5.1 업종 테마주 수집-output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s1KBVebhpSVa"
   },
   "source": [
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/corazzon/finance-data-analysis/blob/main/5.1%20%EC%97%85%EC%A2%85%20%ED%85%8C%EB%A7%88%EC%A3%BC%20%EC%88%98%EC%A7%91-output.ipynb)\n",
    "\n",
    "\n",
    "## 업종/테마 미션\n",
    "\n",
    "* 팀장님이 우리 회사와 유사한 업종과 테마로 사업을 하고 있는 종목을 수집해서 분석해 달라는 미션을 주셨습니다.\n",
    "* 코스피에서 해당 업종 혹은 테마의 시가총액이 가장 많은 종목을 찾아주세요.\n",
    "* 해당 업종/테마의 거래량이 가장 많은 종목을 찾아주세요.\n",
    "* 최근 52주 최고가에서 현재가 기준으로 가장 손실이 크게 난 종목은 어떤 종목일까요?\n",
    "* 거래대금이 20위 안에 있는 코스피 종목 중에 \"PER\"가 가장 낮은 종목은 무엇일까요?\n",
    "\n",
    "\n",
    "## 업종/테마주 수집\n",
    "\n",
    "전체 실습을 하고 아래의 URL로 URL만 변경하게 되면 원하는 업종과 테마를 수집할 수 있습니다.\n",
    "\n",
    "* 2차 전지 : https://finance.naver.com/sise/sise_group_detail.nhn?type=theme&no=64\n",
    "* 제약 업종 : https://finance.naver.com/sise/sise_group_detail.nhn?type=upjong&no=261\n",
    "* 코로나19(덱사메타손) : https://finance.naver.com/sise/sise_group_detail.nhn?type=theme&no=442\n",
    "* 제약업체 : https://finance.naver.com/sise/sise_group_detail.nhn?type=theme&no=172\n",
    "* 바이오시밀러(복제 바이오의약품) : https://finance.naver.com/sise/sise_group_detail.nhn?type=theme&no=241"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Y9SHu_YepSVb"
   },
   "outputs": [],
   "source": [
    "# 라이브러리 로드 : pandas, matplotlib.pyplot\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_font_family():\n",
    "    \"\"\"\n",
    "    시스템 환경에 따른 기본 폰트명을 반환하는 함수\n",
    "    \"\"\"\n",
    "    import platform\n",
    "    system_name = platform.system()\n",
    "    # colab 사용자는 system_name이 'Linux'로 확인\n",
    "\n",
    "    if system_name == \"Darwin\" :\n",
    "        font_family = \"AppleGothic\"\n",
    "    elif system_name == \"Windows\":\n",
    "        font_family = \"Malgun Gothic\"\n",
    "    else:\n",
    "        # Linux\n",
    "        # colab에서는 runtime을 <꼭> 재시작 해야합니다.\n",
    "        # 런타임을 재시작 하지 않고 폰트 설치를 하면 기본 설정 폰트가 로드되어 한글이 깨집니다.\n",
    "        !apt-get update -qq\n",
    "        !apt-get install fonts-nanum -qq  > /dev/null\n",
    "\n",
    "        import matplotlib.font_manager as fm\n",
    "\n",
    "        fontpath = '/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf'\n",
    "        font = fm.FontProperties(fname=fontpath, size=9)\n",
    "        fm._rebuild()\n",
    "        font_family = \"NanumBarunGothic\"\n",
    "    return font_family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "font_family = get_font_family()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n"
     ]
    }
   ],
   "source": [
    "# 사용가능한 폰트 스타일\n",
    "print(plt.style.available)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "OenYgaAjpSVb"
   },
   "outputs": [],
   "source": [
    "# 그래프 스타일 설정\n",
    "plt.style.use(\"seaborn\")\n",
    "\n",
    "# 폰트설정\n",
    "plt.rc(\"font\", family=font_family)\n",
    "\n",
    "# 마이너스폰트 설정\n",
    "plt.rc(\"axes\", unicode_minus=False)\n",
    "\n",
    "# 그래프에 retina display 적용\n",
    "from IPython.display import set_matplotlib_formats\n",
    "set_matplotlib_formats(\"retina\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "67BK4ffjpSVb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6a1aa5650>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 345,
       "width": 479
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 한글폰트가 제대로 동작하는지 확인하기\n",
    "pd.Series([1, 3, 5, -7, 9]).plot(title=\"한글\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 제약업종 수집하기\n",
    "* 제약업종 주소가 변경되었습니다. https://finance.naver.com/sise/sise_group_detail.nhn?type=upjong&no=261"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "6aiUnujopSVb"
   },
   "outputs": [],
   "source": [
    "# 수집할 업종/테마주소\n",
    "# url = \"https://finance.naver.com/sise/sise_group_detail.nhn?type=upjong&no=35\"\n",
    "# 2차전지\n",
    "url = \"https://finance.naver.com/sise/sise_group_detail.nhn?type=theme&no=64\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "HsmAvWbXpSVb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read_html을 통해 해당 업종/테마를 수집합니다.\n",
    "table = pd.read_html(url, encoding=\"cp949\")\n",
    "len(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "5zTFxMaOpSVb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(82, 12)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(79, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터들은 table[2]에 전부 있고\n",
    "# dropna를 통해 결측치를 제거합니다.\n",
    "# how='all' 이면 전부 결측치일시 row나 column을 제거합니다.\n",
    "raw = table[2]\n",
    "print(raw.shape)\n",
    "raw = raw.dropna(how=\"all\").dropna(axis=1, how=\"all\")\n",
    "raw.shape\n",
    "# 3개의 row, 3개의 column이 제거되었습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>종목명</th>\n",
       "      <th>종목명.1</th>\n",
       "      <th>현재가</th>\n",
       "      <th>전일비</th>\n",
       "      <th>등락률</th>\n",
       "      <th>매수호가</th>\n",
       "      <th>매도호가</th>\n",
       "      <th>거래량</th>\n",
       "      <th>거래대금</th>\n",
       "      <th>전일거래량</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>웰크론한텍 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>+20.79%</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>4770.0</td>\n",
       "      <td>32171066.0</td>\n",
       "      <td>146663.0</td>\n",
       "      <td>6941573.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>이엔드디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>+9.02%</td>\n",
       "      <td>43450.0</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>999308.0</td>\n",
       "      <td>41912.0</td>\n",
       "      <td>475248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>삼진엘앤디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>+8.51%</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>4470.0</td>\n",
       "      <td>65461738.0</td>\n",
       "      <td>310756.0</td>\n",
       "      <td>45874433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SK이노베이션</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>283000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>+7.60%</td>\n",
