더': 247,\n",
       " '보다': 248,\n",
       " '잘': 249,\n",
       " '15': 250,\n",
       " '미래': 251,\n",
       " '무작정': 252,\n",
       " '처럼': 253,\n",
       " '강의': 254,\n",
       " '원': 255,\n",
       " '밥': 256,\n",
       " 'TOEFL': 257,\n",
       " '꿈': 258,\n",
       " '삼국지': 259,\n",
       " 'LC': 260,\n",
       " '포함': 261,\n",
       " '수업': 262,\n",
       " '세계사': 263,\n",
       " '리스닝': 264,\n",
       " '첫': 265,\n",
       " '수학': 266,\n",
       " 'ETS': 267,\n",
       " '는다': 268,\n",
       " '사전': 269,\n",
       " '11': 270,\n",
       " '빵': 271,\n",
       " '기념': 272,\n",
       " '중': 273,\n",
       " '성공': 274,\n",
       " '노': 275,\n",
       " '야': 276,\n",
       " '안': 277,\n",
       " '기술': 278,\n",
       " '회': 279,\n",
       " '도쿄': 280,\n",
       " '토플': 281,\n",
       " '위': 282,\n",
       " '유아': 283,\n",
       " '별매': 284,\n",
       " '회화': 285,\n",
       " '기적': 286,\n",
       " '오늘': 287,\n",
       " '오': 288,\n",
       " '시대': 289,\n",
       " '바람': 290,\n",
       " '성': 291,\n",
       " '밤': 292,\n",
       " '습관': 293,\n",
       " '법칙': 294,\n",
       " '단기': 295,\n",
       " '30': 296,\n",
       " '20': 297,\n",
       " '한글': 298,\n",
       " '달': 299,\n",
       " '하루': 300,\n",
       " '다시': 301,\n",
       " '두': 302,\n",
       " '정보': 303,\n",
       " 'MP': 304,\n",
       " '초등': 305,\n",
       " '선': 306,\n",
       " '봉투': 307,\n",
       " '쌤': 308,\n",
       " '14': 309,\n",
       " '저': 310,\n",
       " '경찰': 311,\n",
       " '선재': 312,\n",
       " '문법': 313,\n",
       " '글쓰기': 314,\n",
       " '종': 315,\n",
       " '2013': 316,\n",
       " '만들': 317,\n",
       " 'CD': 318,\n",
       " '죽': 319,\n",
       " '공식': 320,\n",
       " '차': 321,\n",
       " '경제학': 322,\n",
       " '먹': 323,\n",
       " '생': 324,\n",
       " '보이': 325,\n",
       " '여름': 326,\n",
       " '13': 327,\n",
       " '날': 328,\n",
       " '이론': 329,\n",
       " '무엇': 330,\n",
       " '무료': 331,\n",
       " '귀': 332,\n",
       " '몸': 333,\n",
       " '평가': 334,\n",
       " '처리': 335,\n",
       " '문학': 336,\n",
       " '순간': 337,\n",
       " '끝': 338,\n",
       " '아빠': 339,\n",
       " '회분': 340,\n",
       " '철학': 341,\n",
       " '성경': 342,\n",
       " '맨': 343,\n",
       " '찾': 344,\n",
       " '된': 345,\n",
       " '혼자': 346,\n",
       " '영문법': 347,\n",
       " '왕': 348,\n",
       " '길': 349,\n",
       " '총': 350,\n",
       " '반양장': 351,\n",
       " '교육학': 352,\n",
       " '생활': 353,\n",
       " '아름다운': 354,\n",
       " '한길': 355,\n",
       " '후': 356,\n",
       " '고사': 357,\n",
       " '단어': 358,\n",
       " '예수': 359,\n",
       " '장': 360,\n",
       " '던': 361,\n",
       " '16': 362,\n",
       " '독해': 363,\n",
       " '화': 364,\n",
       " '웹': 365,\n",
       " '이렇게': 366,\n",
       " '학교': 367,\n",
       " '백': 368,\n",
       " '심리학': 369,\n",
       " '큰': 370,\n",
       " 'Vol': 371,\n",
       " '신용': 372,\n",
       " '신화': 373,\n",
       " '작': 374,\n",
       " '비': 375,\n",
       " '입니다': 376,\n",
       " '직무': 377,\n",
       " 'ACL': 378,\n",
       " '마': 379,\n",
       " '스페셜': 380,\n",
       " '함께': 381,\n",
       " '아야': 382,\n",
       " '받': 383,\n",
       " '가장': 384,\n",
       " '이큐': 385,\n",
       " '암기': 386,\n",
       " '2012': 387,\n",
       " '동형모': 388,\n",
       " '예상': 389,\n",
       " '혁명': 390,\n",
       " '지식': 391,\n",
       " '쉬운': 392,\n",
       " '워드': 393,\n",
       " '개정판': 394,\n",
       " '째': 395,\n",
       " '21': 396,\n",
       " '세요': 397,\n",
       " '동영상': 398,\n",
       " '지금': 399,\n",
       " '해설': 400,\n",
       " '칼날': 401,\n",
       " '쉽': 402,\n",
       " '고양이': 403,\n",
       " '돌': 404,\n",
       " '부터': 405,\n",
       " '군': 406,\n",
       " '최태성': 407,\n",
       " '하나': 408,\n",
       " '일기': 409,\n",
       " '어휘': 410,\n",
       " '나무': 411,\n",
       " '실': 412,\n",
       " '나라': 413,\n",
       " '걸음': 414,\n",
       " '뇌': 415,\n",
       " '리벤': 416,\n",
       " '주술': 417,\n",
       " '회전': 418,\n",
       " \"'\": 419,\n",
       " '이기': 420,\n",
       " '검사': 421,\n",
       " '연습': 422,\n",
       " '무': 423,\n",
       " '언어': 424,\n",
       " '았': 425,\n",
       " '고급': 426,\n",
       " '위대': 427,\n",
       " '멸': 428,\n",
       " '최': 429,\n",
       " '용': 430,\n",
       " '매일': 431,\n",
       " '죽음': 432,\n",
       " '된다': 433,\n",
       " '무선': 434,\n",
       " '레시피': 435,\n",
       " '으면': 436,\n",
       " '문': 437,\n",
       " '때': 438,\n",
       " '지도': 439,\n",
       " '론': 440,\n",
       " '전쟁': 441,\n",
       " '겠': 442,\n",
       " '소년': 443,\n",
       " '상식': 444,\n",
       " '요약': 445,\n",
       " '초보': 446,\n",
       " '면서': 447,\n",
       " '직업': 448,\n",
       " '하나님': 449,\n",
       " '엑셀': 450,\n",
       " '이동기': 451,\n",
       " '17': 452,\n",
       " '육아': 453,\n",
       " '스프링': 454,\n",
       " '게임': 455,\n",
       " '소녀': 456,\n",
       " '아주': 457,\n",
       " '문장': 458,\n",
       " '헌법': 459,\n",
       " '프로세서': 460,\n",
       " '정판': 461,\n",
       " 'EBS': 462,\n",
       " '씨': 463,\n",
       " '전략': 464,\n",
       " '분석': 465,\n",
       " '방법': 466,\n",
       " '유형': 467,\n",
       " '끝장': 468,\n",
       " '다이어트': 469,\n",
       " '작품': 470,\n",
       " '대한민국': 471,\n",
       " '심화': 472,\n",
       " '초판본': 473,\n",
       " '코': 474,\n",
       " 'Test': 475,\n",
       " 'PSAT': 476,\n",
       " '최고': 477,\n",
       " '산업': 478,\n",
       " '형법': 479,\n",
       " '트': 480,\n",
       " '독학': 481,\n",
       " '을까': 482,\n",
       " '마지막': 483,\n",
       " '합본': 484,\n",
       " '대화': 485,\n",
       " '심리': 486,\n",
       " '구': 487,\n",
       " '텝': 488,\n",
       " '해리': 489,\n",
       " '바꾸': 490,\n",
       " 'N': 491,\n",
       " '실무': 492,\n",
       " '합니다': 493,\n",
       " 'English': 494,\n",
       " '형사': 495,\n",
       " '천재': 496,\n",
       " '적성': 497,\n",
       " '만드': 498,\n",
       " '난': 499,\n",
       " '제공': 500,\n",
       " '마케팅': 501,\n",
       " '동화': 502,\n",
       " '포터': 503,\n",
       " '숲': 504,\n",
       " '300': 505,\n",
       " '아니': 506,\n",
       " '배': 507,\n",
       " '50': 508,\n",
       " '노래': 509,\n",
       " '속': 510,\n",
       " '란': 511,\n",
       " '소송법': 512,\n",
       " '러브': 513,\n",
       " '마스터': 514,\n",
       " '디자인': 515,\n",
       " '영': 516,\n",
       " 'iBT': 517,\n",
       " '최종': 518,\n",
       " '젊': 519,\n",
       " '친구': 520,\n",
       " '괴수': 521,\n",
       " '완벽': 522,\n",
       " 'The': 523,\n",
       " '누구': 524,\n",
       " '유럽': 525,\n",
       " '장판': 526,\n",
       " '김중근': 527,\n",
       " '18': 528,\n",
       " '어라': 529,\n",
       " '라이프': 530,\n",
       " '담사': 531,\n",
       " '종합': 532,\n",
       " '드': 533,\n",
       " '처음': 534,\n",
       " '고전': 535,\n",
       " '청춘': 536,\n",
       " '써니': 537,\n",
       " '걷': 538,\n",
       " '이름': 539,\n",
       " '2023': 540,\n",
       " '하늘': 541,\n",
       " '개년': 542,\n",
       " '열': 543,\n",
       " '손': 544,\n",
       " '데이즈': 545,\n",
       " '사카모토': 546,\n",
       " '니': 547,\n",
       " '도시': 548,\n",
       " '키우': 549,\n",
       " '한정판': 550,\n",
       " '건': 551,\n",
       " '입': 552,\n",
       " '괜찮': 553,\n",
       " '지만': 554,\n",
       " '개념': 555,\n",
       " '일러스트': 556,\n",
       " '불': 557,\n",
       " '포인트': 558,\n",
       " '미국': 559,\n",
       " '중국': 560,\n",
       " '어디': 561,\n",
       " '통합': 562,\n",
       " '해커': 563,\n",
       " '어서': 564,\n",
       " '풀': 565,\n",
       " '딸': 566,\n",
       " '컬러': 567,\n",
       " '군무원': 568,\n",
       " '너무': 569,\n",
       " '기도': 570,\n",
       " '표현': 571,\n",
       " '체': 572,\n",
       " '교실': 573,\n",
       " '균': 574,\n",
       " '진짜': 575,\n",
       " '해석': 576,\n",
       " '아기': 577,\n",
       " '그리고': 578,\n",
       " '선생': 579,\n",
       " '지혜': 580,\n",
       " '%': 581,\n",
       " 'One': 582,\n",
       " '스타트': 583,\n",
       " '및': 584,\n",
       " '개정': 585,\n",
       " '사건': 586,\n",
       " '★': 587,\n",
       " '캐릭터': 588,\n",
       " '눈': 589,\n",
       " '한자': 590,\n",
       " '민준호': 591,\n",
       " '중급': 592,\n",
       " '2000': 593,\n",
       " '새': 594,\n",
       " '세기': 595,\n",
       " '19': 596,\n",
       " '작가': 597,\n",
       " '스토리': 598,\n",
       " 'Compass': 599,\n",
       " '땅': 600,\n",
       " '토마토': 601,\n",
       " '국민': 602,\n",
       " '월': 603,\n",
       " '관리': 604,\n",
       " '만나': 605,\n",
       " '아라': 606,\n",
       " '듣': 607,\n",
       " '교육': 608,\n",
       " '연애': 609,\n",
       " '필요': 610,\n",
       " '신판': 611,\n",
       " '식': 612,\n",
       " '맛있': 613,\n",
       " 'TEPS': 614,\n",
       " '별한': 615,\n",
       " '시사': 616,\n",
       " '바다': 617,\n",
       " '걸': 618,\n",
       " '감': 619,\n",
       " '2018': 620,\n",
       " '보카': 621,\n",
       " 'New': 622,\n",
       " '진실': 623,\n",
       " '온': 624,\n",
       " '사이': 625,\n",
       " '질문': 626,\n",
       " '단편': 627,\n",
       " '사용': 628,\n",
       " '선물': 629,\n",
       " '온라인': 630,\n",
       " '산책': 631,\n",
       " '2007': 632,\n",
       " '어요': 633,\n",
       " '영문': 634,\n",
       " '공기업': 635,\n",
       " '까지': 636,\n",
       " '경매': 637,\n",
       " 'Start': 638,\n",
       " 'VOCA': 639,\n",
       " 'JLPT': 640,\n",
       " '봄': 641,\n",
       " '다이어리': 642,\n",
       " '그릇': 643,\n",
       " '영혼': 644,\n",
       " '지구': 645,\n",
       " '원피스': 646,\n",
       " '시크릿': 647,\n",
       " '미술': 648,\n",
       " '인소': 649,\n",
       " '손진숙': 650,\n",
       " '절': 651,\n",
       " '전효진': 652,\n",
       " '로마인': 653,\n",
       " '전동': 654,\n",
       " '거': 655,\n",
       " '수행': 656,\n",
       " '시즌': 657,\n",
       " '국가': 658,\n",
       " '바': 659,\n",
       " '로드': 660,\n",
       " '겨울': 661,\n",
       " '바보': 662,\n",
       " '정석': 663,\n",
       " '이유식': 664,\n",
       " '뭐': 665,\n",
       " '멋진': 666,\n",
       " '독서': 667,\n",
       " 'Edition': 668,\n",
       " '이상': 669,\n",
       " '맛': 670,\n",
       " '반': 671,\n",
       " 'Basic': 672,\n",
       " '좀': 673,\n",
       " '비즈니스': 674,\n",
       " '공사': 675,\n",
       " '회사': 676,\n",
       " '미': 677,\n",
       " '본': 678,\n",
       " '빈손': 679,\n",
       " '공인': 680,\n",
       " '발견': 681,\n",
       " '방': 682,\n",
       " 'Vocabulary': 683,\n",
       " '공략': 684,\n",
       " '그녀': 685,\n",
       " '애': 686,\n",
       " '단원': 687,\n",
       " '절대': 688,\n",
       " '히어로': 689,\n",
       " '즐거움': 690,\n",
       " '결정': 691,\n",
       " '기업': 692,\n",
       " '그림책': 693,\n",
       " '꼭': 694,\n",
       " '28': 695,\n",
       " '정기': 696,\n",
       " '원칙': 697,\n",
       " '논리': 698,\n",
       " '아카데미': 699,\n",
       " 'Intermediate': 700,\n",
       " '뉴': 701,\n",
       " '줄': 702,\n",
       " '고백': 703,\n",
       " '오래': 704,\n",
       " '프로젝트': 705,\n",
       " '종말': 706,\n",
       " '외전': 707,\n",
       " '용사': 708,\n",
       " '피': 709,\n",
       " '운명': 710,\n",
       " '조선': 711,\n",
       " '안녕': 712,\n",
       " '결혼': 713,\n",
       " '학습': 714,\n",
       " '그리스': 715,\n",
       " '365': 716,\n",
       " '삼성': 717,\n",
       " '프': 718,\n",
       " '태교': 719,\n",
       " '새벽': 720,\n",
       " '마법': 721,\n",
       " '셜록': 722,\n",
       " '500': 723,\n",
       " '개론': 724,\n",
       " '전집': 725,\n",
       " '평전': 726,\n",
       " '기능사': 727,\n",