       "      <td>283000.0</td>\n",
       "      <td>283500.0</td>\n",
       "      <td>3363732.0</td>\n",
       "      <td>950235.0</td>\n",
       "      <td>2014564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>일진머티리얼즈</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>56400.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>+7.22%</td>\n",
       "      <td>56300.0</td>\n",
       "      <td>56400.0</td>\n",
       "      <td>1661973.0</td>\n",
       "      <td>91832.0</td>\n",
       "      <td>399206.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>하나기술 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>143400.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>-3.69%</td>\n",
       "      <td>143400.0</td>\n",
       "      <td>143500.0</td>\n",
       "      <td>331289.0</td>\n",
       "      <td>48566.0</td>\n",
       "      <td>380067.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>씨아이에스 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>-3.70%</td>\n",
       "      <td>10350.0</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>10248736.0</td>\n",
       "      <td>109844.0</td>\n",
       "      <td>21750003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>이아이디</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>311.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-3.72%</td>\n",
       "      <td>311.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>32436621.0</td>\n",
       "      <td>10203.0</td>\n",
       "      <td>36861303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>나라엠앤디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>13150.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>-4.71%</td>\n",
       "      <td>13150.0</td>\n",
       "      <td>13200.0</td>\n",
       "      <td>1655695.0</td>\n",
       "      <td>22094.0</td>\n",
       "      <td>3432023.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>신성델타테크 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>10950.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>-6.41%</td>\n",
       "      <td>10950.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>3368126.0</td>\n",
       "      <td>36828.0</td>\n",
       "      <td>5580965.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         종목명     종목명.1       현재가      전일비      등락률      매수호가      매도호가  \\\n",
       "1    웰크론한텍 *  테마 편입 사유    4765.0    820.0  +20.79%    4765.0    4770.0   \n",
       "2     이엔드디 *  테마 편입 사유   43500.0   3600.0   +9.02%   43450.0   43500.0   \n",
       "3    삼진엘앤디 *  테마 편입 사유    4465.0    350.0   +8.51%    4465.0    4470.0   \n",
       "4    SK이노베이션  테마 편입 사유  283000.0  20000.0   +7.60%  283000.0  283500.0   \n",
       "5    일진머티리얼즈  테마 편입 사유   56400.0   3800.0   +7.22%   56300.0   56400.0   \n",
       "..       ...       ...       ...      ...      ...       ...       ...   \n",
       "75    하나기술 *  테마 편입 사유  143400.0   5500.0   -3.69%  143400.0  143500.0   \n",
       "76   씨아이에스 *  테마 편입 사유   10400.0    400.0   -3.70%   10350.0   10400.0   \n",
       "77      이아이디  테마 편입 사유     311.0     12.0   -3.72%     311.0     312.0   \n",
       "78   나라엠앤디 *  테마 편입 사유   13150.0    650.0   -4.71%   13150.0   13200.0   \n",
       "79  신성델타테크 *  테마 편입 사유   10950.0    750.0   -6.41%   10950.0   11000.0   \n",
       "\n",
       "           거래량      거래대금       전일거래량  \n",
       "1   32171066.0  146663.0   6941573.0  \n",
       "2     999308.0   41912.0    475248.0  \n",
       "3   65461738.0  310756.0  45874433.0  \n",
       "4    3363732.0  950235.0   2014564.0  \n",
       "5    1661973.0   91832.0    399206.0  \n",
       "..         ...       ...         ...  \n",
       "75    331289.0   48566.0    380067.0  \n",
       "76  10248736.0  109844.0  21750003.0  \n",
       "77  32436621.0   10203.0  36861303.0  \n",
       "78   1655695.0   22094.0   3432023.0  \n",
       "79   3368126.0   36828.0   5580965.0  \n",
       "\n",
       "[79 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 파생변수 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "29zzOiqKpSVb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2021-01-09'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datetime을 이용하여 조회한 날짜를 DataFrame에 기록합니다.\n",
    "import datetime\n",
    "today = datetime.datetime.today()\n",
    "today = today.strftime(\"%Y-%m-%d\")\n",
    "today"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "ZjCaJ25WpSVb"
   },
   "outputs": [],
   "source": [
    "# 일자별로 데이터가 다르니 조회일자를 추가해 줍니다.\n",
    "raw[\"조회일자\"] = today"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "yPLsXODEpSVc"
   },
   "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>종목명</th>\n",
       "      <th>종목명.1</th>\n",
       "      <th>현재가</th>\n",
       "      <th>전일비</th>\n",
       "      <th>등락률</th>\n",
       "      <th>매수호가</th>\n",
       "      <th>매도호가</th>\n",
       "      <th>거래량</th>\n",
       "      <th>거래대금</th>\n",
       "      <th>전일거래량</th>\n",
       "      <th>조회일자</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>웰크론한텍 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>+20.79%</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>4770.0</td>\n",
       "      <td>32171066.0</td>\n",
       "      <td>146663.0</td>\n",
       "      <td>6941573.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>이엔드디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>+9.02%</td>\n",
       "      <td>43450.0</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>999308.0</td>\n",
       "      <td>41912.0</td>\n",
       "      <td>475248.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>삼진엘앤디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>+8.51%</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>4470.0</td>\n",
       "      <td>65461738.0</td>\n",
       "      <td>310756.0</td>\n",
       "      <td>45874433.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SK이노베이션</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>283000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>+7.60%</td>\n",
       "      <td>283000.0</td>\n",
       "      <td>283500.0</td>\n",
       "      <td>3363732.0</td>\n",
       "      <td>950235.0</td>\n",
       "      <td>2014564.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>일진머티리얼즈</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>56400.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>+7.22%</td>\n",
       "      <td>56300.0</td>\n",
       "      <td>56400.0</td>\n",
       "      <td>1661973.0</td>\n",
       "      <td>91832.0</td>\n",
       "      <td>399206.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       종목명     종목명.1       현재가      전일비      등락률      매수호가      매도호가  \\\n",
       "1  웰크론한텍 *  테마 편입 사유    4765.0    820.0  +20.79%    4765.0    4770.0   \n",
       "2   이엔드디 *  테마 편입 사유   43500.0   3600.0   +9.02%   43450.0   43500.0   \n",
       "3  삼진엘앤디 *  테마 편입 사유    4465.0    350.0   +8.51%    4465.0    4470.0   \n",
       "4  SK이노베이션  테마 편입 사유  283000.0  20000.0   +7.60%  283000.0  283500.0   \n",
       "5  일진머티리얼즈  테마 편입 사유   56400.0   3800.0   +7.22%   56300.0   56400.0   \n",
       "\n",
       "          거래량      거래대금       전일거래량        조회일자  \n",
       "1  32171066.0  146663.0   6941573.0  2021-01-09  \n",
       "2    999308.0   41912.0    475248.0  2021-01-09  \n",
       "3  65461738.0  310756.0  45874433.0  2021-01-09  \n",
       "4   3363732.0  950235.0   2014564.0  2021-01-09  \n",
       "5   1661973.0   91832.0    399206.0  2021-01-09  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 조회일자가 추가되었는지 확인해 봅니다.\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 거래량과 거래대금 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "sGmjQKQrpSVc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6a2859210>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 342,
       "width": 570
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 종목명 대비 거래량을 bar chart로 그립니다.\n",
    "# set_index를 해주면 plot시에 y axis label로 그려집니다.\n",
    "# sort_values시의 기본값이 오름차순이기 때문에 \n",
    "# DataFrame의 하위 30개를 가져와야 거래량 상위 30개의 종목을 시각화 합니다.\n",
    "temp = raw[[\"종목명\", \"거래량\"]].sort_values(\"거래량\")\n",
    "temp.set_index(\"종목명\").tail(30).plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "BIG1HnR3pSVc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6a2d0d410>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 329,
       "width": 560
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 위와 동일한 방법으로 종목명 대비 거래대금의 bar chart를 시각화 합니다.\n",
    "temp = raw[[\"종목명\", \"거래대금\"]].set_index(\"종목명\")\n",
    "temp = temp.sort_values(\"거래대금\").tail(30)\n",
    "temp.plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NuTjcptepSVc"
   },
   "source": [
    "## 종목코드 병합\n",
    "* 종목명을 통해 미리 수집해 두었던 krx 전체 상장 종목 목록을 가져오거나 수집해서 데이터를 병합합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 기존 종목명에는 * 표시가 있기 때문에 * 문자를 제거해서 \"종목명_전처리\"라는 새로운 컬럼에 담아줍니다.\n",
    "raw[\"종목명_전처리\"] = raw[\"종목명\"].str.replace(\"*\", \"\")\n",
    "raw[\"종목명_전처리\"] = raw[\"종목명_전처리\"].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>종목명</th>\n",
       "      <th>종목명_전처리</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>웰크론한텍 *</td>\n",
       "      <td>웰크론한텍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>이엔드디 *</td>\n",
       "      <td>이엔드디</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>삼진엘앤디 *</td>\n",
       "      <td>삼진엘앤디</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SK이노베이션</td>\n",
       "      <td>SK이노베이션</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>일진머티리얼즈</td>\n",
       "      <td>일진머티리얼즈</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       종목명  종목명_전처리\n",
       "1  웰크론한텍 *    웰크론한텍\n",
       "2   이엔드디 *     이엔드디\n",
       "3  삼진엘앤디 *    삼진엘앤디\n",
       "4  SK이노베이션  SK이노베이션\n",
       "5  일진머티리얼즈  일진머티리얼즈"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# raw 변수에 담긴 데이터프레임을 확인합니다.\n",
    "raw[[\"종목명\", \"종목명_전처리\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Symbol</th>\n",
       "      <th>Market</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sector</th>\n",
       "      <th>Industry</th>\n",
       "      <th>ListingDate</th>\n",
       "      <th>SettleMonth</th>\n",
       "      <th>Representative</th>\n",
       "      <th>HomePage</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>060310</td>\n",
       "      <td>KOSDAQ</td>\n",
       "      <td>3S</td>\n",
       "      <td>특수 목적용 기계 제조업</td>\n",
       "      <td>반도체 웨이퍼 캐리어</td>\n",
       "      <td>2002-04-23</td>\n",
       "      <td>03월</td>\n",
       "      <td>박종익, 김세완 (각자 대표이사)</td>\n",
       "      <td>http://www.3sref.com</td>\n",
       "      <td>서울특별시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>095570</td>\n",
       "      <td>KOSPI</td>\n",
       "      <td>AJ네트웍스</td>\n",
       "      <td>산업용 기계 및 장비 임대업</td>\n",
       "      <td>렌탈(파렛트, OA장비, 건설장비)</td>\n",
       "      <td>2015-08-21</td>\n",
       "      <td>12월</td>\n",
       "      <td>이현우</td>\n",
       "      <td>http://www.ajnet.co.kr</td>\n",
       "      <td>서울특별시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>006840</td>\n",
       "      <td>KOSPI</td>\n",
       "      <td>AK홀딩스</td>\n",
       "      <td>기타 금융업</td>\n",
       "      <td>지주사업</td>\n",
       "      <td>1999-08-11</td>\n",
       "      <td>12월</td>\n",
       "      <td>채형석, 이석주(각자 대표이사)</td>\n",
       "      <td>http://www.aekyunggroup.co.kr</td>\n",
       "      <td>서울특별시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>054620</td>\n",
       "      <td>KOSDAQ</td>\n",
       "      <td>APS홀딩스</td>\n",
       "      <td>기타 금융업</td>\n",
       "      <td>인터넷 트래픽 솔루션</td>\n",
       "      <td>2001-12-04</td>\n",
       "      <td>12월</td>\n",
       "      <td>정기로</td>\n",
       "      <td>http://www.apsholdings.co.kr</td>\n",
       "      <td>경기도</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>265520</td>\n",
       "      <td>KOSDAQ</td>\n",
       "      <td>AP시스템</td>\n",