       " '홈즈': 728,\n",
       " '대하': 729,\n",
       " '한강': 730,\n",
       " '출제': 731,\n",
       " '편지': 732,\n",
       " '초': 733,\n",
       " '고종훈': 734,\n",
       " '아들': 735,\n",
       " '꿈꾸': 736,\n",
       " '이제': 737,\n",
       " '천': 738,\n",
       " '아직': 739,\n",
       " '미술관': 740,\n",
       " '잡': 741,\n",
       " 'C': 742,\n",
       " '정원': 743,\n",
       " '기억': 744,\n",
       " '냐': 745,\n",
       " '중개사': 746,\n",
       " '괴물': 747,\n",
       " '중국어': 748,\n",
       " '며': 749,\n",
       " '달력': 750,\n",
       " '지방': 751,\n",
       " '한국어': 752,\n",
       " '단': 753,\n",
       " '칼': 754,\n",
       " '캐릭': 755,\n",
       " '학년': 756,\n",
       " '모르': 757,\n",
       " '클래식': 758,\n",
       " '던전': 759,\n",
       " '하버드': 760,\n",
       " 'for': 761,\n",
       " '등': 762,\n",
       " '슬픔': 763,\n",
       " '랑': 764,\n",
       " '싫': 765,\n",
       " '23': 766,\n",
       " '라이팅': 767,\n",
       " '스피드': 768,\n",
       " '라는': 769,\n",
       " '어느': 770,\n",
       " '보인다': 771,\n",
       " '놀이': 772,\n",
       " '어떤': 773,\n",
       " '소리': 774,\n",
       " '억': 775,\n",
       " '얼굴': 776,\n",
       " '가이드': 777,\n",
       " '왕자': 778,\n",
       " '이에야스': 779,\n",
       " '마을': 780,\n",
       " '도쿠가와': 781,\n",
       " '김': 782,\n",
       " '빛': 783,\n",
       " '이토록': 784,\n",
       " '박사': 785,\n",
       " '스피킹': 786,\n",
       " '가족': 787,\n",
       " '반찬': 788,\n",
       " '교사': 789,\n",
       " 'Part': 790,\n",
       " '밥상': 791,\n",
       " '운전': 792,\n",
       " 'G': 793,\n",
       " 'X': 794,\n",
       " '로마': 795,\n",
       " '면허': 796,\n",
       " 'the': 797,\n",
       " '라면': 798,\n",
       " '똑똑': 799,\n",
       " '만든': 800,\n",
       " '또': 801,\n",
       " '논어': 802,\n",
       " '동물': 803,\n",
       " '머리': 804,\n",
       " '도올': 805,\n",
       " 'of': 806,\n",
       " '피아노': 807,\n",
       " '모두': 808,\n",
       " '악': 809,\n",
       " '설서': 810,\n",
       " '그리': 811,\n",
       " '간': 812,\n",
       " '센스': 813,\n",
       " '쇄': 814,\n",
       " '위해': 815,\n",
       " '준': 816,\n",
       " '종이': 817,\n",
       " '수상': 818,\n",
       " '존재': 819,\n",
       " '절대로': 820,\n",
       " 'CEO': 821,\n",
       " '실천': 822,\n",
       " '전산': 823,\n",
       " '어도': 824,\n",
       " '서른': 825,\n",
       " '어른': 826,\n",
       " '팬': 827,\n",
       " '패턴': 828,\n",
       " '자격': 829,\n",
       " '1200': 830,\n",
       " '엔': 831,\n",
       " '일본': 832,\n",
       " '계': 833,\n",
       " '경찰학': 834,\n",
       " '이유': 835,\n",
       " '의사': 836,\n",
       " '웃': 837,\n",
       " '해도': 838,\n",
       " '원리': 839,\n",
       " 'Q': 840,\n",
       " '지문': 841,\n",
       " '빈출': 842,\n",
       " '코드': 843,\n",
       " '태백산맥': 844,\n",
       " '태양': 845,\n",
       " 'Season': 846,\n",
       " '불안': 847,\n",
       " '검': 848,\n",
       " '남': 849,\n",
       " '박스': 850,\n",
       " '버': 851,\n",
       " '회계': 852,\n",
       " '킹': 853,\n",
       " '바로': 854,\n",
       " '글씨': 855,\n",
       " '묻': 856,\n",
       " '40': 857,\n",
       " '사기': 858,\n",
       " '살인': 859,\n",
       " '포트': 860,\n",
       " '천국': 861,\n",
       " '상처': 862,\n",
       " '나이': 863,\n",
       " '텔': 864,\n",
       " '특강': 865,\n",
       " '문동': 866,\n",
       " '일상': 867,\n",
       " '희망': 868,\n",
       " '단어장': 869,\n",
       " '길라잡이': 870,\n",
       " '29': 871,\n",
       " '인터': 872,\n",
       " '파일': 873,\n",
       " '테스트': 874,\n",
       " '이런': 875,\n",
       " '입문': 876,\n",
       " '쿠폰': 877,\n",
       " '자동': 878,\n",
       " '감정': 879,\n",
       " '파워': 880,\n",
       " '진': 881,\n",
       " '추리': 882,\n",
       " '치즈': 883,\n",
       " '제국': 884,\n",
       " 'Book': 885,\n",
       " '정답': 886,\n",
       " '재미있': 887,\n",
       " '더니': 888,\n",
       " '잔': 889,\n",
       " '작품집': 890,\n",
       " '자유': 891,\n",
       " '자용': 892,\n",
       " '다른': 893,\n",
       " '다는': 894,\n",
       " '키튼': 895,\n",
       " '거짓말': 896,\n",
       " '거인': 897,\n",
       " '위하': 898,\n",
       " '마이': 899,\n",
       " '숨': 900,\n",
       " '수록': 901,\n",
       " '로운': 902,\n",
       " '로크': 903,\n",
       " '혼불': 904,\n",
       " '리더': 905,\n",
       " '서울대': 906,\n",
       " ']': 907,\n",
       " '포토샵': 908,\n",
       " '[': 909,\n",
       " 'Re': 910,\n",
       " '2020': 911,\n",
       " 'OX': 912,\n",
       " '뿐': 913,\n",
       " 'Writing': 914,\n",
       " '부르': 915,\n",
       " '보험': 916,\n",
       " '베이직': 917,\n",
       " '내게': 918,\n",
       " '관리사': 919,\n",
       " '미학': 920,\n",
       " '미디': 921,\n",
       " '2011': 922,\n",
       " '문명': 923,\n",
       " '스타': 924,\n",
       " '훈련': 925,\n",
       " '패밀리': 926,\n",
       " '엇': 927,\n",
       " '워크': 928,\n",
       " '우주': 929,\n",
       " '우울': 930,\n",
       " '깊': 931,\n",
       " '완전': 932,\n",
       " '오디세이': 933,\n",
       " '영웅': 934,\n",
       " '풍경': 935,\n",
       " '영단': 936,\n",
       " '26': 937,\n",
       " '십': 938,\n",
       " '평생': 939,\n",
       " '앤': 940,\n",
       " '라그나': 941,\n",
       " '아트': 942,\n",
       " '아침': 943,\n",
       " '아서': 944,\n",
       " '없이': 945,\n",
       " '아버지': 946,\n",
       " '거의': 947,\n",
       " '곳': 948,\n",
       " '기타': 949,\n",
       " '내일': 950,\n",
       " '권리': 951,\n",
       " '기관': 952,\n",
       " '갈': 953,\n",
       " '나요': 954,\n",
       " '김종석': 955,\n",
       " '누가': 956,\n",
       " '양장본': 957,\n",
       " '다운': 958,\n",
       " '다정': 959,\n",
       " '증보판': 960,\n",
       " 'All': 961,\n",
       " '즈': 962,\n",
       " '주년': 963,\n",
       " '정말': 964,\n",
       " '잠수': 965,\n",
       " '자수': 966,\n",
       " '자본주의': 967,\n",
       " '자료': 968,\n",
       " '일반': 969,\n",
       " '일곱': 970,\n",
       " '인류': 971,\n",
       " '이해': 972,\n",
       " '이것': 973,\n",
       " '월간': 974,\n",
       " '울': 975,\n",
       " '운': 976,\n",
       " '지박': 977,\n",
       " '착각': 978,\n",
       " '참': 979,\n",
       " '파이널': 980,\n",
       " '향기': 981,\n",
       " '한국인': 982,\n",
       " '프로그램': 983,\n",
       " '2015': 984,\n",
       " '펀드': 985,\n",
       " '25': 986,\n",
       " '27': 987,\n",
       " '파': 988,\n",
       " '천사': 989,\n",
       " '퇴마록': 990,\n",
       " '탄생': 991,\n",
       " '퀸': 992,\n",
       " '코리아': 993,\n",
       " '커피': 994,\n",
       " '치과': 995,\n",
       " '체크': 996,\n",
       " 'GSAT': 997,\n",
       " '고시': 998,\n",
       " '엣': 999,\n",
       " 'KBS': 1000,\n",
       " ...}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp2 = temp.to_dict()\n",
    "map_token_encode = temp2['val']\n",
    "map_token_encode['[PAD]']=0\n",
    "map_token_encode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### padding & integer encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9328,)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['민병철', '생활', '영어', '1']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(book_name.shape)\n",
    "display(book_name[0])\n",
    "len_sent = list(map(len,book_name))\n",
    "display(max(len_sent),min(len_sent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2022', '최신', '판', '렛', '유인', '공기업', '시험', '전', ',', '실전', '감각', 'UP', '!', '단', '5', '+', '3', '회', '로', '취약점', '정면', '돌파', '!', '국민', '건강보험공단', 'NCS', '봉투', '모의고사', '5', '+', '3', '회']\n"
     ]
    }
   ],
   "source": [
    "display(len_sent[np.argmax(len_sent)])\n",
    "print(book_name[np.argmax(len_sent)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.array(len_sent) >= 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Count'>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(len_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9328, 30)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([['민병철', '생활', '영어', ..., '[PAD]', '[PAD]', '[PAD]'],\n",
       "       ['행복', '에', '의', ..., '[PAD]', '[PAD]', '[PAD]'],\n",
       "       ['훈련', '으로', '되', ..., '[PAD]', '[PAD]', '[PAD]'],\n",
       "       ...,\n",
       "       ['타', '몬', '군', ..., '[PAD]', '[PAD]', '[PAD]'],\n",
       "       ['천재', '의', '사', ..., '[PAD]', '[PAD]', '[PAD]'],\n",
       "       ['전지', '적', '루이', ..., '[PAD]', '[PAD]', '[PAD]']], dtype=object)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "padded_bookname = pad_sequences(book_name,padding='post',maxlen=30,value='[PAD]',dtype=object)\n",
    "display(padded_bookname.shape)\n",
    "padded_bookname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8075,  353,   61, ...,    0,    0,    0],\n",
       "       [ 219,   40,    2, ...,    0,    0,    0],\n",
       "       [ 925,   79,  124, ...,    0,    0,    0],\n",
       "       ...,\n",
       "       [1147, 2270,  406, ...,    0,    0,    0],\n",
       "       [ 496,    2,  148, ...,    0,    0,    0],\n",
       "       [1193,  102, 5880, ...,    0,    0,    0]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode_tokens = lambda x: list(map(lambda y : map_token_encode[y],x))\n",
    "encoded_bookname = np.apply_along_axis(encode_tokens,0,padded_bookname)\n",
    "encoded_bookname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44, 0)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len_sent = list(map(len,book_subname))\n",
    "max(len_sent),min(len_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Count'>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(len_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['(', '캐릭터', '책갈피', '1', '종', '+', '투명', '포토', '카드', '2', '종', '+', 'DIY', '편지지', '책자', '+', '컵', '홀더', '2', '종', '+', '테이블', '매트', '(', '패브릭', '포스터', ')', '+', '종이', '코스터', '2', '종', '+', '주접', '대사', '스티커', '3', '종', '+', '박스', '포함', '한', '정판', ')']\n"
     ]
    }
   ],
   "source": [
    "display(len_sent[np.argmax(len_sent)])\n",
    "print(book_subname[np.argmax(len_sent)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(np.sum(np.array(len_sent) >= 20))\n",
    "display(np.sum(np.array(len_sent) >= 25))\n",
    "display(np.sum(np.array(len_sent) >= 30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2836    [(, 테이프, 별매, ), (, Hackers, TOEIC, Listening, ...\n",
       "3226    [(, 교재, (, ETS, X, -, File, 빈출, 표현, 수록, ), +, ...\n",
       "4815    [(, 국가, 직무, 능력, 표준, ), 직업, 기초, 능력, 평가, +, 직무, ...\n",
       "5895    [(, 본, 책, +, 예문, 듣, 기, MP, 3, 파일, 무료, 다운, 로드, ...\n",
       "8513    [(, 캐릭터, 책갈피, 1, 종, +, 투명, 포토, 카드, 2, 종, +, DI...\n",
       "Name: BName_sub, dtype: object"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = (np.array(len_sent) > 25)\n",
    "book_subname[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   0, ...,   0,   0,   0],\n",
       "       [  0,   0,   0, ...,   0,   0,   0],\n",
       "       [  0,   0,   0, ...,   0,   0,   0],\n",
       "       ...,\n",
       "       [  0,   0,   0, ...,   0,   0,   0],\n",
       "       [  0,   0,   0, ...,   0,   0,   0],\n",
       "       [  8, 957,   9, ...,   0,   0,   0]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "padded_subname = pad_sequences(book_subname,padding='post',maxlen=25,value='[PAD]',dtype=object)\n",
    "encoded_subname = np.apply_along_axis(encode_tokens,0,padded_subname)\n",
    "encoded_subname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len_sent = list(map(len,category))\n",
    "max(len_sent),min(len_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['소설', '/', '시', '/', '희곡']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(len_sent[np.argmax(len_sent)])\n",