       "      <td>특수 목적용 기계 제조업</td>\n",
       "      <td>디스플레이 제조 장비</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>12월</td>\n",
       "      <td>김영주</td>\n",
       "      <td>http://www.apsystems.co.kr</td>\n",
       "      <td>경기도</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Symbol  Market    Name           Sector             Industry ListingDate  \\\n",
       "0  060310  KOSDAQ      3S    특수 목적용 기계 제조업          반도체 웨이퍼 캐리어  2002-04-23   \n",
       "1  095570   KOSPI  AJ네트웍스  산업용 기계 및 장비 임대업  렌탈(파렛트, OA장비, 건설장비)  2015-08-21   \n",
       "2  006840   KOSPI   AK홀딩스           기타 금융업                 지주사업  1999-08-11   \n",
       "3  054620  KOSDAQ  APS홀딩스           기타 금융업          인터넷 트래픽 솔루션  2001-12-04   \n",
       "4  265520  KOSDAQ   AP시스템    특수 목적용 기계 제조업          디스플레이 제조 장비  2017-04-07   \n",
       "\n",
       "  SettleMonth      Representative                       HomePage Region  \n",
       "0         03월  박종익, 김세완 (각자 대표이사)           http://www.3sref.com  서울특별시  \n",
       "1         12월                 이현우         http://www.ajnet.co.kr  서울특별시  \n",
       "2         12월   채형석, 이석주(각자 대표이사)  http://www.aekyunggroup.co.kr  서울특별시  \n",
       "3         12월                 정기로   http://www.apsholdings.co.kr    경기도  \n",
       "4         12월                 김영주     http://www.apsystems.co.kr    경기도  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 저장해 두었던 전체거래소목록(krx.csv) 파일을 불러오거나 \n",
    "# FinanceDataReader로 StockListing을 다시 받아서 df_krx 라는 변수에 담습니다.\n",
    "import FinanceDataReader as fdr\n",
    "df_krx = fdr.StockListing(\"KRX\")\n",
    "df_krx.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### merge 를 통한 종목코드 만들기\n",
    "* concat과 merge의 차이\n",
    "    * concat :  \n",
    "        * axis=0 행을 기준으로 위아래로 같은 컬럼끼리 값을 이어 붙여 새로운 행을 만듦\n",
    "        * axis=1 컬럼을 기준으로 인덱스가 같은 값을 옆으로 붙여 새로운 컬럼을 만듦\n",
    "    * merge :\n",
    "        * index 혹은 특정 컬럼 값을 기준으로 두 개의 데이터프레임을 연결\n",
    "<img src=\"https://pandas.pydata.org/docs/_images/merging_concat_axis1_join_axes.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>종목코드</th>\n",
       "      <th>종목명_전처리</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>060310</td>\n",
       "      <td>3S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>095570</td>\n",
       "      <td>AJ네트웍스</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>006840</td>\n",
       "      <td>AK홀딩스</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>054620</td>\n",
       "      <td>APS홀딩스</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>265520</td>\n",
       "      <td>AP시스템</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     종목코드 종목명_전처리\n",
       "0  060310      3S\n",
       "1  095570  AJ네트웍스\n",
       "2  006840   AK홀딩스\n",
       "3  054620  APS홀딩스\n",
       "4  265520   AP시스템"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"Symbol\", \"Name\" 만 사용하기 위해 따로 df_item_code_name 변수로 가져옵니다.\n",
    "# 연결할 key 값으로 사용할 컬럼명을 같게 만들어 주면 전처리가 줄어듭니다.\n",
    "# Symbol => \"종목코드\", Name => \"종목명_전처리\" 로 만들어주면 \n",
    "# \"종목명_전처리\" 를 키값으로 합쳤을 때 전처리가 줄어듭니다.\n",
    "df_item_code_name = df_krx[[\"Symbol\", \"Name\"]]\n",
    "df_item_code_name.columns = [\"종목코드\", \"종목명_전처리\"]\n",
    "df_item_code_name.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "cGh-BUCDpSVc",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79, 12)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 종목갯수를 확인하기 위해 df의 행과 열의 수를 출력합니다.\n",
    "raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79, 13)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 종목코드를 만들어주기 위해 종목명_전처리 컬럼을 기준으로 df_item_code_name 데이터프레임과 병합합니다.\n",
    "# df\n",
    "df = raw.merge(df_item_code_name, on=\"종목명_전처리\", how='left')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>종목명</th>\n",
       "      <th>종목명.1</th>\n",
       "      <th>현재가</th>\n",
       "      <th>전일비</th>\n",
       "      <th>등락률</th>\n",
       "      <th>매수호가</th>\n",
       "      <th>매도호가</th>\n",
       "      <th>거래량</th>\n",
       "      <th>거래대금</th>\n",
       "      <th>전일거래량</th>\n",
       "      <th>조회일자</th>\n",
       "      <th>종목명_전처리</th>\n",
       "      <th>종목코드</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>웰크론한텍 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>+20.79%</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>4770.0</td>\n",
       "      <td>32171066.0</td>\n",
       "      <td>146663.0</td>\n",
       "      <td>6941573.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>웰크론한텍</td>\n",
       "      <td>076080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>이엔드디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>+9.02%</td>\n",
       "      <td>43450.0</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>999308.0</td>\n",
       "      <td>41912.0</td>\n",
       "      <td>475248.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>이엔드디</td>\n",
       "      <td>101360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>삼진엘앤디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>+8.51%</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>4470.0</td>\n",
       "      <td>65461738.0</td>\n",
       "      <td>310756.0</td>\n",
       "      <td>45874433.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>삼진엘앤디</td>\n",
       "      <td>054090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SK이노베이션</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>283000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>+7.60%</td>\n",
       "      <td>283000.0</td>\n",
       "      <td>283500.0</td>\n",
       "      <td>3363732.0</td>\n",
       "      <td>950235.0</td>\n",
       "      <td>2014564.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>SK이노베이션</td>\n",
       "      <td>096770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>일진머티리얼즈</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>56400.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>+7.22%</td>\n",
       "      <td>56300.0</td>\n",
       "      <td>56400.0</td>\n",