    "display(category[np.argmax(len_sent)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Count'>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAqp3tQT6EylUimPPPJI/uVf/iULFizIa6+9lq233joDBw7MiBEj8pWvfCXV1e1fgtdffz1XXnllHn300SxfvjyDBg3KCSeckFGjRm3wMZqamvLjH/84d999d1577bVsv/32Oeqoo3L22Wdnq6222uBtZs6cmVtuuSUvv/xy6urqcsghh+Tcc8/Nrrvu2uGvAQAAAAB0lm4dFxctWpTTTz89VVVVGTZsWIYNG5bGxsY89dRTeeaZZ/LQQw/lhhtuSFVVVZJ1YfG4447L4sWLs/fee+eTn/xknnrqqVxyySV56aWXcvHFF7e7/+bm5px22mmZNWtWdthhhxx22GGZP39+brzxxjz99NOZNm1aevTo0e4211xzTX70ox+lZ8+eOfTQQ7NkyZLcfffdeeyxx3LHHXdk4MCBXfb6AAAAAMCH0a3jYl1dXb75zW/mK1/5Sj72sY+Vt7/00ks54YQT8thjj+Wuu+7K8ccfnySZOHFiFi9enOOOOy6TJk1KkjQ2NmbkyJGZOnVqPv/5z+fAAw8s38/tt9+eWbNmpb6+PtOnT0/v3r2zdu3anHPOOXnggQdyww035Oyzzy6PX7BgQa677rrU1dXljjvuyODBg5MkN910UyZPnpyLL744U6dO7YqXBgAAAAA+tG59zsV+/frl3HPPbRcWk2SvvfbKyJEjkySPPPJIkmTx4sVpaGjIVlttlYsuuqg8dtttt81xxx2XJLnlllva3c/06dOTJBdeeGF69+6dJKmqqsro0aOTJNOmTcvatWvL42fMmJFSqZRTTz21HBaT5MQTT0xdXV2eeuqpzJs3r0OeOwAAAAB0tm4dF9/PbrvtliRZtWpVkuSJJ55IS0tLDjrooGy99dblcW+//XZmzJhRHtPc3Jwkee211/Lyyy+nT58+Ofjgg9vd9/XXX58kWbp0aebOnVve/vjjjydJjjjiiHbjb7755qxcuTLJu7ETAAAAADZ3H9m4+Pvf/z5JsueeeyZJXnzxxSTJsGHD2o2bPHlyFi9enNra2qxcuTJ//OMfk6w7xDlJ6uvrU1n57ss4c+bM/OY3v0nPnj3bjVu1alUWLlyY6urq7L333u3mMWXKlPL4+fPnd/hzBQAAAIDO8JGMi7Nnz86MGTPSs2fPnHjiiUnWXfwlSXbaaafyuEcffTR33nlnTjzxxOy+++7txm1o/OLFizNp0qQMGTIkxxxzzHrjS6VStttuu9TU1CRJWlpa8p3vfCdVVVU577zz2o0HAAAAgM1dt76gS1sTJkzIm2++mYULF2bu3LnZbbfdMmnSpOy6665JkuXLlydJ+vTpU/7zd7/73QwYMCAXXHBBzjjjjHbj/nR8kowfPz4rVqzIxIkT8/DDD7cbt2LFivXGT506NXPmzMnYsWMzdOjQduO6QqlUSlNTU5c9Xmdpbm5ObW1tkqSlVEqppbSJZ7Tx2s61O/wsNrU1a9Zs8Gs+mqwHWlkLtGU90MpaoC3rgVbWAm11x/VQKnV8M/nIxMW77747S5cuLf95yJAh6devX/nPrWGnda/CK664Iq+++mr+6Z/+Kb169SpfsKX1HI1/Ov6ee+5JQ0NDTj/99AwbNizPPPPM+45fuHBhrrrqqgwfPjwnn3xyu8Onu0pzc3O7c0JuqWpqarLvvvsmSVa9szLLm7ruNfywKmuby1/Pmzev27xZbQ5cHIm2rAdaWQu0ZT3QylqgLeuBVtYCbVkP7+0jExdnz56dpqamLFq0KL/4xS/yT//0T3niiSdy8803Z9iwYamuXvdSVFRUZPbs2bn99ttz9NFH5zOf+UySdVeBTt6Ng63/r6ioyJtvvpkJEyZk0KBBOeusszY4vvX+k3WVeOzYsWlubs7EiRNTWVlZPm9j63gAAAAA2Nx9ZOJiktTW1mb33XfPmDFj0qNHj1x55ZWZPHlypk2bll69eiVJlixZkkmTJqV///656KKLyrdt3aOsdVxdXV2SdYcxT5gwIY2NjZkyZUp69OiRJOWrSreOa/3/ypUrc9ttt2X27Nk577zzyheU+dPxXaG6ujpDhgzpssfrLK2vXZL03KouLTVbzrKuq+lR/rq+vn4TzqR7WLNmTflfk+rr68X6jzjrgVbWAm1ZD7SyFmjLeqCVtUBb3XE9vPDCC+06SkfYcipMB/ubv/mbXHnllXn22WfT1NSUHXbYIUny/e9/P42Njbn66quzzTbblMe/+eabSZIBAwYkSXbcccckyf3335/GxsacdNJJ2X///cvjlyxZkiTZeeedk7x74ZdFixbliiuuyJAhQ/L1r399vfGt998VKioqyucq7C4qKypSUVmxqaex0drOtbv9LDa1mpoaryll1gOtrAXash5oZS3QlvVAK2uBtrrLeqio6Phm8pG8WnSSckxsbm7O22+/nT322CNJ0tjYmCOOOCJHHXVUeWxLS0tefPHF1NbWZuDAgUnSbnzrRV/amj9/fpJkr732SrLuQi79+/dPU1NTVq9enYkTJ7Y7VLp1/ODBgzvj6QIAAABAh/vIxsVZs2YlSbbeeuv069cvhxxySJJ1BXfs2LHtxj766KNZsWJFPvWpT5Ur9Z577lnee/H8888vHy6dJEuXLs2sWbNSV1eX4cOHl7cffPDBSZJjjz02w4YNa/cY9913X5Lk05/+dEc+TQAAAADoNN06Ll522WV57LHH0tLS0m77Cy+8kHHjxiVJjj766FRWVmbQoEE54IADUiqV8utf/7o8trGxMZdddlmS5IQTTmh3P8cff3ySdWGw9VLea9euzaWXXprVq1dn5MiR7XaZHTlyZJLkySefzFtvvVXePn369Dz33HMZOnRouxgJAAAAAJuzbn3OxSeffDI//elPs+2222bYsGHZZpttsmjRojz33HNZu3Zt9t9//5x33nnl8ePGjcuoUaMyefLk3H///dl+++0ze/bsNDY25sgjj8zhhx/e7v6/9rWvpaGhIQ0NDRkxYkTq6+vz/PPP55VXXsnAgQNz5plntht/0EEH5Zhjjsldd92VL3zhCznggAPyxhtvZM6cOenRo0fGjx/fJa8LAAAAAHSEbh0XJ0+enF/84hd5/PHH8+yzz2bVqlXp3bt3PvnJT+bLX/5yjj322FRVVZXH77PPPvnZz36Wq666Kk8//XTmzp2bXXbZJaNHj87o0aPXu/9evXpl2rRpueaaa/Lggw+moaEh/fr1y6hRozJmzJj07dt3vdtMnDgxe++9d+6888489NBDqaury2GHHZYxY8a4WjAAAAAAW5RuHReHDh2aoUOHfqDb7LXXXrn22ms3enzfvn0zbty48mHWf05VVdV7xkoAAAAA2JJ063MuAgAAAACdR1wEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAopHpTT6CzvfDCC7n99tvz5JNP5r/+679SWVmZQYMGZcSIETn11FPTs2fP8th7770355577nve1y233JIDDzyw3bampqb8+Mc/zt13353XXnst22+/fY466qicffbZ2WqrrTZ4PzNnzswtt9ySl19+OXV1dTnkkENy7rnnZtddd+2Q5wwAAAAAXaFbx8VHHnkkp59+ekqlUgYOHJjPfvazeeeddzJnzpz84Ac/yL333pupU6emd+/eSZJly5YlSYYNG5a99957vfvbbrvt2v25ubk5p512WmbNmpUddtghhx12WObPn58bb7wxTz/9dKZNm5YePXq0u80111yTH/3oR+nZs2cOPfTQLFmyJHfffXcee+yx3HHHHRk4cGAnvRoAAAAA0LG6dVxctmxZhg8fnm9961v55Cc/Wd7e2NiYU045Jc8//3x++tOf5uyzz06SvP3220mSY445Jqeccsqfvf/bb789s2bNSn19faZPn57evXtn7dq1Oeecc/LAAw/khhtuKN93kixYsCDXXXdd6urqcscdd2Tw4MFJkptuuimTJ0/OxRdfnKlTp3bkSwAAAAAAnaZbn3Pxc5/7XGbMmNEuLCbJtttum9GjRydJHn300fL21j0Xt9566426/+nTpydJLrzwwvLej1VVVeX7njZtWtauXVseP2PGjJRKpZx66qnlsJgkJ554Yurq6vLUU09l3rx5H/RpAgAAAMAm0a3jYs+ePVNRUbHB7/Xv3z/Ju3srtv26T58+f/a+X3vttbz88svp06dPDj744Hbfu/7665MkS5cuzdy5c8vbH3/88STJEUcc0W78zTffnJUrVyZZdyg3AAAAAGwJunVcfD+vvvpqkmTnnXcub/sgcXHBggVJkvr6+lRWvvsyzpw5M7/5zW/KF4ppHbdq1aosXLgw1dXV7c7n+Pvf/z5Tpkwpj58/f/6HeVoAAAAA0GW69TkX3899992XJPnEJz5R3tZ6WPR9992XX/3qV2lpackuu+ySww8/PLvvvnu72y9atChJstNOO5W3LV68OJMmTcqQIUOy33775dZbby2PW7RoUUqlUrbbbrvU1NQkSVpaWvKd73wnVVVVGTNmTCZPnlwe3xVKpVKampq67PE6S3Nzc2pra5MkLaVSSi2lTTyjjdd2rt3hZ7GprVmzZoNf89FkPdDKWqAt64FW1gJtWQ+0shZoqzuuh1Kp45vJRzIuPvnkk3nyySfTo0ePjBw5sry9dc/FW265pd34H/7whznzzDNz1llnlbctX748Sfu9HMePH58VK1Zk4sSJefjhh9uNW7FixXrjp06dmjlz5mTs2LEZOnRou3Fdobm5ud1h21uqmpqa7LvvvkmSVe+szPKmVZt4Rhuvsra5/PW8efO6zZvV5sD5S2nLeqCVtUBb1gOtrAXash5oZS3QlvXw3j5ycXHZsmUZN25ckuSb3/xmdthhh/L3vvGNb6R///7Zeeed07t37yxatCgzZ87MT37yk0yZMiV77LFHRowYkeTdvcxa90K855570tDQkNNPPz3Dhg3LM888k2Td4dAbGr9w4cJcddVVGT58eE4++eR2h08DAAAAwJbgIxUXS6VSvv3tb2fhwoUZPnx4vv71r7f7/mc/+9l2f95jjz1y/vnnp2/fvrn88stz0003leNiaySsqKjIm2++mQkTJmTQoEHlvRurqqrajauufvelLpVKGTt2bJqbmzNx4sRUVlaWz9vYOr4rVFdXZ8iQIV32eJ2lufndvf96blWXlpotZ1nX1fQof11fX78JZ9I9rFmzpvyvSfX19V36+8Tmx3qglbVAW9YDrawF2rIeaGUt0FZ3XA8vvPBCu47SEbacCtMBrrvuujz44IPZbrvtcvXVV2/0ovjiF7+Yyy+/PL/73e9SKpVSUVGRurq6JOsOY54wYUIaGxszZcqU9OixLha1/qBax7X+f+XKlbntttsye/bsnHfeedlzzz03OL4rVFRUlM9V2F1UVlSkonLDVwjfHLWda3f7WWxqNTU1XlPKrAdaWQu0ZT3QylqgLeuBVtYCbXWX9VBR0fHN5CMTFx9++OFce+21qa6uztVXX50dd9xxo2+77bbbJlkXAJubm1NTU1O+/f3335/GxsacdNJJ2X///cu3WbJkSZJ3r0bdeuGXRYsW5YorrsiQIUPa7TnZOn7AgAEf4lkCAAAAQNep3NQT6AqvvPJKvvWtb6WlpSV/93d/1y4CboyFCxcmSfr161fe23GPPfZIkjQ2NmbAgAG54IIL2t1m/vz5SZK99toryboLufTv3z9NTU1ZvXp1Jk6c2O5Q6dbxgwcPLvAMAQAAAKDrdfu4uGLFipx11llZtmxZjj766Jxyyikf+D5+/vOfJ0kOOeSQ8rY999yzvPfi+eefn169epW/t3Tp0syaNSt1dXUZPnx4efvBBx+cJDn22GMzbNiwdo9x3333JUk+/elPf+D5AQAAAMCm0O3j4kUXXZQXX3wxw4YNy6WXXvqe4/7xH/8xDzzwwHontXzwwQdz0003paamZr0LwBx//PFJ1oXBUqmUJFm7dm0uvfTSrF69OiNHjmx3PP7IkSOTJE8++WTeeuut8vbp06fnueeey9ChQ9vFSAAAAADYnHXrcy42NDSU9wjs27dvLrnkkg2Ou+yyyzJ37txceeWV6dOnTz7+8Y+nX79+eeONN/LMM8+koqIi3/ve99a7svLXvva1NDQ0pKGhISNGjEh9fX2ef/75vPLKKxk4cGDOPPPMduMPOuigHHPMMbnrrrvyhS98IQcccEDeeOONzJkzJz169Mj48eM754UAAAAAgE7QrePismXLyl8/8cQT7znusssuyyWXXJJ99tknjzzySObOnZuVK1dmm222yZFHHpnTTz89Q4cOXe92vXr1yrRp03LNNdfkwQcfTENDQ/r165dRo0ZlzJgx6du373q3mThxYvbee+/ceeedeeihh1JXV5fDDjssY8aMSX19fcc8cQAAAADoAt06Lh577LE59thjN2rsDjvskHPOOSfnnHPOB3qMvn37Zty4cRk3btxGja+qqsro0aMzevToD/Q4AAAAALC56fbnXAQAAAAAOoe4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUEj1pp4AAAB0pZqamk09BQCAbkNcBADgI2Xffffd1FMAAOg2HBYNAAAAABRiz0UAAD6Sps2fnRUtTZt6GhutV3WPnDz4U5t6GgAA7YiLAAB8JC1vXp0Va7ecuAgAsDlyWDQAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQSJfExVNPPTWXXXbZRo19+umn86lPfSrXXnttJ88KAAAAAPgwuiQuPvXUU3n++ec3auz++++fysrK/PrXv+7kWQEAAAAAH8Zmd1h0RUVFdtttt7z66qubeioAAAAAwPvY7OJic3Nz/uu//is9e/bc1FMBAAAAAN5HdUff4euvv54//OEP621ftmxZnn766fe97bJly3LXXXflv//7v3PQQQd19NQAAAAAgA7U4XFx9erV+epXv5pSqdRu+7x583Lqqaf+2duXSqVUVFTkG9/4RkdPDQAAAADoQB1+WPRuu+2WI488MpWVleX/knXRsO22Df3Xs2fPDBs2LFdeeaU9FwEAAABgM9fhey4myZVXXtnuz/X19fnUpz6VW265pTMeDgAAAADYBDa7C7oAAAAAAFuGLomLBxxwQIYMGdIVDwUAAAAAdJFOOSz6T02dOrUrHmaDXnjhhdx+++158skn81//9V+prKzMoEGDMmLEiJx66qnp2bNnu/Gvv/56rrzyyjz66KNZvnx5Bg0alBNOOCGjRo3a4P03NTXlxz/+ce6+++689tpr2X777XPUUUfl7LPPzlZbbbXB28ycOTO33HJLXn755dTV1eWQQw7Jueeem1133bXDnz8AAAAAdJZufVj0I488kmOOOSa33nprSqVSPvvZz+ZTn/pU/vjHP+YHP/hBTjjhhCxfvrw8/vXXX89xxx2Xu+66K/369ctf/dVf5fXXX88ll1yS733ve+vdf3Nzc0477bRcd911WbVqVQ477LDU1tbmxhtvzKmnnprVq1evd5trrrkm3/72t/Pyyy/n0EMPzaBBg3L33Xfn+OOPz3/+53926usBAAAAAB2pS/Zc3FSWLVuW4cOH51vf+lY++clPlrc3NjbmlFNOyfPPP5+f/vSnOfvss5MkEydOzOLFi3Pcccdl0qRJ5bEjR47M1KlT8/nPfz4HHnhg+X5uv/32zJo1K/X19Zk+fXp69+6dtWvX5pxzzskDDzyQG264oXzfSbJgwYJcd911qauryx133JHBgwcnSW666aZMnjw5F1988SbdyxMAAAAAPogui4srV67MXXfdlaeeeipLlizJ0qVLs2bNmve9zX333fehHvNzn/tcRowYkYqKinbbt91224wePToXXXRRHn300Zx99tlZvHhxGhoastVWW+Wiiy5qN/a4447LVVddlVtuuaVdXJw+fXqS5MILL0zv3r2TJFVVVRk9enQeeOCBTJs2LWeccUaqqqqSJDNmzEipVMqpp55aDotJcuKJJ+bqq6/OU089lXnz5qW+vv5DPW8AAAAA6ApdEhdff/31nHLKKVm4cGFKpdJG3eZPg2ARf3o+xbb69++fJHn77beTJE888URaWlpy0EEHZeutty6Pe/vttzNjxozymObm5lRXV+e1117Lyy+/nD59+uTggw9ud9/XX399kmTp0qWZO3du9ttvvyTJ448/niQ54ogj2o2/+eabs3LlyiTrDuUWFwEAAADYEnRJXPzBD36QP/zhD+nbt2++8IUvZI899kivXr1SWbnpTvn46quvJkl23nnnJMmLL76YJBk2bFi7cZMnT87ixYtTW1ublStX5o9//GMGDRqUBQsWJEnq6+vbPY+ZM2fmN7/5TXr27JlVq1ZlwYIF2W+//bJq1aosXLgw1dXV2Xvvvcvjf//732fKlCnl8fPnz+/U5w0AAAAAHaVL4uJjjz2WmpqazJgxI3vuuWdXPOSf1XrI9Sc+8YkkyaJFi5IkO+20U3nMo48+mjvvvDMnnXRSnnnmmcyfPz+LFi3KoEGDNjh+8eLFmTRpUoYMGZL99tsvt956a3ncokWLUiqVst1226WmpiZJ0tLSku985zupqqrKmDFjMnny5PL4rlAqldLU1NRlj9dZmpubU1tbmyRpKZVSatm4vWM3B23n2h1+Fpta21Mt/LnTLtD9WQ+0shZoy+cGWnlvoC3rgVbWAm11x/WwsUcUfxBdEhdXrFiRvfbaa7MJi08++WSefPLJ9OjRIyNHjkyS8lWj+/TpU/7zd7/73QwYMCAXXHBBzjjjjHbj/nR8kowfPz4rVqzIxIkT8/DDD7cbt2LFivXGT506NXPmzMnYsWMzdOjQduO6QnNzc+bOndtlj9dZampqsu+++yZJVr2zMsubVm3iGW28ytrm8tfz5s3rNm9Wm4N58+Zt6imwGbEeaGUt4HMDG+K9gbasB1pZC7RlPby3LjkuedCgQXnrrbe64qH+rGXLlmXcuHFJkm9+85vZYYcdkrz7r7+texVeccUVefXVVzN+/Pj06tWrfMGWVatWbXD8Pffck4aGhowePTrDhg37s+MXLlyYq666KsOHD8/JJ5+83ngAAAAA2Nx1yZ6Lp5xySi6++OJ8//vfz7e+9a2ueMgNKpVK+fa3v52FCxdm+PDh+frXv17+XnX1upeioqIis2fPzu23356jjz46n/nMZ5KkfMXn1jjY+v+Kioq8+eabmTBhQgYNGpSzzjprg+Nb7791HmPHjk1zc3MmTpyYysrK8nkbW8d3herq6gwZMqTLHq+zNDe/+6/4PbeqS0tNl10E/UOrq+lR/tqFfD68NWvWlP81qb6+vkt/n9j8WA+0shZoy+cGWnlvoC3rgVbWAm11x/XwwgsvtPs81BG65NPU8ccfnwULFuTGG2/MM888k1GjRmWvvfbK9ttv/76323HHHTt0Htddd10efPDBbLfddrn66qvbLYpevXolSZYsWZJJkyalf//+ueiii8rfbz3spHVcXV1dknWHMU+YMCGNjY2ZMmVKevRY96Gv9QfVOq71/ytXrsxtt92W2bNn57zzzisfKv6n47tCRUVF+ZxD3UVlRUUqKj/8lca7Stu5drefxaZWU1PjNaXMeqCVtUBbPjfQynsDbVkPtLIWaKu7rIeKio7/7NMlcbHt3nH//u//nn//93//s7epqKjI888/32FzePjhh3Pttdemuro6V1999XrhsvXw6O9///tpbGzM1VdfnW222ab8/TfffDNJMmDAgCTvhs/7778/jY2NOemkk7L//vuXxy9ZsiTJu1ejbr3wy6JFi3LFFVdkyJAh7facbB3fev8AAAAAsLnrkrhY5Eo0HXn1mldeeSXf+ta3yldnbhsBW+2xxx5JksbGxhxxxBE56qijyt9raWnJiy++mNra2gwcOHC98a0XfWlr/vz5SZK99toryboLufTv3z9LlixJS0tLJk6c2O5Q6dbxgwcP7qinDQAAAACdqkvi4oMPPtgVD7NBK1asyFlnnZVly5bl6KOPzimnnLLBcYccckiSdXtMjh07tt33Hn300axYsSKf/vSny7vA7rnnntlxxx3z+uuv5/zzzy8fLp0kS5cuzaxZs1JXV5fhw4eXtx988MG5++67c+yxx2bYsGHtHuO+++5Lknz605/+8E8aAAAAALpAl1wteueddy70X0e46KKL8uKLL2bYsGG59NJL33PcoEGDcsABB6RUKuXXv/51eXtjY2Muu+yyJMkJJ5zQ7jbHH398knVhsHVPy7Vr1+bSSy/N6tWrM3LkyHbH448cOTJJ8uSTT7a7evb06dPz3HPPZejQoe1iJAAAAABszracy+MV0NDQUN4jsG/fvrnkkks2OK41Ho4bNy6jRo3K5MmTc//992f77bfP7Nmz09jYmCOPPDKHH354u9t97WtfS0NDQxoaGjJixIjU19fn+eefzyuvvJKBAwfmzDPPbDf+oIMOyjHHHJO77rorX/jCF3LAAQfkjTfeyJw5c9KjR4+MHz++E14FAAAAAOgcXRIXf/nLXxa63Ze+9KUP9bjLli0rf/3EE0+857jWuLjPPvvkZz/7Wa666qo8/fTTmTt3bnbZZZeMHj06o0ePXu92vXr1yrRp03LNNdfkwQcfTENDQ/r165dRo0ZlzJgx6du373q3mThxYvbee+/ceeedeeihh1JXV5fDDjssY8aMSX19/Yd6vgAAAADQlbokLv7t3/5toUtdf9i4eOyxx+bYY4/9QLfZa6+9cu211270+L59+2bcuHEZN27cRo2vqqp6z1gJAAAAAFuSLomLAwYMeN/v//d//3eampqSrLugysc//vHU1NR0xdQAAAAAgIK6JC4+9NBDf3bMwoULc+utt+aWW25JdXV1brrpps6fGAAAAABQWJdcLXpj7Lrrrrnwwgtz+eWX59lnn83111+/qacEAAAAALyPzSYuthoxYkQGDx6cn//855t6KgAAAADA+9js4mKSrFmzJm+88camngYAAAAA8D42u7jY0NCQ//zP/8x22223qacCAAAAALyPLrmgy7hx4973+6VSKStWrMh//Md/ZMGCBUmSo446qiumBgAAAAAU1CVx8Wc/+1kqKipSKpU2avznPve5jBkzppNnBQAAAAB8GF0SF48++uhUVFS85/dramrSt2/fbLfddjnwwANTX1/fFdMCAAAAAD6ELomLl112WVc8DAAAAADQhTa7C7oAAAAAAFuGLtlzsa2mpqY8++yzeeWVV/LWW2+ltrY2/fv3zw477JB99903W221VVdPCQAAAAAooMvi4sqVK/PDH/4w//qv/5pVq1ZtcExNTU2OOeaY/O3f/m169+7dVVMDAAAAAArokrjY1NSUk08+OS+88EJKpVJ69OiR3XbbLdtss01KpVLeeuut/OEPf8jq1atzxx135Nlnn80dd9yRnj17dsX0AAAAAIACuiQu3njjjXn++eez66675tvf/nY+85nPpLa2tt2YpqamPPzww7nsssvy4osv5p//+Z9z1llndcX0AAAAAIACuuSCLvfcc0+qq6vz05/+NIcffvh6YTFJamtrc8QRR+Tmm29OVVVVfv3rX3fF1AAAAACAgrokLv7xj39MfX19dtlllz87dtddd80+++yTRYsWdcHMAAAAAICiuiQuVlZWprm5eaPHr127NpWVXTI1AAAAAKCgLil4AwcOzIIFC/Liiy/+2bHz5s3L/PnzM3DgwC6YGQAAAABQVJfExS996UtpaWnJ//7f/zu33XZb3njjjfXGvP7665k2bVr+z//5P0mSL37xi10xNQAAAACgoC65WvSpp56ahx9+OLNnz8748eMzfvz49O7dO3369EmSvPXWW1mxYkWSpFQq5YADDshXv/rVrpgaAAAAAFBQl+y5WFVVlX/+53/OOeeck+222y6lUinLli3LokWLsmjRoixfvjylUinbbrttzjzzzNx4442pqqrqiqkBAAAAAAV1yZ6LSVJTU5MzzjgjZ5xxRv7whz/kjTfeyJIlS1IqldK/f//0798/u+++eyoqKrpqSgAAAADAh9BlcbGt3XbbLbvtttt625cvX54XXnghFRUV2X///TfBzAAAAACAjdUph0U//vjj+dznPpcvf/nLaWpq2ujbrV27Nt/+9rdzyimn5L777uuMqQEAAAAAHaRT4uLdd9+dV199NV/60pdSW1u70bfr27dvLr744pRKpfziF7/ojKkBAAAAAB2kU+Lib3/72/Ts2TOnnHLKB77tYYcdlm222Sa//e1vO2FmAAAAAEBH6ZS4+Nprr2X33XdPz549C91+4MCBefPNNzt4VgAAAABAR+qUuNjc3Pyhrvq8evXqD3Q4NQAAAADQ9TolLu6000558cUX89Zbb33g277++ut56aWXstNOO3XCzAAAAACAjtIpcfGv//qv09TUlIsuuugDXS36nXfeybe//e2sXbs2n/nMZzpjagAAAABAB+mUuHjqqaemT58++bd/+7ccffTR+dd//df893//93uOf/XVVzN9+vR86UtfyuzZs1NXV1foYjAAAAAAQNep7ow7/djHPparrroqY8aMyX/8x3/k4osvTpL069cvO+64Y+rq6tLS0pKVK1fmv/7rv/L2228nSUqlUnr27Jkf/OAHGTBgQGdMDQAAAADoIJ0SF5PkkEMOyV133ZUpU6bk3nvvTVNTU5YsWZIlS5ZscHxVVVU+85nP5Pzzz8/gwYM7a1oAAAAAQAfptLiYJLvsskv+4R/+IePHj8/TTz+dl19+Oa+//npWrFiRUqmUrbbaKtttt1322GOPHHDAAenbt29nTgcAAAAANlpNTc2mnsJmr1PjYquePXvm0EMPzaGHHtoVDwcAAAAAH0ptbW323XffJPlAFyz+qOmUC7oAAAAAAN1fl+y5CAAAAMBH17QXn8qK5tWbehofSK/K2py8z4GbehqbPXERAAAAgE61onl1lq/ZsuJiqaq0qaewRXBYNAAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFPKRiYtr1qzJP/zDP2SfffbJPvvskz/+8Y/rjbn33nvL39/Qf7Nnz17vNk1NTbn66qvz+c9/Pn/xF3+Rww47LJdffnneeeed95zLzJkzc+yxx+Yv//Ivc/DBB+eCCy7IwoULO/T5AgAAAEBnq97UE+gKjzzySH7wgx9k3rx57ztu2bJlSZJhw4Zl7733Xu/72223Xbs/Nzc357TTTsusWbOyww475LDDDsv8+fNz44035umnn860adPSo0ePdre55ppr8qMf/Sg9e/bMoYcemiVLluTuu+/OY489ljvuuCMDBw78kM8WAAAAALpGt4+LX/3qVzNr1qxUVFTkhBNOyK9+9au8/fbbGxzbuv2YY47JKaec8mfv+/bbb8+sWbNSX1+f6dOnp3fv3lm7dm3OOeecPPDAA7nhhhty9tlnl8cvWLAg1113Xerq6nLHHXdk8ODBSZKbbropkydPzsUXX5ypU6d2wLMGAAAAgM7X7Q+L/t3vfpdPfOITmTFjRv7+7/8+lZXv/ZRb91zceuutN+q+p0+fniS58MIL07t37yRJVVVVRo8enSSZNm1a1q5dWx4/Y8aMlEqlnHrqqeWwmCQnnnhi6urq8tRTT/3ZvSsBAAAAYHPR7fdcvPnmmzNs2LCNGtu652KfPn3+7NjXXnstL7/8cvr06ZODDz643feuv/76JMnSpUszd+7c7LfffkmSxx9/PElyxBFHrDfHlStXJll3CHd9ff1GzRcAAAAANqVuv+fixobF5IPFxQULFiRJ6uvr2+0NOXPmzPzmN79Jz549241btWpVFi5cmOrq6nbnc/z973+fKVOmlMfPnz9/o+cLAAAAAJtSt99z8YNoPSz6vvvuy69+9au0tLRkl112yeGHH57dd9+93dhFixYlSXbaaafytsWLF2fSpEkZMmRI9ttvv9x6663lcYsWLUqpVMp2222XmpqaJElLS0u+853vpKqqKmPGjMnkyZPL47tCqVRKU1NTlz1eZ2lubk5tbW2SpKVUSqmltIlntPHazrU7/Cw2tTVr1mzwaz6arAdaWQu05XMDrbw30Jb1QCtroeO1/r1batmy/t5NkpbKd+fb3Ny8CWfScUqljv8ZiItttO65eMstt7Tb/sMf/jBnnnlmzjrrrPK25cuXJ2m/l+P48eOzYsWKTJw4MQ8//HC7cStWrFhv/NSpUzNnzpyMHTs2Q4cObTeuKzQ3N2fu3Lld9nidpaamJvvuu2+SZNU7K7O8adUmntHGq6x9981p3rx5/vLqQM5fSlvWA62sBXxuYEO8N9CW9UAra+HDa/v37sqVK7aov3eTpLK2Z/nrl156yd+970FcbOMb3/hG+vfvn5133jm9e/fOokWLMnPmzPzkJz/JlClTsscee2TEiBFJ3v3X4ta9EO+55540NDTk9NNPz7Bhw/LMM88kWXc49IbGL1y4MFdddVWGDx+ek08+ud3h0wAAAACwJRAX2/jsZz/b7s977LFHzj///PTt2zeXX355brrppnJcbI2EFRUVefPNNzNhwoQMGjSovHdjVVVVu3HV1e++1KVSKWPHjk1zc3MmTpyYysrK8nkbW8d3herq6gwZMqTLHq+ztN01uedWdWmp2XKWdV1Nj/LXLuTz4a1Zs6b8r4v19fVd+vvE5sd6oJW1QFs+N9DKewNtWQ+0shY6T11dry3q790k6VldW/56r732atd2tlQvvPBChx/iveW/Kl3gi1/8Yi6//PL87ne/S6lUSkVFRerq6pKsO4x5woQJaWxszJQpU9Kjx7oPfa0/qNZxrf9fuXJlbrvttsyePTvnnXde9txzzw2O7woVFRXlcx90F5UVFamorNjU09hobefa3X4Wm1pNTY3XlDLrgVbWAm353EAr7w20ZT3QylroWBWVW9bfu8m6zwqtqquru8V6qKjo+J+BuLgRtt122yTrAmBzc3Nqamqy4447Jknuv//+NDY25qSTTsr+++9fvs2SJUuSJDvvvHOSdy/8smjRolxxxRUZMmRIvv71r683fsCAAZ3/hAAAAACgA1Ru6glsCRYuXJgk6devX3mX6D322CNJ0tjYmAEDBuSCCy5od5v58+cnWbfbbLLuQi79+/dPU1NTVq9enYkTJ7bbnbZ1/ODBgzv3yQAAAABABxEXN8LPf/7zJMkhhxxS3rbnnnuW9148//zz06tXr/L3li5dmlmzZqWuri7Dhw8vbz/44IOTJMcee2yGDRvW7jHuu+++JMmnP/3pznkSAAAAANDBxMX/8Y//+I954IEH1jup5YMPPpibbropNTU17Q5jTpLjjz8+ybowWCqVkiRr167NpZdemtWrV2fkyJHtjscfOXJkkuTJJ5/MW2+9Vd4+ffr0PPfccxk6dGi7GAkAAAAAm7Nuf87F559/PhMmTCj/efny5UmSMWPGlMPfuHHjMnfu3Fx55ZXp06dPPv7xj6dfv35544038swzz6SioiLf+9731ruy8te+9rU0NDSkoaEhI0aMSH19fZ5//vm88sorGThwYM4888x24w866KAcc8wxueuuu/KFL3whBxxwQN54443MmTMnPXr0yPjx4zv51QAAAACAjtPt4+KyZcsyZ86c9bb/9re/bTfmkksuyT777JNHHnkkc+fOzcqVK7PNNtvkyCOPzOmnn56hQ4eudx+9evXKtGnTcs011+TBBx9MQ0ND+vXrl1GjRmXMmDHp27fvereZOHFi9t5779x555156KGHUldXl8MOOyxjxoxJfX19xz55gI+41vPkAgAA0Dm6fVw88MADyxdL+XPOOeecnHPOOR/o/vv27Ztx48Zl3LhxGzW+qqoqo0ePzujRoz/Q4wDwwdTW1mbffffd1NMAAADo1pxzEQAAAAAopNvvuQjA5m/ai09lRfPqDr3PUkspK1euSJLU1fVKRWVFh913r+oeOXnwpzrs/gAAALZU4iIAm9yK5tVZvqbj4+LyplVJkpaa6g6NiwAAAKzjsGgAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAACjkIxMX16xZk3/4h3/IPvvsk3322Sd//OMfNzju9ddfz9/93d/l//v//r/85V/+Zf7X//pfue22297zfpuamnL11Vfn85//fP7iL/4ihx12WC6//PK8884773mbmTNn5thjj81f/uVf5uCDD84FF1yQhQsXfujnCAAAAABd6SMRFx955JEcf/zx+clPfvK+415//fUcd9xxueuuu9KvX7/81V/9VV5//fVccskl+d73vrfe+Obm5px22mm57rrrsmrVqhx22GGpra3NjTfemFNPPTWrV69e7zbXXHNNvv3tb+fll1/OoYcemkGDBuXuu+/O8ccfn//8z//ssOcMAAAAAJ2t28fFr371qznttNMyf/78nHDCCenTp897jp04cWIWL16c4447Lr/85S9zzTXX5N57782uu+6aqVOnZvbs2e3G33777Zk1a1bq6+tz77335uqrr86vfvWrHH744Xnuuedyww03tBu/YMGCXHfddamrq8u//Mu/5Nprr82tt96aiy66KEuXLs3FF1/cKa8BAAAAAHSGbh8Xf/e73+UTn/hEZsyYkb//+79PZeWGn/LixYvT0NCQrbbaKhdddFF5+7bbbpvjjjsuSXLLLbe0u8306dOTJBdeeGF69+6dJKmqqsro0aOTJNOmTcvatWvL42fMmJFSqZRTTz01gwcPLm8/8cQTU1dXl6eeeirz5s3rgGcNAAAAAJ2velNPoLPdfPPNGTZs2J8d98QTT6SlpSUHHXRQtt566/L2t99+OzNmzCiPaW5uTnV1dV577bW8/PLL6dOnTw4++OB293X99dcnSZYuXZq5c+dmv/32S5I8/vjjSZIjjjhivTmuXLkyybpDuOvr64s9WQAAAADoQt1+z8WNCYtJ8uKLL25w/OTJk7N48eLU1tZm5cqV5QvBLFiwIElSX1/fbm/ImTNn5je/+U169uzZbtyqVauycOHCVFdXZ++99y6P//3vf58pU6aUx8+fP7/I0wQAAACALtft91zcWIsWLUqS7LTTTuVtjz76aO68886cdNJJeeaZZzJ//vwsWrQogwYN2uD4xYsXZ9KkSRkyZEj222+/3HrrreVxixYtSqlUynbbbZeampokSUtLS77zne+kqqoqY8aMyeTJk8vju0KpVEpTU1OXPV5naW5uTm1tbZKkpVRKqaW0iWe08drOtTv8LDa1NWvWbPBrNl+tv7ullo7/3W0pldp9XdnScfftd3fL4r2BtnxuoJX3BtqyHmhlLXS8zvzM39laKt+db3Nz8yacSccplTr+ZyAu/o/ly5cnSfmCL8uXL893v/vdDBgwIBdccEHOOOOMduP+dHySjB8/PitWrMjEiRPz8MMPtxu3YsWK9cZPnTo1c+bMydixYzN06NB247pCc3Nz5s6d22WP11lqamqy7777JklWvbMyy5tWbeIZbbzK2nffnObNm+cvrw7k/KWbv7a/uytXrujU392VKzv2vdXv7pbLewM+N7Ah3htoy3qglbXw4XXlZ/7OUFnbs/z1Sy+95O/e99DtD4veWK3/+tu6V+EVV1yRV199NePHj0+vXr3KF2xZtWrVBsffc889aWhoyOjRozNs2LA/O37hwoW56qqrMnz48Jx88snrjQcAAACAzZ09F/9HdfW6l6KioiKzZ8/O7bffnqOPPjqf+cxnkqy7CnTybhxs/X9FRUXefPPNTJgwIYMGDcpZZ521wfGt95+s2wV17NixaW5uzsSJE1NZWVk+b2Pr+K5QXV2dIUOGdNnjdZa2uyb33KouLTVbzrKuq+lR/tqFfD68NWvWlP91sb6+vkt/n/hw6up6dfjvbkupVN5jsa6uVyorKjrsvv3ublm8N9CWzw208t5AW9YDrayFztMZn/k7W8/q2vLXe+21V7u2s6V64YUXOvwQ7y3/VekgvXr1SpIsWbIkkyZNSv/+/XPRRReVv9+662vruLq6uiTrDmOeMGFCGhsbM2XKlPTose5DX+sPqnVc6/9XrlyZ2267LbNnz855552XPffcc4Pju0JFRUX53AfdRWVFRSoqOy4gdLa2c+1uP4tNraamxmu6Bamo7Pjf3bbnWOzo9wa/u1su7w205XMDrbw30Jb1QCtroWN1xmf+ztZ2B4Xq6upusR4qOnCni1bi4v/YYYcdkiTf//7309jYmKuvvjrbbLNN+ftvvvlmkmTAgAFJkh133DFJcv/996exsTEnnXRS9t9///L4JUuWJEl23nnnJO9e+GXRokW54oorMmTIkHz9619fb3zr/QMAAADA5s45F//HHnvskSRpbGzMEUcckaOOOqr8vZaWlrz44oupra3NwIED1xvfetGXtubPn59k3W6zyboLufTv3z9NTU1ZvXp1Jk6c2G532tbxgwcP7qRnCAAAAAAdS1z8H4ccckiSdbuHjh07tt33Hn300axYsSKf+tSnyrvA7rnnnuW9F88///zy4dJJsnTp0syaNSt1dXUZPnx4efvBBx+cJDn22GMzbNiwdo9x3333JUk+/elPd/AzAwAAAIDOIS7+j0GDBuWAAw5IqVTKr3/96/L2xsbGXHbZZUmSE044od1tjj/++CTrwmCpVEqSrF27NpdeemlWr16dkSNHtjsef+TIkUmSJ598Mm+99VZ5+/Tp0/Pcc89l6NCh7WIkAAAAAGzOuv05F59//vlMmDCh/Ofly5cnScaMGVMOf+PGjcvQoUMzbty4jBo1KpMnT87999+f7bffPrNnz05jY2OOPPLIHH744e3u+2tf+1oaGhrS0NCQESNGpL6+Ps8//3xeeeWVDBw4MGeeeWa78QcddFCOOeaY3HXXXfnCF76QAw44IG+88UbmzJmTHj16ZPz48Z38agAAAABAx+n2cXHZsmWZM2fOett/+9vfthuTJPvss09+9rOf5aqrrsrTTz+duXPnZpdddsno0aMzevTo9e6jV69emTZtWq655po8+OCDaWhoSL9+/TJq1KiMGTMmffv2Xe82EydOzN57750777wzDz30UOrq6nLYYYdlzJgxqa+v78BnDgAAAACdq9vHxQMPPLB8sZSNsddee+Xaa6/d6PF9+/bNuHHjMm7cuI0aX1VV9Z6xEgAAAAC2JM65CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUUr2pJ7C5GT9+fGbMmPGe358/f367P7/++uu58sor8+ijj2b58uUZNGhQTjjhhIwaNWqDt29qasqPf/zj3H333Xnttdey/fbb56ijjsrZZ5+drbbaqkOfCwAAAAB0JnHxT7z99ttJkr/6q79Kv3793nfs66+/nuOOOy6LFy/O3nvvnU9+8pN56qmncskll+Sll17KxRdf3G58c3NzTjvttMyaNSs77LBDDjvssMyfPz833nhjnn766UybNi09evTotOcGAAAAAB1JXPwTy5YtS5JccMEF2Weffd537MSJE7N48eIcd9xxmTRpUpKksbExI0eOzNSpU/P5z38+Bx54YHn87bffnlmzZqW+vj7Tp09P7969s3bt2pxzzjl54IEHcsMNN+Tss8/uvCcHAAAAAB3IORf/ROuei3369HnfcYsXL05DQ0O22mqrXHTRReXt2267bY477rgkyS233NLuNtOnT0+SXHjhhendu3eSpKqqKqNHj06STJs2LWvXru2YJwIAAAAAnUxc/BOtey5uvfXW7zvuiSeeSEtLSw466KB2Y99+++3yORufeOKJNDc3J0lee+21vPzyy+nTp08OPvjgdvd1/fXXJ0mWLl2auXPndthzAQAAAIDOJC7+ibfffjtVVVXlPQvfy4svvpgkGTZsWLvtkydPzuLFi1NbW5uVK1fmj3/8Y5JkwYIFSZL6+vpUVr77ss+cOTO/+c1v0rNnz3bjAAAAAGBz55yLf2LZsmWpqanJj370o7z++uupra3N4MGDc+SRR2abbbYpj1u0aFGSZKeddipve/TRR3PnnXfmpJNOyjPPPJP58+dn0aJFGTRo0AbHL168OJMmTcqQIUOy33775dZbby2P6wqlUilNTU1d9nidpbm5ObW1tUmSllIppZbSJp7Rxms71+7ws9jU1qxZs8Gv2Xy1/u6WWjr+d7elVGr3dWVLx923390ti/cG2vK5gVbeG2jLeqCVtdDxOvMzf2drqXx3vq1Hpm7pSqWO/xmIi22sWbMm77zzTpLkmmuuafe9yy+/PD/4wQ/y13/910mS5cuXJ3n33IzLly/Pd7/73QwYMCAXXHBBzjjjjHbj/nR8kowfPz4rVqzIxIkT8/DDD7cb1xWam5u7xWHYNTU12XfffZMkq95ZmeVNqzbxjDZeZe27b07z5s3zl1cHmjdv3qaeAn9G29/dlStXdOrv7sqVKzr0/vzubrm8N+BzAxvivYG2rAdaWQsfXld+5u8MlbU9y1+/9NJL/u59Dw6LbqNUKuXKK6/ML3/5yzzzzDP5f//v/+VnP/tZjjzyyCxfvjznnntuFi5cmOTdfy2uqalJklxxxRV59dVXM378+PTq1at8WPWqVas2OP6ee+5JQ0NDRo8enWHDhq03HgAAAAA2d/ZcbKO2tjYjRoxot23ffffNNddck//7f/9v/u3f/i233357vvWtb6W6et1LV1FRkdmzZ+f222/P0Ucfnc985jNJ1l0FOnk3Jrb+v6KiIm+++WYmTJiQQYMG5ayzztrg+K5QXV2dIUOGdNnjdZa2uyb33KouLTVbzrKuq+lR/rq+vn4TzqR7WLNmTflfF+vr67v094kPp66uV4f/7raUSuU9FuvqeqWyoqLD7tvv7pbFewNt+dxAK+8NtGU90Mpa6Dyd8Zm/s/Wsri1/vddee5Vb0JbshRde6PBDvLf8V6WLjBgxIv/2b/+W3/3ud0mSXr16JUmWLFmSSZMmpX///rnooovK41t3lW0dV1dXlyRZsWJFJkyYkMbGxkyZMiU9eqz7kNj6g20d1xUqKirK5z7oLiorKlJR2XEBobO1nWt3+1lsajU1NV7TLUhFZcf/7rY9x2JHvzf43d1yeW+gLZ8baOW9gbasB1pZCx2rMz7zd7a2OyhUV1d3i/VQ0YE7XbQSFzfStttumyTlczLusMMOSZLvf//7aWxszNVXX93ugi9vvvlmkmTAgAFJkh133DFJcv/996exsTEnnXRS9t9///L4JUuWJEl23nnnzn0iAAAAANBBnHNxI7Wea7E1Eu6xxx5JksbGxhxxxBE56qijymNbWlry4osvpra2NgMHDlxvfOtFX9qaP39+knW72QIAAADAlkBc3Ei//OUvkySHHHJIu/9XVFRk7Nix7cY++uijWbFiRT71qU+Vd5ndc889y2Hy/PPPLx8unSRLly7NrFmzUldXl+HDh3f6cwEAAACAjiAutvHd7343zz33XLttrVeQnjNnTgYMGJAvf/nLSZJBgwblgAMOSKlUyq9//evy+MbGxlx22WVJkhNOOKHdfR1//PFJkvvuuy+lUilJsnbt2lx66aVZvXp1Ro4c2S2O3wcAAADgo8E5F9u49957c/vtt+djH/tY6uvr06tXryxYsCALFixIv379cu2112arrbYqjx83blxGjRqVyZMn5/7778/222+f2bNnp7GxMUceeWQOP/zwdvf/ta99LQ0NDWloaMiIESNSX1+f559/Pq+88koGDhyYM888s6ufMgAAAAAUZs/FNm6++eaMGjUqW221VWbNmpX77rsvK1euzKmnnppf/OIXGTZsWLvx++yzT372s5/l85//fF5++eU89NBD2XbbbXPBBRfkhz/84Xr336tXr0ybNi0nn3xy3nnnnTQ0NOSdd97JqFGjctttt6Vv375d9VQBAAAA4EOz52IbQ4cOzfjx4z/Qbfbaa69ce+21Gz2+b9++GTduXMaNG/dBpwcAAAAAmxV7LgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAP9/e/ceZGVd/wH8vVxW5CJCiNfEa6I7qSj+SmmmsD8omnHUJDK8jFpOlrfUyXQiUxKxxkxSU6dyKMShi100FVdJCWO94YWQFvMWQ+o4grEgshf294ezq+QNnj2H5Vlerxln4DznfPe77Oe8d32f5zwLQCHKRQAAAACgEOUiAAAAAFCIchEAAAAAKES5CAAAAAAUolwEAAAAAApRLgIAAAAAhSgXAQAAAIBClIsAAAAAQCHKRQAAAACgEOUiAAAAAFCIchEAAAAAKES5CAAAAAAUolwEAAAAAApRLgIAAAAAhfTp7g0AAABAd+vbt293bwGglJSLAAAAbNVqa2tz4IEHdvc2AErJ26IBAAAAgEKcuQgAAEBpzHzm4axpXVfRNdvXt+eNN9YkSfr3H5CaXjUVXX9An21ywr7/V9E1AbYUykUAAABKY03ruqxuqXy5uLr5zSTJ+r59Kl4uAvRk3hYNAAAAABSiXAQAAAAAClEuAgAAAACFKBcBAAAAgEKUiwAAAABAIcpFAAAAAKAQ5SIAAAAAUIhyEQAAAAAoRLkIAAAAABSiXAQAAAAAClEuAgAAAACFKBcBAAAAgEKUiwAAAABAIcpFAAAAAKAQ5SIAAAAAUIhyEQAAAAAoRLkIAAAAABSiXAQAAAAAClEuAgAAAACFKBcBAAAAgEKUiwAAAABAIcpFAAAAAKAQ5SIAAAAAUIhyEQAAAAAoRLkIAAAAABSiXAQAAAAAClEuAgAAAACFKBcBAAAAgEKUiwAAAABAIcpFAAAAAKAQ5SIAAAAAUIhyEQAAAAAoRLkIAAAAABSiXAQAAAAAClEuAgAAAACFKBcBAAAAgEKUiwAAAABAIcpFAAAAAKAQ5WI3efzxx3Paaadl9OjROfTQQ3PyySenoaGhu7cFAAAAABtNudgN5s+fnxNOOCEPPvhg6urqcthhh+Wxxx7LKaeckrvvvru7twcAAAAAG6VPd29ga9Pc3JyLL744ra2tufLKK3P00UcnSZ588slMmjQp3/3udzNmzJgMGjSoezcKAAAAAB/CmYub2b333ptXXnklo0aN6iwWk+Sggw7KYYcdlqamptx2223dt0EAAAAA2EjKxc3swQcfTJKMGzdug9ufeOKJzmsuzps3b7PvCwAAAAA2lXJxM3vmmWeSJAcccEDnbR1vla6trU2SLF26tFv2BgAAAACbwjUXN7Ply5cnSXbeeefO237605/m2WefzZQpUzJ58uS8+uqraW5u7iwbq6W9vT3Nzc1V/RibQ2tra+e/Vf8+1f03q7QBvd7eb0/4WnS31tbW9O3bt/PPbPk6nrsDetWmvXd7Rdde36s9vWr7JUn69alNr5qaiq3tuVsusoF3amtr6/yznxu2brKhfMr6c0Pi+VsmsqHyqvncrbZ3/qzQU+ahvb3yX4Oa9mqsyvs66KCD8uabb6ahoSFDhgzJ4sWL86UvfSljx47NtddemwMOOCBtbW2dx6th4cKFVRmm7tQR/gAAAACV1tLS0t1bqKiampoccsghFVnLmYubWcerVH379k1LS0suuuiiDBgwIJdcckmSZMCAAVm1alXWrVtXtT30tGIx6XlPcgAAAIBqqWQ3pFzczPr06ZPm5ubU1NTkhhtuSGNjY6ZOnZoddtghSdKr11uXwazmmXi9evXK+vXrU1NTkz59jAAAAADA1qC1tTXt7e2d/VMlaJY2s/79+6e5uTlPPPFEbrzxxowZMyZf/OIXO493vIe/f//+VdvDqFGjqrY2AAAAAFsPvy16M9txxx2TJOeff3769u2byy67rPPYunXrsnr16gwZMiTbbrttd20RAAAAADaKcnEz22uvvZIkK1euzHnnnZfddtut81hjY2OSZN999+2WvQEAAADAplAubmZHHHFEkmTPPffMpEmTNjg2Z86cJMmnPvWpzb4vAAAAANhUysXN7POf/3wGDhyYZcuWZfHixZ23L1q0KDNnzky/fv1y7LHHduMOAQAAAGDj1LRX8ndPs1Fuu+22XHTRRamtrc2YMWOSJPPnz09LS0smT56cE044oZt3CAAAAAAfTrnYTebOnZuf//znWbJkSZJkv/32yymnnJJx48Z1884AAAAAYOMoFwEAAACAQlxzEQAAAAAoRLkIAAAAABSiXAQAAAAAClEuAgAAAACFKBcBAAAAgEKUiwAAAABAIcpFAAAAAKAQ5SIAAAAAUIhyEQAAAAAoRLkIAAAAABSiXAQAAAAAClEuAgAAAACFKBcBAAAAgEL6dPcG4J1aWlry4x//OL/85S+TJPfdd1922223Lq/b3Nycn/3sZ7njjjvy8ssvZ4cddsjnPve5nHXWWdl22227vD7VUY15WLFiRQ4//PD3PX7mmWfmrLPO6tLHoLKWLFmS2bNnZ8GCBXnppZfSq1ev7LHHHhk/fnxOOumk9OvXr0vry4fyqOYsyIbyaG9vz7x58/K73/0uS5cuzcsvv5xBgwZlxIgRGT9+fCZOnJg+fbr2I65cKI9qz4NsKL/nnnsuRx11VFpaWnLMMcdk2rRpXVpPPpRbJedBPpTHpZdemlmzZr3v8cbGxi6tLxeUi2xB5s2bl6uuuir//Oc/K7pua2trvva1r6WhoSHDhw/PkUcemcbGxvziF7/II488kpkzZ2abbbap6Mek66o1D6tWrUqSDB8+PGPGjHnX8f3337+iH4+umTdvXk4//fS0t7dnxIgRGTt2bNauXZuFCxfmqquuyl133ZVf//rXGThwYKH15UN5VHsWZEN5LF++PKeffnp69+6durq61NXVZeXKlXn44Yfz6KOPZu7cubnpppvSu3fvQuvLhXKp9jzIhvKbNm1aWlpaKrKWfCi/Ss6DfCiPjq/Vpz/96QwdOrSia8uFtygX2SKcfPLJaWhoSE1NTY4//vj85S9/6QyArpo9e3YaGhoycuTI3HLLLRk4cGDa2tpy9tln5957781NN93kFaUtTDXnoWOdgw8+uMuvXFN9TU1NGTVqVC644IIceuihnbevXLkyJ554Yp5++uncfPPNhZ/D8qE8qj0LsqE8+vfvnzPOOCMTJ07Mzjvv3Hn7v/71rxx//PGZP39+/vCHP+S4444rtL5cKJdqz4NsKLd58+blgQceSP/+/fPGG290eT35UG6Vngf5UB5NTU1JkvPPPz/77bdfRdeWC29xzUW2CIsXL84hhxySWbNm5fvf/3569arcaN5yyy1Jkm9/+9udZ7T07t07p556apJk5syZaWtrq9jHo+uqOQ8d31gGDRpUsTWpns9+9rOZNWvWBmVSkgwZMqTzOfy3v/2t8PryoTyqPQuyoTyGDh2ac889d4MiKUn22WefTJgwIclb/wNZlFwol2rPg2wor9bW1lxxxRXp06dPjj322IqsKR/KqxrzIB/Ko6MI3m677Sq+tlx4i3KRLcKMGTNy66235pBDDqnoui+//HKeffbZbLfddu+6HsaNN96YJHn99dezaNGiin5cuqZa85BU9xsLldevX7/U1NS857GPfOQjSVL4rFb5UC7VnIV3PlY2lNvuu++eJHnzzTcLPV4u9CxdnYdENpTZzJkz89xzz+X444/Pnnvu2eX15EO5VXoeEvlQJtUqguXC25SLbBHq6uqqsu7SpUuTJCNHjtzg7Lc//vGPeeCBBzov/t9xP7YM1ZqHxCuMPcl//vOfJMmuu+5a6PHyoefo6iwksqGneP7555Mke++9d6HHy4WepavzkMiGslq5cmWuu+66DB48OGeeeWZF1pQP5VWNeUjkQ5msWrUqvXv3Lnxt7vcjF97mmov0aMuXL0+S7LTTTp23vfrqq5k6dWr233//HHzwwbn11ls770fP1/EK45IlSzJ16tSsXbs2w4YNyxFHHJHDDjusm3fHppgzZ06SFD7DVT70HF2dhUQ29AQPPfRQZs2alX79+uUrX/lKoTXkQs9RiXlIZENZXXPNNVm1alUuvvjibL/99hVZUz6UVzXmIZEPZdLU1JS+ffvmuuuuyyuvvJLa2trsu+++GTduXJdmQi68TblIj7Z69eokG56qfumll2bNmjW5/PLLc//9929wP3q+jlcY6+vrN7j9+uuvz9ixY/OTn/yk8xUmtlwLFizIggULss0223ReU2tTyYeeoRKzkMiGspoyZUpWrFiRZcuWZdGiRdl9990zderUfPSjHy20nlwot0rPQyIbyqixsTG/+c1vsscee3SpWP5f8qGcqjUPiXwoi5aWlqxduzZJMn369A2O/fCHP8xVV12Vz3zmM4XWlgtv87ZoerTm5uYkSd++fZMkd955Z+rr63Pqqaemrq6u87TorlyLh3I58sgjM2PGjDzwwANZtGhR5s6dmx/84AcZNmxY/vrXv+aKK67o7i3yIZqamjJ58uQkyRlnnJHhw4cXWkc+lF+lZiGRDWV1xx135M477+y8ltH++++foUOHFl5PLpRbpechkQ1lNHXq1LS1teXCCy/sfC5Xgnwop2rNQyIfyqK9vT1XX311br/99jz66KN57LHH8tvf/jbjxo3L6tWrc+6552bZsmWF1pYLb1Mu0qN1PMlramqyYsWKTJkyJXvssUfntTZ69+69wf3o+Q466KB88pOfzE477ZTa2trsuuuumTBhQm644YYkye9///ut4pWlsmpvb8+FF16YZcuWZdSoUfnqV79aeC35UG6VnIVENpTVQw89lEWLFuXuu+/ON77xjcydOzcTJ07M4sWLC60nF8qt0vOQyIayqa+vT0NDQw4//PAceeSRFV1bPpRPNechkQ9lUVtbm/Hjx+djH/tYBg0alIEDB+bAAw/M9OnTM3bs2KxduzazZ88utLZceJtykR6tf//+SZI1a9ZkypQpWblyZS6//PJss802SZLW1tYN7sfW6+Mf/3hGjBiRlpaWNDY2dvd2eB/XX3997rvvvgwbNizXXHNNl75Ry4dyq+QsfBDZsOWrra3NnnvumXPOOSdnnnlmmpqaCp8tIhfKr5Lz8EFkw5anubk5V155ZXr16pXvfOc7FV9fPpRLtefhg8iH8hg/fnySFH4RSi68zTUX6dF23HHHJMk999yTlStXZtKkSRk9enTn8ddeey1J137DKD3HkCFD8uKLL24Vp62X0f33359rr702ffr0yTXXXNP5/C5KPpRXpWfhw8iG8vjCF76Qq6++Oo8//niam5tTW1u7SY+XCz1LV+fhw8iGLcuTTz6ZZcuWZfDgwbn++us3OPbvf/87SfLwww/n7LPPzq677poLL7xwk9aXD+VS7Xn4MPKhHIYMGZIknddk3FRy4W3KRXq0vfbaK0mycuXK7LLLLjn//PM3ON7xStI+++yz2ffGlqfjWhvVLirYdC+88EIuuOCCrF+/PhdffPEG37SLkg/lVI1Z+DCyoTw6rrvZ2tqaVatWZdiwYZv0eLnQs3R1Hj6MbNiyrF+/Pkny3//+N3PmzHnP+yxfvjzLly/PyJEjN3l9+VAu1Z6HDyMfyqGrXye58DZvi6a0VqxYkRNOOCGjR4/Odddd95732XvvvTuD4rzzzsuAAQM6j73++utpaGhI//79M2rUqM2yZ6pnY+bhg8yfPz+vvfZahg8fnr333rsKO6SoNWvWdL617eijj86JJ574oY+RDz1TtWbhg8iGcmloaEiSDBo06F2/yEMubH26Og8fRDZseT7xiU+ksbHxPf/ruP7ZMccck8bGxvzpT3/a4LHyoeep9jx8EPlQHrfffnuS5IgjjnjXMbmwaZSLlNaMGTPyyCOPpKmpKdOnT8+LL774nvc77rjjkiRz5sxJe3t7kqStrS2XXXZZ1q1blwkTJlT8bTJsfhszD/fcc09+9atfpampaYPbn3/++VxyySVJkq9//eupqanZLHtm41x00UV55plnUldXl8suu2yjHiMfeqZqzYJsKJdp06Zl/vz5nWeldFiyZEnnbw8/+uij06vXhj/myoWeqZrzIBu2HvKBd5IPPcv3vve9PPXUUxvc1vEbpBcuXJhddtklRx111LseJxc2jbdFs0V4+umnM2XKlM6/d/xWrXPOOafziTh58uQccMABm7z2aaedlvr6+tTX12f8+PEZOXJknn766bzwwgsZMWJEvvnNb1bmk6BiqjUPL730UqZOnZof/ehHqauryy677JI33ngjDQ0NWbt2bSZMmJBJkyZV7hOhy+rr6zvfyjJ48ODOH9b+17Rp0wqtLx/Ko5qzIBvKZcGCBbn55pszZMiQ1NXVZfvtt8/y5cvz1FNPpa2tLaNHj863vvWtwuvLhXKp5jzIBv6XfKCDfCiPu+66K7Nnz87OO++ckSNHZsCAAVm6dGmWLl2aoUOH5tprr822225beH258BblIluEpqamLFy48F23/+Mf/9jgPu908skn57HHHsuSJUty6qmnZsSIEe+59oABAzJz5sxMnz499913X+rr6zN06NB8+ctfzjnnnJPBgwdX9pOhy6o1DxMnTkxtbW3mzJmTxsbGLFq0KP369cuBBx6YSZMmZdy4cZX/ZOiSd36d//73v7/v/f63UJIPPU81Z0E2lMsVV1yRP//5z3nwwQfz+OOP580338zAgQNz6KGH5qijjsqxxx6b3r17v+txcqFnquY8yIath3zgneRDzzJjxozMnj07Dz/8cBoaGtLa2podd9wxJ510Uk4//fTssMMO7/k4ubBpato7ztsEAAAAANgErrkIAAAAABSiXAQAAAAAClEuAgAAAACFKBcBAAAAgEKUiwAAAABAIcpFAAAAAKAQ5SIAAAAAUIhyEQAAAAAoRLkIAAAAABSiXAQAAAAAClEuAgAAAACFKBcBAAAAgEKUiwAAAABAIcpFAAAAAKAQ5SIAAAAAUIhyEQAAAAAoRLkIAAAAABSiXAQAAAAAClEuAgAAAACFKBcBAAAAgEKUiwAAAABAIcpFAAAAAKAQ5SIAAAAAUMj/A+Y0omUInbq3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(len_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 20,   0,   0,   0,   0],\n",
       "       [ 43,   1,  44,   0,   0],\n",
       "       [ 43,   1,  44,   0,   0],\n",
       "       ...,\n",
       "       [  7,   0,   0,   0,   0],\n",
       "       [133,   0,   0,   0,   0],\n",
       "       [ 18,   0,   0,   0,   0]])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "padded_category = pad_sequences(category,padding='post',maxlen=5,value='[PAD]',dtype=object)\n",
    "encoded_category = np.apply_along_axis(encode_tokens,0,padded_category)\n",
    "encoded_category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### publshr, author encoding\n",
    "- publshr는 k = 50 기준으로 전처리 했었음\n",
    "- author에 대해서도 파악 필요"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Author\n",
       "데이빗 조 (David Cho)    49\n",
       "길벗 R&D               43\n",
       "ETS                  37\n",
       "이동기                  37\n",
       "해커스어학연구소             36\n",
       "                     ..\n",
       "초(정솔)                 1\n",
       "구수환                   1\n",
       "남충희                   1\n",
       "요시다 타로                1\n",
       "시와스 유키                1\n",
       "Name: count, Length: 5555, dtype: int64"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors = bookinfo.Author\n",
    "counts = authors.value_counts()\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(counts.values,bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Author\n",
       "데이빗 조 (David Cho)    49\n",
       "길벗 R&D               43\n",
       "ETS                  37\n",
       "이동기                  37\n",
       "해커스어학연구소             36\n",
       "                     ..\n",
       "문학수                   2\n",
       "고은정                   2\n",
       "키네코 시바이               2\n",
       "존 윌리엄스                2\n",
       "박서련                   2\n",
       "Name: count, Length: 1410, dtype: int64"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = counts >= 2\n",
    "counts[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Author\n",
       "일본보그사        2\n",
       "스타니스와프 렘     2\n",
       "서대중          2\n",
       "백수린          2\n",
       "혼Job취업연구소    2\n",
       "            ..\n",
       "문학수          2\n",
       "고은정          2\n",
       "키네코 시바이      2\n",
       "존 윌리엄스       2\n",
       "박서련          2\n",
       "Name: count, Length: 764, dtype: int64"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = counts == 2\n",
    "counts[cond]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**만약 SalesPoint를 반영한다면?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "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>SalesPoint</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Author</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ETS</th>\n",
       "      <td>1138125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Cho</th>\n",
       "      <td>870206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>데이빗 조 (David Cho)</th>\n",
       "      <td>819133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>유시민</th>\n",
       "      <td>743504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>최태성</th>\n",
       "      <td>613568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>윤재근</th>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>정영택</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>생명의삶</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>정현웅</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>송난호</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5555 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SalesPoint\n",
       "Author                       \n",
       "ETS                 1138125.0\n",
       "David Cho            870206.0\n",
       "데이빗 조 (David Cho)    819133.0\n",
       "유시민                  743504.0\n",
       "최태성                  613568.0\n",
       "...                       ...\n",
       "윤재근                      21.0\n",
       "정영택                      20.0\n",
       "생명의삶                     17.0\n",
       "정현웅                      17.0\n",
       "송난호                      15.0\n",
       "\n",
       "[5555 rows x 1 columns]"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvtb = pd.pivot_table(data=bookinfo,index='Author',values='SalesPoint',aggfunc=np.sum)\n",
    "pvtb = pvtb.sort_values(by='SalesPoint',ascending=False)\n",
    "pvtb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(pvtb,bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "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>SalesPoint</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Author</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ETS</th>\n",
       "      <td>1138125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Cho</th>\n",
       "      <td>870206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>데이빗 조 (David Cho)</th>\n",
       "      <td>819133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>유시민</th>\n",
       "      <td>743504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>최태성</th>\n",
       "      <td>613568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCS 정보처리기술사 연구회</th>\n",
       "      <td>10105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>러셀 로버츠</th>\n",
       "      <td>10102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>김도윤</th>\n",
       "      <td>10061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>원아영</th>\n",
       "      <td>10041.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>김웅</th>\n",