       "      <td>1661973.0</td>\n",
       "      <td>91832.0</td>\n",
       "      <td>399206.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>일진머티리얼즈</td>\n",
       "      <td>020150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       종목명     종목명.1       현재가      전일비      등락률      매수호가      매도호가  \\\n",
       "0  웰크론한텍 *  테마 편입 사유    4765.0    820.0  +20.79%    4765.0    4770.0   \n",
       "1   이엔드디 *  테마 편입 사유   43500.0   3600.0   +9.02%   43450.0   43500.0   \n",
       "2  삼진엘앤디 *  테마 편입 사유    4465.0    350.0   +8.51%    4465.0    4470.0   \n",
       "3  SK이노베이션  테마 편입 사유  283000.0  20000.0   +7.60%  283000.0  283500.0   \n",
       "4  일진머티리얼즈  테마 편입 사유   56400.0   3800.0   +7.22%   56300.0   56400.0   \n",
       "\n",
       "          거래량      거래대금       전일거래량        조회일자  종목명_전처리    종목코드  \n",
       "0  32171066.0  146663.0   6941573.0  2021-01-09    웰크론한텍  076080  \n",
       "1    999308.0   41912.0    475248.0  2021-01-09     이엔드디  101360  \n",
       "2  65461738.0  310756.0  45874433.0  2021-01-09    삼진엘앤디  054090  \n",
       "3   3363732.0  950235.0   2014564.0  2021-01-09  SK이노베이션  096770  \n",
       "4   1661973.0   91832.0    399206.0  2021-01-09  일진머티리얼즈  020150  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 병합이 잘 되었는지 확인합니다.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QUofMFm2pSVc"
   },
   "source": [
    "## 종목상세정보 수집\n",
    "\n",
    "'종목코드', '시가총액', '시가총액순위', '상장주식수', '액면가l매매단위', '외국인한도주식수(A)',\n",
    "'외국인보유주식수(B)', '외국인소진율(B/A)', '투자의견l목표주가', '52주최고l최저',\n",
    "'추정PERlEPS', '동일업종 PER', '동일업종 등락률', '배당수익률'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전체 종목을 수집하기 전에 일부 종목을 먼저 수집합니다.\n",
    "item_code = \"068270\"\n",
    "item_name = \"셀트리온\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "o4qSGs-RpSVc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://finance.naver.com/item/main.nhn?code=068270'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 종목상세정보를 위한 url을 확보합니다.\n",
    "# item_code에는 종목코드 수집시에 마지막에 수집된 데이터가 남아있습니다.\n",
    "item_url = f\"https://finance.naver.com/item/main.nhn?code={item_code}\"\n",
    "item_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "Va-PEsOHpSVc",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read_html을 이용해 데이터를 수집합니다.\n",
    "tables = pd.read_html(item_url, encoding=\"cp949\")\n",
    "len(tables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "V4re1BHApSVc",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>주요재무정보</th>\n",
       "      <th>최근 분기 실적</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>주요재무정보</th>\n",
       "      <th>2020.12(E)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>주요재무정보</th>\n",
       "      <th>IFRS연결</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>매출액</td>\n",
       "      <td>5260.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>영업이익</td>\n",
       "      <td>2444.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>당기순이익</td>\n",
       "      <td>1724.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>영업이익률</td>\n",
       "      <td>46.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>순이익률</td>\n",
       "      <td>32.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ROE(지배주주)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>부채비율</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>당좌비율</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>유보율</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EPS(원)</td>\n",
       "      <td>1282.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PER(배)</td>\n",
       "      <td>286.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BPS(원)</td>\n",
       "      <td>25019.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PBR(배)</td>\n",
       "      <td>14.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>주당배당금(원)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>시가배당률(%)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>배당성향(%)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       주요재무정보   최근 분기 실적\n",
       "       주요재무정보 2020.12(E)\n",
       "       주요재무정보     IFRS연결\n",
       "0         매출액    5260.00\n",
       "1        영업이익    2444.00\n",
       "2       당기순이익    1724.00\n",
       "3       영업이익률      46.46\n",
       "4        순이익률      32.78\n",
       "5   ROE(지배주주)        NaN\n",
       "6        부채비율        NaN\n",
       "7        당좌비율        NaN\n",
       "8         유보율        NaN\n",
       "9      EPS(원)    1282.00\n",
       "10     PER(배)     286.31\n",
       "11     BPS(원)   25019.00\n",
       "12     PBR(배)      14.67\n",
       "13   주당배당금(원)        NaN\n",
       "14   시가배당률(%)        NaN\n",
       "15    배당성향(%)        NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 제대로 수집이 되었는지 확인합니다.\n",
    "tables[3].iloc[:, [0, -1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "6MtjfQQ0pSVc"
   },
   "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>외국인한도주식수(A)</th>\n",
       "      <th>외국인보유주식수(B)</th>\n",
       "      <th>외국인소진율(B/A)</th>\n",
       "      <th>투자의견l목표주가</th>\n",
       "      <th>52주최고l최저</th>\n",
       "      <th>PERlEPS(2020.09)</th>\n",
       "      <th>추정PERlEPS</th>\n",
       "      <th>PBRlBPS (2020.09)</th>\n",
       "      <th>배당수익률</th>\n",
       "      <th>동일업종 PER</th>\n",
       "      <th>동일업종 등락률</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>134997805</td>\n",
       "      <td>28093033</td>\n",
       "      <td>20.81%</td>\n",
       "      <td>3.93매수  l  364,325</td>\n",
       "      <td>396,240  l  136,008</td>\n",
       "      <td>99.19배  l  3,700원</td>\n",
       "      <td>85.00배  l  4,325원</td>\n",
       "      <td>15.47배  l  23,717원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.73배</td>\n",
       "      <td>+0.84%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 외국인한도주식수(A) 외국인보유주식수(B) 외국인소진율(B/A)           투자의견l목표주가  \\\n",
       "1   134997805    28093033      20.81%  3.93매수  l  364,325   \n",
       "\n",
       "0             52주최고l최저   PERlEPS(2020.09)          추정PERlEPS  \\\n",
       "1  396,240  l  136,008  99.19배  l  3,700원  85.00배  l  4,325원   \n",
       "\n",
       "0   PBRlBPS (2020.09) 배당수익률 동일업종 PER 동일업종 등락률  \n",
       "1  15.47배  l  23,717원   NaN  109.73배   +0.84%  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tables의 원소값을 확인하여 원하는 데이터가 어디에 있는지 확인합니다.\n",
    "pd.concat([tables[6], tables[7], tables[8], tables[9]]).set_index(0).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eMOVYQ-fpSVc"
   },
   "source": [
    "## 컬럼명과 값 형태로 되어 있는 테이블만 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 수집한 정보를 item_info에 list 형태로 관리합니다.\n",
    "item_info = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 가장 최근의 주요재무정보를 가져와서 item_info 라는 변수에 담습니다.\n",
    "finance_info = tables[3].iloc[:, [0, -1]]\n",
    "finance_info.columns = [0, 1]\n",
    "item_info.append(finance_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "NWylba2PpSVc"
   },
   "outputs": [],
   "source": [
    "# tables을 반복문으로 돌며 컬럼의 수가 2인 테이블만 따로 추출해 \n",
    "# 위에서 만든 item_info 에 리스트 형태로 추가합니다.\n",
    "for t in tables:\n",
    "    if t.shape[1] == 2:\n",
    "        item_info.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "daNwwV1lpSVc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[            0         1\n",
       " 0         매출액   5260.00\n",
       " 1        영업이익   2444.00\n",
       " 2       당기순이익   1724.00\n",
       " 3       영업이익률     46.46\n",
       " 4        순이익률     32.78\n",
       " 5   ROE(지배주주)       NaN\n",
       " 6        부채비율       NaN\n",
       " 7        당좌비율       NaN\n",
       " 8         유보율       NaN\n",
       " 9      EPS(원)   1282.00\n",
       " 10     PER(배)    286.31\n",
       " 11     BPS(원)  25019.00\n",
       " 12     PBR(배)     14.67\n",
       " 13   주당배당금(원)       NaN\n",
       " 14   시가배당률(%)       NaN\n",
       " 15    배당성향(%)       NaN,\n",
       "           0               1\n",
       " 0      시가총액  49조  5,442  억원\n",
       " 1    시가총액순위          코스피 9위\n",
       " 2     상장주식수       134997805\n",
       " 3  액면가l매매단위   1,000원  l  1주,\n",
       "              0          1\n",
       " 0  외국인한도주식수(A)  134997805\n",
       " 1  외국인보유주식수(B)   28093033\n",
       " 2  외국인소진율(B/A)     20.81%,\n",
       "            0                    1\n",
       " 0  투자의견l목표주가   3.93매수  l  364,325\n",
       " 1   52주최고l최저  396,240  l  136,008,\n",
       "                    0                   1\n",
       " 0   PERlEPS(2020.09)   99.19배  l  3,700원\n",
       " 1          추정PERlEPS   85.00배  l  4,325원\n",
       " 2  PBRlBPS (2020.09)  15.47배  l  23,717원\n",
       " 3              배당수익률                 NaN,\n",
       "           0        1\n",
       " 0  동일업종 PER  109.73배\n",
       " 1  동일업종 등락률   +0.84%]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 위에서 만든 리스트의 값을 확인합니다.\n",
    "item_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z6x2p1XypSVc"
   },
   "source": [
    "## concat을 통해 데이터를 병합\n",
    "\n",
    "* concat과 merge의 차이\n",
    "    * concat :  \n",
    "        * axis=0 행을 기준으로 위아래로 같은 컬럼끼리 값을 이어 붙여 새로운 행을 만듦\n",
    "        * axis=1 컬럼을 기준으로 인덱스가 같은 값을 옆으로 붙여 새로운 컬럼을 만듦\n",
    "    * merge :\n",
    "        * index 혹은 특정 컬럼 값을 기준으로 두 개의 데이터프레임을 연결\n",
    "\n",
    "<img src=\"https://pandas.pydata.org/pandas-docs/stable/_images/merging_concat_keys.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>외국인한도주식수(A)</td>\n",
       "      <td>134997805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>외국인보유주식수(B)</td>\n",
       "      <td>28093033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>외국인소진율(B/A)</td>\n",
       "      <td>20.81%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0          1\n",
       "0  외국인한도주식수(A)  134997805\n",
       "1  외국인보유주식수(B)   28093033\n",
       "2  외국인소진율(B/A)     20.81%"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# item_info[0] 의 컬럼명을 봅니다. 같은 컬럼이 아니면 행끼리 합칠 수 없습니다.\n",
    "item_info[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "4Hd9PBoFpSVc"
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>매출액</td>\n",
       "      <td>5260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>영업이익</td>\n",
       "      <td>2444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>당기순이익</td>\n",
       "      <td>1724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>영업이익률</td>\n",
       "      <td>46.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>순이익률</td>\n",
       "      <td>32.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ROE(지배주주)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>부채비율</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>당좌비율</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>유보율</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EPS(원)</td>\n",
       "      <td>1282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PER(배)</td>\n",
       "      <td>286.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BPS(원)</td>\n",
       "      <td>25019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PBR(배)</td>\n",
       "      <td>14.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>주당배당금(원)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>시가배당률(%)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>배당성향(%)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>시가총액</td>\n",
       "      <td>49조  5,442  억원</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>시가총액순위</td>\n",
       "      <td>코스피 9위</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>상장주식수</td>\n",
       "      <td>134997805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>액면가l매매단위</td>\n",
       "      <td>1,000원  l  1주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>외국인한도주식수(A)</td>\n",
       "      <td>134997805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>외국인보유주식수(B)</td>\n",
       "      <td>28093033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>외국인소진율(B/A)</td>\n",
       "      <td>20.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>투자의견l목표주가</td>\n",