       "      <td>10012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1072 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SalesPoint\n",
       "Author                       \n",
       "ETS                 1138125.0\n",
       "David Cho            870206.0\n",
       "데이빗 조 (David Cho)    819133.0\n",
       "유시민                  743504.0\n",
       "최태성                  613568.0\n",
       "...                       ...\n",
       "NCS 정보처리기술사 연구회       10105.0\n",
       "러셀 로버츠                10102.0\n",
       "김도윤                   10061.0\n",
       "원아영                   10041.0\n",
       "김웅                    10012.0\n",
       "\n",
       "[1072 rows x 1 columns]"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = pvtb['SalesPoint'] >= 10000\n",
    "pvtb[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "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>SalesPoint</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Author</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ETS</th>\n",
       "      <td>1138125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Cho</th>\n",
       "      <td>870206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>데이빗 조 (David Cho)</th>\n",
       "      <td>819133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>유시민</th>\n",
       "      <td>743504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>최태성</th>\n",
       "      <td>613568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>장문정</th>\n",
       "      <td>5019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>미셸 오바마</th>\n",
       "      <td>5017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>이해수</th>\n",
       "      <td>5014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>박상배</th>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>에이미 샤</th>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1754 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SalesPoint\n",
       "Author                       \n",
       "ETS                 1138125.0\n",
       "David Cho            870206.0\n",
       "데이빗 조 (David Cho)    819133.0\n",
       "유시민                  743504.0\n",
       "최태성                  613568.0\n",
       "...                       ...\n",
       "장문정                    5019.0\n",
       "미셸 오바마                 5017.0\n",
       "이해수                    5014.0\n",
       "박상배                    5000.0\n",
       "에이미 샤                  5000.0\n",
       "\n",
       "[1754 rows x 1 columns]"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = pvtb['SalesPoint'] >= 5000 \n",
    "pvtb[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "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>SalesPoint</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Author</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ETS</th>\n",
       "      <td>1138125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Cho</th>\n",
       "      <td>870206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>데이빗 조 (David Cho)</th>\n",
       "      <td>819133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>유시민</th>\n",
       "      <td>743504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>최태성</th>\n",
       "      <td>613568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>스톤뮤직엔터테인먼트</th>\n",
       "      <td>3018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>진경혜</th>\n",
       "      <td>3016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>이찬수</th>\n",
       "      <td>3012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>앙리 르페브르</th>\n",
       "      <td>3008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>가마타 미노루</th>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2293 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SalesPoint\n",
       "Author                       \n",
       "ETS                 1138125.0\n",
       "David Cho            870206.0\n",
       "데이빗 조 (David Cho)    819133.0\n",
       "유시민                  743504.0\n",
       "최태성                  613568.0\n",
       "...                       ...\n",
       "스톤뮤직엔터테인먼트             3018.0\n",
       "진경혜                    3016.0\n",
       "이찬수                    3012.0\n",
       "앙리 르페브르                3008.0\n",
       "가마타 미노루                3000.0\n",
       "\n",
       "[2293 rows x 1 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = pvtb['SalesPoint'] >= 3000 \n",
    "pvtb[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cond = pvtb['SalesPoint'] < 10000\n",
    "temp = pvtb[cond]\n",
    "sns.histplot(temp,bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cond = pvtb['SalesPoint'] < 5000\n",
    "temp = pvtb[cond]\n",
    "sns.histplot(temp,bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cond = pvtb['SalesPoint'] < 4500\n",
    "temp = pvtb[cond]\n",
    "sns.histplot(temp,bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 491,
       "width": 651
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cond = pvtb['SalesPoint'] < 3000\n",
    "temp = pvtb[cond]\n",
    "sns.histplot(temp,bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "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>SalesPoint</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Author</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ETS</th>\n",
       "      <td>1138125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Cho</th>\n",
       "      <td>870206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>데이빗 조 (David Cho)</th>\n",
       "      <td>819133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>유시민</th>\n",
       "      <td>743504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>최태성</th>\n",
       "      <td>613568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>엘리자베스 짐머만</th>\n",
       "      <td>2504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>마사 피퍼. 윌리엄 피퍼</th>\n",
       "      <td>2503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>함윤미.문혜진</th>\n",
       "      <td>2503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>신붕섭</th>\n",
       "      <td>2502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>가토우 쇼우지</th>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2500 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SalesPoint\n",
       "Author                       \n",
       "ETS                 1138125.0\n",
       "David Cho            870206.0\n",
       "데이빗 조 (David Cho)    819133.0\n",
       "유시민                  743504.0\n",
       "최태성                  613568.0\n",
       "...                       ...\n",
       "엘리자베스 짐머만              2504.0\n",
       "마사 피퍼. 윌리엄 피퍼          2503.0\n",
       "함윤미.문혜진                2503.0\n",
       "신붕섭                    2502.0\n",
       "가토우 쇼우지                2500.0\n",
       "\n",
       "[2500 rows x 1 columns]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = pvtb['SalesPoint'] >= 2500 \n",
    "pvtb[cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ETS', 'David Cho', '데이빗 조 (David Cho)', '유시민', '최태성', '세이노(SayNo)',\n",
       "       '무라카미 하루키', '해커스어학연구소', '히가시노 게이고', '김호연',\n",
       "       ...\n",
       "       '아카시 칵카쿠', '앤드루 스탠턴', '마이클 브린', '니토리 고이치', '홍민지', 'KBS <생로병사의 비밀> 제작팀',\n",
       "       '소정현', '두산동아 편집부', '죠 키이코', '원보람'],\n",
       "      dtype='object', name='Author', length=3542)"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_top_slspnt= pvtb[pvtb['SalesPoint']>=1000].index\n",
    "author_top_slspnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['데이빗 조 (David Cho)', '길벗 R&D', 'ETS', '이동기', '해커스어학연구소', 'David Cho',\n",
       "       '베르나르 베르베르', '전한길', '조정래', '최태성',\n",
       "       ...\n",
       "       '타코', '박태균', '해커스JLPT연구소', '김용석', '한일', '문학수', '고은정', '키네코 시바이',\n",
       "       '존 윌리엄스', '박서련'],\n",
       "      dtype='object', name='Author', length=1410)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_top_cnt= counts[counts>=2].index\n",
    "author_top_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'FUNA',\n",
       " 'KEN',\n",
       " 'SD적성검사연구소',\n",
       " 'T&T교육교재개발팀',\n",
       " '강도운',\n",
       " '강영숙.박수정',\n",
       " '강효백',\n",
       " '고은정',\n",
       " '고종훈.신승욱',\n",
       " '곽동언',\n",
       " '구효서',\n",
       " '권지현',\n",
       " '김성혜',\n",
       " '김세영',\n",
       " '김송은.에듀플렉스 교육개발연구소',\n",
       " '김영준.김병태',\n",
       " '김용석',\n",
       " '김유향',\n",
       " '김지하',\n",
       " '김진구',\n",
       " '나츠미즈 리츠',\n",
       " '나카노 교코',\n",
       " '나카무라 하지메',\n",
       " '냔냐',\n",
       " '노가원',\n",
       " '니시 케이코',\n",
       " '다나카 요시키',\n",
       " '다카하시 루미코',\n",
       " '디모데 편집부',\n",
       " '레이 몽크',\n",
       " '로즈빈',\n",
       " '루스 보든',\n",
       " '류재현',\n",
       " '류향',\n",
       " '리무코로',\n",
       " '마르시아스 심',\n",
       " '마순자',\n",
       " '마에카와 유타카',\n",
       " '마이클 크라이튼',\n",
       " '메이비',\n",
       " '모리모토 슈',\n",
       " '모리하시 빙고',\n",
       " '문시현',\n",
       " '문언희',\n",
       " '발락',\n",
       " '배재민',\n",
       " '법조문연구회',\n",
       " '생명의말씀사 편집부',\n",
       " '성혜림',\n",
       " '손석춘',\n",
       " '손에스더',\n",
       " '송재정',\n",
       " '스즈키 히사시',\n",
       " '시사상식편집부',\n",
       " '시사영어사 편집부',\n",
       " '신일숙',\n",
       " '실천민속학회',\n",
       " '심우철.박진우',\n",
       " '아라키 신',\n",
       " '아오이 세키나',\n",
       " '아카마츠 켄',\n",
       " '아카필로 편집부',\n",
       " '아키야마 미즈히토',\n",
       " '안창현',\n",
       " '애덤 잭슨',\n",
       " '앨런 폴섬',\n",
       " '야마모토 아리',\n",
       " '야스다 스즈히토',\n",
       " '에듀스 인재개발원',\n",
       " '에듀윌 교육출판연구소',\n",
       " '에듀윌 상식연구소',\n",
       " '엘리자베트 루디네스코',\n",
       " '영진교재개발팀',\n",
       " '오바 츠쿠미',\n",
       " '오사카 미에코',\n",
       " '오우기 유즈하',\n",
       " '오자와 유키',\n",
       " '옥한흠',\n",
       " '윈글리쉬닷컴',\n",
       " '윌리엄 리스트 히트문',\n",
       " '유도주',\n",
       " '유미즈루 이즈루',\n",
       " '유연',\n",
       " '유우키 마사미',\n",
       " '유호철',\n",
       " '이동숙',\n",
       " '이와이 슌지',\n",
       " '이육사',\n",
       " '이자와 모토히코',\n",
       " '이정우',\n",
       " '이진욱',\n",
       " '이태종',\n",
       " '장재혁',\n",
       " '장정일',\n",
       " '잭 캔필드',\n",
       " '전국민주노동조합총연맹 교육선전실',\n",
       " '정채봉',\n",
       " '제석강',\n",
       " '조선희',\n",
       " '조영남',\n",
       " '조유진',\n",
       " '증권연수원',\n",
       " '직무종합수행능력평가연구소',\n",
       " '체루야 살레브',\n",
       " '최내경',\n",
       " '최란',\n",
       " '최연희',\n",
       " '최인원',\n",
       " '최일도',\n",
       " '최창호',\n",
       " '최현규',\n",
       " '카마타 토시오',\n",
       " '키미노 사쿠라코',\n",
       " '킨다이치 렌쥬로',\n",
       " '타치바나 히구치',\n",
       " '토노 타에',\n",
       " '토리코 치야',\n",
       " '피에르 제르마',\n",
       " '피터 슈워츠',\n",
       " '피터 제임스',\n",
       " '하리 멀리쉬',\n",
       " '한국간호과학회 기본간호학회',\n",
       " '한국테스트센터',\n",
       " '한창훈',\n",
       " '헤닝 만켈',\n",
       " '혼Job취업연구소',\n",
       " '홍종남',\n",
       " '황종휴'}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = (set(author_top_cnt).difference(set(author_top_slspnt)))\n",
    "display(len(temp))\n",
    "display(temp)\n",
    "display('김대중' in temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2260"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'황철곤',\n",
       " '앙드레 베르제.드 니 위스망',\n",
       " 'BLW 연구소',\n",
       " '우노 다카시',\n",
       " '문지웅',\n",
       " '네비게이토 편집부',\n",
       " '윤선현',\n",
       " '(주)아이비즈넷',\n",
       " '김현근',\n",
       " '장 자끄 상뻬 글',\n",
       " '로버트 기요사키 & 샤론 레흐트',\n",
       " '이유명호',\n",
       " '마이클 길모어',\n",
       " '김점선',\n",