       "      <td>3.93매수  l  364,325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52주최고l최저</td>\n",
       "      <td>396,240  l  136,008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PERlEPS(2020.09)</td>\n",
       "      <td>99.19배  l  3,700원</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>추정PERlEPS</td>\n",
       "      <td>85.00배  l  4,325원</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PBRlBPS (2020.09)</td>\n",
       "      <td>15.47배  l  23,717원</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>배당수익률</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>동일업종 PER</td>\n",
       "      <td>109.73배</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>동일업종 등락률</td>\n",
       "      <td>+0.84%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0                    1\n",
       "0                 매출액                 5260\n",
       "1                영업이익                 2444\n",
       "2               당기순이익                 1724\n",
       "3               영업이익률                46.46\n",
       "4                순이익률                32.78\n",
       "5           ROE(지배주주)                  NaN\n",
       "6                부채비율                  NaN\n",
       "7                당좌비율                  NaN\n",
       "8                 유보율                  NaN\n",
       "9              EPS(원)                 1282\n",
       "10             PER(배)               286.31\n",
       "11             BPS(원)                25019\n",
       "12             PBR(배)                14.67\n",
       "13           주당배당금(원)                  NaN\n",
       "14           시가배당률(%)                  NaN\n",
       "15            배당성향(%)                  NaN\n",
       "0                시가총액       49조  5,442  억원\n",
       "1              시가총액순위               코스피 9위\n",
       "2               상장주식수            134997805\n",
       "3            액면가l매매단위        1,000원  l  1주\n",
       "0         외국인한도주식수(A)            134997805\n",
       "1         외국인보유주식수(B)             28093033\n",
       "2         외국인소진율(B/A)               20.81%\n",
       "0           투자의견l목표주가   3.93매수  l  364,325\n",
       "1            52주최고l최저  396,240  l  136,008\n",
       "0    PERlEPS(2020.09)    99.19배  l  3,700원\n",
       "1           추정PERlEPS    85.00배  l  4,325원\n",
       "2   PBRlBPS (2020.09)   15.47배  l  23,717원\n",
       "3               배당수익률                  NaN\n",
       "0            동일업종 PER              109.73배\n",
       "1            동일업종 등락률               +0.84%"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat을 이용하여 item_info를 하나의 DataFrame으로 만듭니다.\n",
    "df_item = pd.concat(item_info)\n",
    "df_item"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7R7c2d3ppSVc"
   },
   "source": [
    "## transpose를 통해 행과 열의 위치를 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>매출액</th>\n",
       "      <td>5260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>영업이익</th>\n",
       "      <td>2444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>당기순이익</th>\n",
       "      <td>1724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>영업이익률</th>\n",
       "      <td>46.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>순이익률</th>\n",
       "      <td>32.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROE(지배주주)</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부채비율</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>당좌비율</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>유보율</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPS(원)</th>\n",
       "      <td>1282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PER(배)</th>\n",
       "      <td>286.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPS(원)</th>\n",
       "      <td>25019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PBR(배)</th>\n",
       "      <td>14.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>주당배당금(원)</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>시가배당률(%)</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>배당성향(%)</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>시가총액</th>\n",
       "      <td>49조  5,442  억원</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>시가총액순위</th>\n",
       "      <td>코스피 9위</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>상장주식수</th>\n",
       "      <td>134997805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>액면가l매매단위</th>\n",
       "      <td>1,000원  l  1주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>외국인한도주식수(A)</th>\n",
       "      <td>134997805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>외국인보유주식수(B)</th>\n",
       "      <td>28093033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>외국인소진율(B/A)</th>\n",
       "      <td>20.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>투자의견l목표주가</th>\n",
       "      <td>3.93매수  l  364,325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52주최고l최저</th>\n",
       "      <td>396,240  l  136,008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERlEPS(2020.09)</th>\n",
       "      <td>99.19배  l  3,700원</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>추정PERlEPS</th>\n",
       "      <td>85.00배  l  4,325원</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PBRlBPS (2020.09)</th>\n",
       "      <td>15.47배  l  23,717원</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>배당수익률</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동일업종 PER</th>\n",
       "      <td>109.73배</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동일업종 등락률</th>\n",
       "      <td>+0.84%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     1\n",
       "0                                     \n",
       "매출액                               5260\n",
       "영업이익                              2444\n",
       "당기순이익                             1724\n",
       "영업이익률                            46.46\n",
       "순이익률                             32.78\n",
       "ROE(지배주주)                          NaN\n",
       "부채비율                               NaN\n",
       "당좌비율                               NaN\n",