       " '오비디우스',\n",
       " '조르조 바사니',\n",
       " '오키타 미즈호',\n",
       " '김옥남.송재현',\n",
       " '석지영',\n",
       " '제현주',\n",
       " '편혜영',\n",
       " '피터 마운트 샤스타',\n",
       " '최태성.장종근',\n",
       " '존 브라이트',\n",
       " '김명호',\n",
       " '크리스티네 뇌스트링거',\n",
       " '박경숙',\n",
       " '이하나',\n",
       " '앤소니 버제스',\n",
       " '장재화',\n",
       " '전우용',\n",
       " '백온유',\n",
       " '얀 케르쇼트',\n",
       " '손철주.이주은',\n",
       " '차민진(밍찌)',\n",
       " '대커 켈트너',\n",
       " '우에타니 부부',\n",
       " '고화정',\n",
       " '허진희',\n",
       " '이창수',\n",
       " '오현웅',\n",
       " '키노 씨네필 편집부',\n",
       " '진실의 힘 세월호 기록팀',\n",
       " '김그래',\n",
       " '서이레',\n",
       " '스티븐 크보스키',\n",
       " '한화순',\n",
       " '심상정',\n",
       " '임홍택',\n",
       " '크리스티나 순톤밧',\n",
       " '레일 라운즈',\n",
       " '정병열',\n",
       " 'Michael A. Putlack & e-Creative Contents',\n",
       " '이해수',\n",
       " '천장팅',\n",
       " '이유리',\n",
       " '호시노 미즈키',\n",
       " '조혜진',\n",
       " '이종구',\n",
       " '이선미',\n",
       " '네이선 퍼.수재너 하몬 퍼',\n",
       " '이븐 알렉산더',\n",
       " '성철.법정',\n",
       " '린다 유',\n",
       " '김리연',\n",
       " '쿠켄 편집부',\n",
       " '서울대학교 TEPS',\n",
       " '베네딕트 앤더슨',\n",
       " '장하성',\n",
       " '최윤식',\n",
       " '이원지',\n",
       " '황윤권',\n",
       " '임민찬',\n",
       " '문성희',\n",
       " 'JRC 중국어연구소',\n",
       " '세이카 료겐',\n",
       " '이병관',\n",
       " '성정아',\n",
       " '케빈 켈리',\n",
       " '미셸 오바마',\n",
       " '미셸 자우너',\n",
       " '강헌',\n",
       " '최희수.신영일',\n",
       " '권다현',\n",
       " '최승욱',\n",
       " '리디아 데이비스',\n",
       " '임용옥',\n",
       " '에쿠니 가오리.쓰지 히토나리',\n",
       " '강금주',\n",
       " '박은봉',\n",
       " '하이케 팔러',\n",
       " '존 보글',\n",
       " '돈복남',\n",
       " '이소라',\n",
       " '이가람',\n",
       " '김도환',\n",
       " '박상현',\n",
       " '신정철',\n",
       " '정덕현',\n",
       " '김태현',\n",
       " '신승호',\n",
       " '리디아 브란코비치',\n",
       " '칼린디',\n",
       " '심영순',\n",
       " '다니엘 앨트먼',\n",
       " '잭파시(최경천)',\n",
       " '볼프 슈나이더',\n",
       " '양희은',\n",
       " '테일러 스위프트',\n",
       " '김영로',\n",
       " '전도현',\n",
       " '이슬비',\n",
       " '이승일',\n",
       " '최승필',\n",
       " '아다치 토카',\n",
       " '사토 도미오',\n",
       " '김미숙',\n",
       " 'DR하브루타교육연구회',\n",
       " '김기택',\n",
       " '루츠 폰 베르더. 바바라 슐테-슈타이니케',\n",
       " '조지프 르두',\n",
       " '라시드 할리디',\n",
       " '스테판 에셀',\n",
       " '정희원.전현우',\n",
       " '김성중.심정묘',\n",
       " '앤 루니',\n",
       " '청예',\n",
       " '정지용',\n",
       " '양승준',\n",
       " '박성덕',\n",
       " '아르민 호프만',\n",
       " '크리스토퍼 모스',\n",
       " '전중환',\n",
       " '찰스 부코스키',\n",
       " '미야츠키 모소코.카도마루 츠부라',\n",
       " '우지인.김성민',\n",
       " '416 세월호 참사 기록위원회 작가기록단',\n",
       " '태양의 후예 문전사.NEW',\n",
       " '요코다 하마오',\n",
       " '소피 슈임',\n",
       " '랜들 먼로',\n",
       " '장우진',\n",
       " '필립 맥마이클',\n",
       " '주장수',\n",
       " '권오민',\n",
       " '대니얼 예긴',\n",
       " '모리노 메구미',\n",
       " '모티머 J.애들러',\n",
       " '리상룽',\n",
       " '앙투안 볼로딘',\n",
       " '이홍렬',\n",
       " 'EBS 자본주의 제작팀',\n",
       " '조 사코',\n",
       " '서울대학교 기초교육원.안철수',\n",
       " '오스카 E. 페르난데스',\n",
       " '마중물',\n",
       " '조용학',\n",
       " '송준호',\n",
       " '이중복',\n",
       " 'KBS 명견만리 제작진',\n",
       " '매리언 울프',\n",
       " '페터 볼레벤',\n",
       " '테렌스 데 프레',\n",
       " '그레그 스타인메츠',\n",
       " '야마다 카네히토',\n",
       " '하츠하루',\n",
       " '정하연',\n",
       " '이용세',\n",
       " '아나신',\n",
       " '배혜림',\n",
       " '심채경',\n",
       " '하영애.우노 히토미',\n",
       " '마샤 리네한',\n",
       " '에구치 렌',\n",
       " '나봄',\n",
       " '김성원',\n",
       " '케네스 E. 베일리',\n",
       " '노마 히데키',\n",
       " '길다 윌리엄스',\n",
       " '이영도',\n",
       " '전혜린',\n",
       " '션 B. 캐럴',\n",
       " 'E. 풀러 토리',\n",
       " '미카.유카',\n",
       " '마이클 굿윈',\n",
       " '강지나',\n",
       " '타케무라 요헤이',\n",
       " '로저 로젠블라트',\n",
       " '하비 다이아몬드',\n",
       " '닉 폴슨.제임스 스콧',\n",
       " '무레 요코',\n",
       " '정해연',\n",
       " '김중미.공지영.박완서',\n",
       " '알프레드 랜싱',\n",
       " '니콜라스 카',\n",
       " '김준영',\n",
       " '케빈 강.해나 변',\n",
       " '미셸 푸꼬',\n",
       " '김민아',\n",
       " '권영식',\n",
       " '루시 M. 몽고메리',\n",
       " '두산동아 편집부',\n",
       " '이고은',\n",
       " '이상훈',\n",
       " '리어 키스',\n",
       " '하태완',\n",
       " '다카시마 데쓰지',\n",
       " '루스 렌들',\n",
       " '씨씨코',\n",
       " '신동윤',\n",
       " '데이비드 앨런 시블리',\n",
       " '브래드 필론',\n",
       " '하인리히 야콥',\n",
       " '정비석',\n",
       " '그랜트 카돈',\n",
       " '캐서린 피트먼.엘리자베스 칼',\n",
       " '청울림',\n",
       " 'Charles C. Manz',\n",
       " 'J. 켄지 로페즈 알트',\n",
       " '레이먼드 챈들러',\n",
       " '후쿠츠지 도시키',\n",
       " '서장원',\n",
       " '라이언 맥긴리',\n",
       " '전경린',\n",
       " '민음사 편집부',\n",
       " '김동호',\n",
       " '천쓰홍',\n",
       " '김태광',\n",
       " '조성근',\n",
       " '웨인 W. 다이어',\n",
       " '이자람',\n",
       " '최갑수',\n",
       " '폴 아자르',\n",
       " '앤디 그리피스',\n",
       " '한정원',\n",
       " '정희모.이재성',\n",
       " '양운덕',\n",
       " '에픽테토스',\n",
       " '단요',\n",
       " '홀썸모먼트',\n",
       " '서머셋 몸',\n",
       " '문호진.단요',\n",
       " '김멜라',\n",
       " 'Longman 편집부',\n",
       " '백유연',\n",
       " '팀 켈러.캐시 켈러',\n",
       " '김상복',\n",
       " '아글라야 페터라니',\n",
       " '김정기',\n",
       " '손현주',\n",
       " '이서원',\n",
       " '안정효',\n",
       " '홍신자',\n",
       " '버트 도드슨',\n",
       " '김지용',\n",
       " '대니얼 조슈아 루빈',\n",
       " '팀 하포드',\n",
       " '윤태규',\n",
       " '윤가은',\n",
       " '황경택',\n",
       " '버지니아 사티어',\n",
       " '이정동',\n",
       " '서덕준',\n",
       " '라파엘 로젠',\n",
       " '달곰미디어 기획팀',\n",
       " '썬킴',\n",
       " '박희란',\n",
       " '이다',\n",
       " '김지안',\n",
       " '제이포럼',\n",
       " '박혜선',\n",
       " '토머스 화이트맨',\n",
       " '스티마',\n",
       " '신우섭',\n",
       " '박경진',\n",
       " '조한경',\n",
       " '명업식',\n",
       " '웨인 그루뎀',\n",
       " '박현미',\n",
       " '이수명',\n",
       " '와타나베 아야',\n",
       " 'NHK 스페셜 제작팀',\n",
       " '장혜령',\n",
       " '신성종',\n",
       " '선대인경제연구소',\n",
       " '제리 위코프',\n",
       " '존 라이언.앨런 테인 더닝',\n",
       " '카타리나 잉겔만 순드베리',\n",
       " '김창옥',\n",
       " '공윤조',\n",
       " '김해인',\n",
       " '허먼 멜빌',\n",
       " '서재걸',\n",
       " '엘리자베스 길버트',\n",
       " '폴 칼라니티',\n",
       " '김혜자',\n",
       " '벤저민 카터 헷',\n",
       " '미국추리작가협회',\n",
       " '탤렌 마이데너',\n",
       " '유선경',\n",
       " '차드 멩 탄',\n",
       " '반건호',\n",
       " '전용문.박현민',\n",
       " '마이크 브라운',\n",
       " '슬구',\n",
       " '하말넘많',\n",
       " '존 M. 렉터',\n",
       " '헨리 데이빗 소로',\n",
       " '시기즈문트 크르지자놉스키',\n",
       " '이혜원',\n",
       " '은현장',\n",
       " '김리리',\n",
       " '제인 넬슨',\n",
       " '김양순',\n",
       " '메그',\n",
       " '채상욱',\n",
       " '정혜용',\n",
       " '줄리언 반스',\n",
       " '홍현주.고은영',\n",
       " '랍비 조너선 색스',\n",
       " 'EBS 아이의 사생활 제작팀',\n",
       " '에릭 시노웨이 & 메릴 미도우',\n",
       " '정약용.박지원.강희맹',\n",
       " '마루야마 마사오+가토 슈이치',\n",
       " '리처드 J. 라이더 & 데이비드 A. 샤피로',\n",
       " '텐게시로',\n",
       " '김영갑',\n",
       " '이승원',\n",
       " 'KBS <생로병사의 비밀> 제작팀',\n",
       " '요시다 타로',\n",
       " '조지 S. 클래이슨',\n",
       " '김지훈',\n",
       " '토머스 펠프스',\n",
       " '토스',\n",
       " '사토 야마토',\n",
       " '오노레 드 발자크',\n",
       " '홍춘욱',\n",
       " '이은미',\n",
       " '기 드 모파상',\n",
       " '김학인',\n",
       " '신윤정',\n",
       " '정치근',\n",
       " '신현강(부룡)',\n",
       " '원정연(만쥬)',\n",
       " '김내훈',\n",
       " '정보라',\n",
       " '이중환',\n",
       " '존 브래드쇼',\n",
       " '이규천',\n",
       " '데이비드 핸드',\n",
       " '미치코 가쿠타니',\n",
       " '록산 게이',\n",
       " '최문희',\n",
       " '박혜란',\n",
       " '정회도',\n",
       " '가스파드',\n",
       " '조 슈워츠',\n",
       " '팀 버튼',\n",
       " '홍진걸',\n",
       " '김누리',\n",
       " '시미즈 켄지',\n",
       " '김유라',\n",
       " '피터 나바로',\n",
       " '츠지무라 미즈키',\n",
       " '카를로 젠',\n",
       " '장문정',\n",
       " '장콩',\n",
       " '이태석',\n",
       " '조상훈',\n",
       " '제프 콕스·하워드 스티븐스',\n",
       " '팀 교양만두',\n",
       " '김훈기',\n",
       " '신혜우',\n",
       " '정운',\n",
       " '사샤 아랑고',\n",
       " '임유정',\n",
       " '마이클 셸런버거',\n",
       " '우에니시 아키라',\n",
       " '이영애',\n",
       " '로버트 루이스 스티븐슨',\n",
       " '앨리스허(허미숙)',\n",
       " '김경미',\n",
       " '페터 비에리',\n",
       " '김범석',\n",
       " '곽재구',\n",
       " '존 비비어',\n",
       " '한정림',\n",
       " '조성경',\n",
       " '자카리 쇼어',\n",
       " '베르너 하이젠베르크',\n",
       " '정은궐',\n",
       " '이우일 글',\n",
       " '마지 슈조',\n",
       " '박성규',\n",
       " '넬레 노이하우스',\n",
       " '도정일.박원순',\n",
       " '조한별',\n",
       " '황지원',\n",
       " '텐진 갸초(달라이 라마).빅터 챈',\n",
       " '앨런 피즈',\n",
       " '이진민',\n",
       " '조규미',\n",
       " '이서영.강승임',\n",
       " '고윤(페이서스 코리아)',\n",
       " '우공이산외국어연구소',\n",
       " '조안 마그레타',\n",
       " '스탠리 하우워어스',\n",
       " '톰 헤드',\n",
       " '안그람',\n",
       " '레오나르드 믈로디노프',\n",
       " '제이컵 솔',\n",
       " '이근후',\n",
       " '로버트 O. 팩스턴',\n",
       " '김종희',\n",
       " '메리 올리버',\n",
       " '파멜라 드러커맨',\n",
       " '안전길실',\n",
       " '로드니 스타크',\n",
       " '오히라 노부타카',\n",
       " '트레이시 슈발리에',\n",
       " '삼호ETM 콘텐츠기획개발부.조희순',\n",
       " '노승대',\n",
       " '박지성',\n",
       " '칼릴 지브란',\n",
       " '최시한',\n",
       " '노무라 유스케',\n",
       " '토머스 프리드만',\n",
       " 'Mike Hwang',\n",
       " '김언정',\n",
       " '조신영',\n",
       " '에버하르트 뫼비우스',\n",
       " '벤키 라마크리슈난',\n",
       " '미도리 후우',\n",
       " '이찬승',\n",
       " '웬디 베케트',\n",
       " '이시원.시원스컨텐츠 연구소',\n",
       " '강승임',\n",
       " '김수원',\n",
       " '도미니크 로로',\n",
       " '박부성',\n",
       " '줄리아 로스먼',\n",
       " '김정환',\n",
       " '노애리',\n",
       " '포스코인재창조원',\n",
       " '릭 워렌',\n",
       " '피터 워더스',\n",
       " '보후밀 흐라발',\n",
       " '장문철',\n",
       " '이은선',\n",
       " '존 릴런드',\n",
       " '사이토 미에코',\n",
       " '홍현주',\n",
       " '모리시타 노리코',\n",
       " '후지모토 사키코',\n",
       " '돌만',\n",
       " '메이브 빈치',\n",
       " '마이클 콜린스',\n",
       " '이영문',\n",
       " '루이스 캐롤',\n",
       " 'G-TELP KOREA',\n",
       " '마이클 J. 모부신',\n",
       " '국세청',\n",
       " '역시연구회',\n",
       " '스콧 니어링',\n",
       " '박시하',\n",
       " '홍정선.강계숙',\n",
       " '함유근.채승병',\n",
       " '무라카미 리코',\n",
       " '무코야마 다카히코',\n",
       " '오바라 가즈히로',\n",
       " '아고타 크리스토프',\n",
       " '전광.서정희.한건수',\n",
       " '짐 알칼릴리',\n",
       " '찰스 해낼',\n",
       " '이경덕',\n",
       " '성하성',\n",
       " '랜디 포시.제프리 재슬로',\n",
       " '테루야 하나코 & 오카다 게이코',\n",
       " '김진형.이현주',\n",
       " '권정태',\n",
       " '이원율',\n",
       " '김보은',\n",
       " '한국고용정보원',\n",
       " '이세정',\n",
       " '서후',\n",
       " '마크 웨이스블러스',\n",
       " '헨리에트 앤 클라우저',\n",
       " '고은',\n",
       " '정무늬',\n",
       " '신수정',\n",
       " '베터홈 협회',\n",
       " '석정현',\n",
       " '이우신',\n",
       " '김설',\n",
       " '시어도어 그레이',\n",
       " '워리 라인스',\n",
       " '릭킴',\n",
       " '김동문',\n",
       " '노아 케이건',\n",
       " '필립 델브스 브러턴',\n",
       " '위베르 졸리.캐롤라인 램버트',\n",
       " 'C. S. 루이스',\n",
       " '민석홍',\n",
       " '송형석',\n",
       " '이근철',\n",
       " '우노 츠네히로',\n",
       " '스티븐 존슨',\n",
       " '네코쿠라게',\n",
       " '다니엘 골먼.리처드 J. 데이비드슨',\n",
       " '조단 E. 로젠펠드',\n",
       " '류태영',\n",
       " '김사라',\n",
       " '곽순애',\n",
       " '브라이언 W. 커니핸',\n",
       " '대니얼 코일',\n",
       " '손호성',\n",
       " '요쉬카 피셔',\n",
       " '강원국',\n",
       " '안톤 파블로비치 체홉',\n",
       " '조이도우슨',\n",
       " '최성애.조벽.존 가트맨',\n",
       " '히라이 다카시',\n",
       " '정민우.유근용',\n",
       " '칼 필레머',\n",
       " '이명옥',\n",
       " '장영지',\n",
       " '마이클 브린',\n",
       " '플라톤',\n",
       " '후지마루',\n",
       " 'Suzanne Francis',\n",
       " '윌리엄 세익스피어',\n",
       " '이소호',\n",
       " '앨릭스 코브',\n",
       " '임지현.권혁범',\n",
       " '장은교',\n",
       " '유진 오닐',\n",
       " '클라이브 스테이플즈 루이스',\n",
       " '샤론 강',\n",
       " '이영직',\n",
       " '남인숙',\n",
       " '하정우',\n",
       " '안드레아스 빙켈만',\n",
       " '김희종',\n",
       " '서정태',\n",
       " '스베틀라나 알렉시예비치',\n",
       " '다큐멘터리 〈Noble Asks〉 제작팀',\n",
       " '리처드 쇼튼',\n",
       " '고코로야 진노스케',\n",
       " '제이슨 펑.지미 무어',\n",
       " '최민준',\n",
       " '와조스키',\n",
       " '매트 리들리',\n",
       " '조영준',\n",
       " '0. 헨리',\n",
       " '이시형',\n",
       " '장일호',\n",
       " '리스 하스아우트',\n",
       " '지이.태복',\n",
       " '최경희',\n",
       " '찰스 브라메스코',\n",
       " '이현진',\n",
       " '타일러 라쉬',\n",
       " '리처드 H. 탈러 & 카스 R. 선스타인',\n",
       " '해커스 JLPT연구소',\n",
       " '한국사능력검정시험연구회',\n",
       " '하임 기너트',\n",
       " '프랜시스 앰블러',\n",
       " '최원형',\n",
       " '이정윤',\n",
       " '홍민영',\n",
       " '김웅현',\n",
       " '곽수인',\n",
       " '야마구치 슈',\n",
       " '편은지 PD',\n",
       " '에릭 노먼드',\n",
       " '사이니야',\n",
       " '곤도 노리코',\n",
       " '조너선 스타인하트',\n",
       " '진민규.해커스 무역시험연구소',\n",
       " '윤광준',\n",
       " '테레사 라살라.조디 맥비티.수잔 스미사',\n",
       " '김우현',\n",
       " '박홍기',\n",
       " '유수연',\n",
       " '김봉렬',\n",
       " '황인영',\n",
       " '주성철',\n",
       " '피터 헤더.존 래플리',\n",
       " '하마구치 다카노리',\n",
       " '제리 와이코프',\n",
       " '김종훈',\n",
       " '일연',\n",
       " '이디스 워턴',\n",
       " '존 드레인',\n",
       " 'B.K.S.아헹가',\n",
       " '김기현',\n",
       " '김한민',\n",
       " '헥터 맥도널드',\n",
       " 'EBS(한국교육방송공사) 편집부',\n",
       " '에크낫 이스워런',\n",
       " '니시무라 아키라',\n",
       " '현각스님',\n",
       " '케이 우라나',\n",
       " '카토 코토노',\n",
       " '손영옥',\n",
       " '김상균.신병호',\n",
       " '흔글',\n",
       " '서덕희.최은영',\n",
       " '제시 싱어',\n",
       " '로렌스 R. 스펜서',\n",
       " 'W. 휴 미실다인',\n",
       " '유성호',\n",
       " '마크 미오도닉',\n",
       " '김민정',\n",
       " '사노 나미',\n",
       " '이마무라 마사히로',\n",
       " '원성 스님',\n",
       " '알렉산더 로이드.벤 존슨',\n",
       " '김인환',\n",
       " '이윤미',\n",
       " '바바라 런던',\n",
       " '송희창',\n",
       " '로버트 마우어',\n",
       " '박경수.장경애',\n",
       " '크리스티안 A.슈바르츠',\n",
       " '다소마미',\n",
       " '이븐 알렉산더.프톨레미 톰킨스',\n",
       " '제임스 보웬',\n",
       " '윤희숙',\n",
       " '권오경',\n",
       " '이광형',\n",
       " '배상복',\n",
       " '다음을 지키는 엄마모임',\n",
       " '피터 스완슨',\n",
       " '데이비드 니븐',\n",
       " '벤 호건.허버트 워런 윈드',\n",
       " '곽윤정',\n",
       " '조성희',\n",
       " '헬렌 니어링.스코트 니어링',\n",
       " '사사키 다케시',\n",
       " '바버라 에런라이크',\n",
       " '션 애덤스',\n",
       " 'A. J. 프로스트 & 로버트 R. 프렉터 주니어',\n",
       " '뿡빵뀨',\n",
       " '세스 스티븐스 다비도위츠',\n",
       " '남기심.고영근',\n",
       " '강석진',\n",
       " '에티엔 발리바르.이매뉴얼 월러스틴',\n",
       " '김진중',\n",
       " '콜린 브라이어.빌 카',\n",
       " '매튜 룬',\n",
       " '이창현',\n",
       " '게오르크 W.F. 헤겔',\n",
       " '토마스 주 남',\n",
       " '민주사회를 위한 변호사모임',\n",
       " '김홍희',\n",
       " '와카야마 요코',\n",
       " '모종린',\n",
       " '세쿼이아 나가마쓰',\n",
       " '유득공',\n",
       " 'YBM시사닷컴 MOS 교재개발팀',\n",
       " '조엘 슈월츠버그',\n",
       " '나민애',\n",
       " '마커스 버킹엄 & 도널드 클리프턴',\n",
       " '김진',\n",
       " '김빵',\n",
       " '모드 쥘리앵',\n",
       " '문단열.펀글리시',\n",
       " '이상철',\n",
       " '박원순',\n",
       " '이준석',\n",
       " '이원복 글',\n",
       " '장 자크 상뻬',\n",
       " '우다영',\n",
       " '황광우',\n",
       " '아리카와 마유미',\n",
       " '에이드리언 코난 도일.존 딕슨 카',\n",
       " '조형진',\n",
       " '마크 롤랜즈',\n",
       " '이와시타 케이코',\n",
       " '수전 케인',\n",
       " '이동귀.손하림.김서영',\n",
       " '오오타 야스스케',\n",
       " '홍평표 외 청소년상담사 수험연구소',\n",
       " '박성규.오승호',\n",
       " '강아',\n",
       " '리사 제노바',\n",
       " '폴린 브라운',\n",
       " '송연석',\n",
       " '하야시다 규',\n",
       " '마키타 젠지',\n",
       " '태지원',\n",
       " '한젬마',\n",
       " '서현',\n",
       " '정영욱',\n",
       " '윤영무',\n",
       " '하워드 막스',\n",
       " '에마뉘엘 토드',\n",
       " '강석기.박동균',\n",
       " '카와카미 유키',\n",
       " '다나카 료조',\n",
       " '박성윤.박철종',\n",
       " '벤 호로위츠',\n",
       " '이대표',\n",
       " '차홍',\n",
       " '안경훈',\n",
       " '캐서린 스토켓',\n",
       " '신정근',\n",
       " '키류 미사오',\n",
       " '김두엽',\n",
       " '라우라 에스키벨',\n",
       " '황현필',\n",
       " '캐런 킹스턴',\n",
       " '타이터스 윈터스.톰 맨쉬렉.하이럼 라이트',\n",
       " 'IBLP',\n",
       " '할 엘로드',\n",
       " '시가 아키라',\n",
       " '아오키 가즈오',\n",
       " '김길',\n",
       " '에카르트 폰 히르슈하우젠',\n",
       " '박현도',\n",
       " '호톤도신데이루',\n",
       " '폴 라파르그',\n",
       " '최백규',\n",
       " '황미진',\n",
       " '전선영.임종수',\n",
       " '게리 폴슨',\n",
       " '크리스티앙 보뱅',\n",
       " '더 케어 컬렉티브',\n",
       " '양승아',\n",
       " '야마다 가요코',\n",
       " '사이토 가쓰히로',\n",
       " '돈 리처드 리소',\n",
       " '강수진',\n",
       " '최규석',\n",
       " '박민수(샌드 타이거 샤크)',\n",
       " '조제프 앙투안 투생 디누아르',\n",
       " '김학민',\n",
       " '전병욱',\n",
       " '박주용',\n",
       " '신철희',\n",
       " '오자키 이라',\n",
       " '소윤',\n",
       " '칼 벅스트롬.제빈 웨스트',\n",
       " '최재훈',\n",
       " '심승현',\n",
       " '프리츠 오르트만',\n",
       " '권수영',\n",
       " '알퐁스 도데',\n",
       " '기형도',\n",
       " '정현미 (모모맘)',\n",
       " '다테나오토',\n",
       " '얀 마텔',\n",
       " '박미나(미나뜨)',\n",
       " '마이클 매클리어',\n",
       " '니나 프루덴버거',\n",
       " '김종옥',\n",
       " '권혁도',\n",
       " '김덕진.서승완',\n",
       " '에드워드 윌슨',\n",
       " '다비드 르 브르통',\n",
       " '알리스터 맥그래스',\n",
       " '도널드 클리프턴.톰 래스',\n",
       " '김수정',\n",
       " '이원영',\n",
       " '허균',\n",
       " '전은규',\n",
       " '장 프랑수아 마르미옹',\n",
       " '현태준. 이우일',\n",
       " '파비앙 드구레',\n",
       " '요네다 코우',\n",
       " '루이스 다트넬',\n",
       " '토머스 게이건',\n",
       " '프리모 레비',\n",
       " '김연아',\n",
       " '조윤경',\n",
       " '김보경',\n",
       " '유랑',\n",
       " '세상에 하나뿐인 우리엄마',\n",
       " '피터 자이한',\n",
       " '한자능력검정시험연구회',\n",
       " '마이클렌 다우클레프',\n",
       " '마리 수아레즈',\n",
       " '윤경희',\n",
       " '장수경',\n",
       " '구해근',\n",
       " '정종철',\n",
       " '문재인 대통령 비서실',\n",
       " '문보영',\n",
       " '안인희',\n",
       " '김나영',\n",
       " '배예람',\n",
       " '김학렬',\n",
       " '헤일 도스킨',\n",
       " '셸리 케이건',\n",
       " '이건.최준철.홍영표',\n",
       " '데이비드 보르게닉트.조슈아 피븐',\n",
       " '시니맘(박지혜)',\n",
       " '김준한',\n",
       " '라라 E. 필딩',\n",
       " '데번 프라이스',\n",
       " '정재찬',\n",
       " '최영원',\n",
       " '김금선',\n",
       " '마틴 포드',\n",
       " '장선용',\n",
       " '이재철',\n",
       " '김현구',\n",
       " '스테판 M. 폴란',\n",
       " '한화택',\n",
       " '하루소노 쇼',\n",
       " '스즈마루 민타',\n",
       " '이서희',\n",
       " '윤성우',\n",
       " '트레이시 커크로',\n",
       " '강기태',\n",
       " '프랜시스 M.코스그로브',\n",
       " '엘리자베스 짐머만',\n",
       " '박용범',\n",
       " '니토리 고이치',\n",
       " '미하엘 엔데',\n",
       " '유태우',\n",
       " '김영익',\n",
       " '커트 베이커',\n",
       " '김종율(옥탑방보보스)',\n",
       " '박종홍',\n",
       " '유은실',\n",
       " '정재훈',\n",
       " '남충희',\n",
       " '에바 졸러',\n",
       " '이사야 벌린',\n",
       " '제임스 팁트리 주니어',\n",
       " '오세라비',\n",
       " '디즈니 프린세스',\n",
       " '서보건',\n",
       " '나카무로 마키코',\n",
       " '토마스 패빗',\n",
       " '이송희일',\n",
       " '방현희',\n",
       " '권남희',\n",
       " '고선경',\n",
       " '로버트 H. 프랭크',\n",
       " '스탠리 하우어워스',\n",
       " '야마구치 요헤이',\n",
       " '마우로 기옌',\n",
       " '김민서',\n",
       " '제임스 M. 배너 주니어.해럴드 C. 캐넌',\n",
       " '키마타 료',\n",
       " '존 케닉',\n",
       " '노구치 사토시',\n",
       " '로버트 제임스 월러',\n",
       " '김건호',\n",
       " '노진.이민규',\n",
       " '소라',\n",
       " '오정우.넥서스텝스연구팀',\n",
       " '데이비드 차',\n",
       " '스튜어트 쉥커',\n",
       " '귀스타브 르 봉',\n",
       " '장용민',\n",
       " '혼다 켄',\n",
       " '양민정',\n",
       " '야마구찌 마유',\n",
       " '조승리',\n",
       " '이영숙',\n",
       " '자크 랑시에르',\n",
       " '최민영',\n",
       " '데이비드 J. 리버만',\n",
       " '룰루 밀러',\n",
       " '노지은.이현정',\n",
       " '테드 알렌',\n",
       " '사사키 다이스케',\n",
       " '이문필',\n",
       " '네온비 글',\n",
       " '김웅',\n",
       " '박연준.장석주',\n",
       " '딘 셔만',\n",
       " '히다카 쇼코',\n",
       " 'Collins 사전 편집부',\n",
       " '마쓰이에 마사시',\n",
       " '장 지오노',\n",
       " '무라세 다케시',\n",
       " '쓰지 히토나리',\n",
       " '가마타 미노루',\n",
       " '후미노 유키',\n",
       " '석가모니',\n",
       " '마츠모토 타이요',\n",
       " '성필규',\n",
       " '주현성',\n",
       " '정시우',\n",
       " '테라니시 에리코',\n",
       " '구작가',\n",
       " '머레이 브룸버그.멜빈 고든',\n",
       " '카타야마 쿄이치',\n",
       " '미도리카와 유키',\n",
       " '아이뉴턴 편집부',\n",
       " '브래드퍼드 들롱',\n",
       " '제이슨 르쿨락',\n",
       " '나토리 호겐',\n",
       " '무라야마 사키',\n",
       " '올리비에 시보니',\n",
       " '데이브 아스프리',\n",
       " '시와스 유키',\n",
       " '대프니 듀 모리에',\n",
       " '폴커 키츠.마누엘 투쉬',\n",
       " '강신주.지승호',\n",
       " '탄줘잉',\n",
       " '사카토 켄지',\n",
       " '닐 도날드 월쉬',\n",
       " '안선이',\n",
       " '울리히 벡.벡-게른스하임',\n",
       " '나카바 스즈키',\n",
       " '김완',\n",
       " '백희나',\n",
       " '에이미 샤',\n",
       " '존 R. 스토트',\n",
       " '김병만',\n",
       " '윤정은',\n",
       " '신동흔',\n",
       " '김이환',\n",
       " '해리 G. 프랭크퍼트',\n",
       " '강보원',\n",
       " '에리히 프롬 지음. 방곤',\n",
       " '이동형',\n",
       " '초(정솔)',\n",
       " '매기 넬슨',\n",
       " '명진 스님',\n",
       " '델리아 오언스',\n",
       " '정윤선(윤선디자인)',\n",
       " '리사 펠드먼 배럿',\n",
       " '마고사키 우케루',\n",
       " '앨릭스 E. 해로우',\n",
       " '로리 넬슨 스필먼',\n",
       " '캐롤라인 알렉산더',\n",
       " '앤디 앤드루스',\n",
       " '서울대학교 TEPS 관리위원회 문제',\n",
       " '리처드 H. 스미스',\n",
       " '변진경',\n",
       " '사만다 헤이즈',\n",
       " '가와카미 데쓰야',\n",
       " '라이더 캐롤',\n",
       " '김신회',\n",
       " '앤터니 스토',\n",
       " '권석만',\n",
       " '강기진',\n",
       " '바바라 G. 워커',\n",
       " '박명희',\n",
       " '공여사들',\n",
       " '퍼포먼스 코치 리아',\n",
       " '조성준',\n",
       " '엘렌 식수',\n",
       " '미우치 스즈에',\n",
       " '아리엘 슈워츠',\n",
       " '정미경.오두환',\n",
       " '갈로아',\n",
       " '해커스 일본어연구소',\n",
       " '신붕섭',\n",
       " '사이토 고키',\n",
       " '이수연',\n",
       " '장미가족의 태그교실',\n",
       " '박희철',\n",
       " '테리 번햄',\n",
       " '앨런 피즈.바바라 피즈',\n",
       " '박태하',\n",
       " '조성익',\n",
       " '월터 아이작슨',\n",
       " '신민숙.해커스 공무원시험연구소',\n",
       " '샹탈 아케르만',\n",
       " '사전연구회',\n",
       " '윌 곰퍼츠',\n",
       " '마가렛 애트우드',\n",
       " '김이재',\n",
       " '창원특수강 6시그마연구회',\n",
       " '조르주 디디 위베르만',\n",
       " '팸 존슨 베넷',\n",
       " '제인 넬슨.셰릴 어윈.로즐린 앤 더피',\n",
       " '박창모',\n",
       " '편해문',\n",
       " '이재석',\n",
       " '정서경',\n",
       " '호보닛칸이토이신문',\n",
       " '그렉 브레이든',\n",
       " '김수미',\n",
       " 'NHK출판사 편집부',\n",
       " '에드 콘웨이',\n",
       " '이상욱',\n",
       " '오찬호',\n",
       " '가브리엘 루아',\n",
       " '사이쇼 히로시',\n",
       " '권헌익.정병호',\n",
       " '한수희',\n",
       " '제레미 모로',\n",
       " 'A.G 로엠메르스',\n",
       " '앨리스 제임스',\n",
       " '에드워드 버네이스',\n",
       " '서준호',\n",
       " '키 영어학습방법연구소',\n",
       " '더 라이트 편집부',\n",
       " '미쉘',\n",
       " '김보영',\n",
       " '정선균',\n",
       " ...}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = (set(author_top_slspnt).difference(set(author_top_cnt)))\n",
    "display(len(temp))\n",
    "display(temp)\n",
    "display('김대중' in temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display('김대중' in author_top_slspnt)\n",
    "display('기타 저자' in author_top_slspnt)"
   ]
  }
 ],
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