       "유보율                                NaN\n",
       "EPS(원)                            1282\n",
       "PER(배)                          286.31\n",
       "BPS(원)                           25019\n",
       "PBR(배)                           14.67\n",
       "주당배당금(원)                           NaN\n",
       "시가배당률(%)                           NaN\n",
       "배당성향(%)                            NaN\n",
       "시가총액                    49조  5,442  억원\n",
       "시가총액순위                          코스피 9위\n",
       "상장주식수                        134997805\n",
       "액면가l매매단위                 1,000원  l  1주\n",
       "외국인한도주식수(A)                  134997805\n",
       "외국인보유주식수(B)                   28093033\n",
       "외국인소진율(B/A)                     20.81%\n",
       "투자의견l목표주가           3.93매수  l  364,325\n",
       "52주최고l최저           396,240  l  136,008\n",
       "PERlEPS(2020.09)     99.19배  l  3,700원\n",
       "추정PERlEPS            85.00배  l  4,325원\n",
       "PBRlBPS (2020.09)   15.47배  l  23,717원\n",
       "배당수익률                              NaN\n",
       "동일업종 PER                       109.73배\n",
       "동일업종 등락률                        +0.84%"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_item.set_index(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "tUp-hTXZpSVc",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>매출액</th>\n",
       "      <th>영업이익</th>\n",
       "      <th>당기순이익</th>\n",
       "      <th>영업이익률</th>\n",
       "      <th>순이익률</th>\n",
       "      <th>ROE(지배주주)</th>\n",
       "      <th>부채비율</th>\n",
       "      <th>당좌비율</th>\n",
       "      <th>유보율</th>\n",
       "      <th>EPS(원)</th>\n",
       "      <th>...</th>\n",
       "      <th>외국인보유주식수(B)</th>\n",
       "      <th>외국인소진율(B/A)</th>\n",
       "      <th>투자의견l목표주가</th>\n",
       "      <th>52주최고l최저</th>\n",
       "      <th>PERlEPS(2020.09)</th>\n",
       "      <th>추정PERlEPS</th>\n",
       "      <th>PBRlBPS (2020.09)</th>\n",
       "      <th>배당수익률</th>\n",
       "      <th>동일업종 PER</th>\n",
       "      <th>동일업종 등락률</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5260</td>\n",
       "      <td>2444</td>\n",
       "      <td>1724</td>\n",
       "      <td>46.46</td>\n",
       "      <td>32.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1282</td>\n",
       "      <td>...</td>\n",
       "      <td>28093033</td>\n",
       "      <td>20.81%</td>\n",
       "      <td>3.93매수  l  364,325</td>\n",
       "      <td>396,240  l  136,008</td>\n",
       "      <td>99.19배  l  3,700원</td>\n",
       "      <td>85.00배  l  4,325원</td>\n",
       "      <td>15.47배  l  23,717원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.73배</td>\n",
       "      <td>+0.84%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0   매출액  영업이익 당기순이익  영업이익률   순이익률 ROE(지배주주) 부채비율 당좌비율  유보율 EPS(원)  ...  \\\n",
       "1  5260  2444  1724  46.46  32.78       NaN  NaN  NaN  NaN   1282  ...   \n",
       "\n",
       "0 외국인보유주식수(B) 외국인소진율(B/A)           투자의견l목표주가             52주최고l최저  \\\n",
       "1    28093033      20.81%  3.93매수  l  364,325  396,240  l  136,008   \n",
       "\n",
       "0   PERlEPS(2020.09)          추정PERlEPS   PBRlBPS (2020.09) 배당수익률 동일업종 PER  \\\n",
       "1  99.19배  l  3,700원  85.00배  l  4,325원  15.47배  l  23,717원   NaN  109.73배   \n",
       "\n",
       "0 동일업종 등락률  \n",
       "1   +0.84%  \n",
       "\n",
       "[1 rows x 31 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transpose는 .transpose() 혹은  .T 로 사용해 보실 수 있습니다.\n",
    "df_item.set_index(0).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NUuOcGZbpSVc"
   },
   "source": [
    "## 개별 주가 정보를 수집하는 함수를 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "4Qo2qU_HpSVc"
   },
   "outputs": [],
   "source": [
    "# 위에서 한 종목코드에 대해 개별 주가 정보를 얻는 방법을 확인하였으니,\n",
    "# 같은 방법으로 모든 종목코드의 주가 정보를 추출합니다.\n",
    "# 무리한 네트워크 요청은 서버에 부담을 줍니다. \n",
    "# sleep을 사용해서 서버에 무리한 요청을 하지 않습니다.\n",
    "import time\n",
    "\n",
    "def get_item_info(item_code):\n",
    "    \n",
    "    item_url = f\"https://finance.naver.com/item/main.nhn?code={item_code}\"\n",
    "    tables = pd.read_html(item_url, encoding=\"cp949\")\n",
    "    \n",
    "    item_info = []\n",
    "    finance_info = tables[3].iloc[:, [0, -1]]\n",
    "    finance_info.columns = [0, 1]\n",
    "    item_info.append(finance_info)\n",
    "    \n",
    "    for t in tables:\n",
    "        if t.shape[1] == 2:\n",
    "            item_info.append(t)\n",
    "            \n",
    "    df_item = pd.concat(item_info)\n",
    "    df_item = df_item.set_index(0).T\n",
    "    time.sleep(0.1)\n",
    "    return df_item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "z6bpZx4upSVc"
   },
   "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>매출액</th>\n",
       "      <th>영업이익</th>\n",
       "      <th>당기순이익</th>\n",
       "      <th>영업이익률</th>\n",
       "      <th>순이익률</th>\n",
       "      <th>ROE(지배주주)</th>\n",
       "      <th>부채비율</th>\n",
       "      <th>당좌비율</th>\n",
       "      <th>유보율</th>\n",
       "      <th>EPS(원)</th>\n",
       "      <th>...</th>\n",
       "      <th>외국인보유주식수(B)</th>\n",
       "      <th>외국인소진율(B/A)</th>\n",
       "      <th>투자의견l목표주가</th>\n",
       "      <th>52주최고l최저</th>\n",
       "      <th>PERlEPS(2020.09)</th>\n",
       "      <th>추정PERlEPS</th>\n",
       "      <th>PBRlBPS (2020.09)</th>\n",
       "      <th>배당수익률</th>\n",
       "      <th>동일업종 PER</th>\n",
       "      <th>동일업종 등락률</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5260</td>\n",
       "      <td>2444</td>\n",
       "      <td>1724</td>\n",
       "      <td>46.46</td>\n",
       "      <td>32.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1282</td>\n",
       "      <td>...</td>\n",
       "      <td>28093033</td>\n",
       "      <td>20.81%</td>\n",
       "      <td>3.93매수  l  364,325</td>\n",
       "      <td>396,240  l  136,008</td>\n",
       "      <td>99.19배  l  3,700원</td>\n",
       "      <td>85.00배  l  4,325원</td>\n",
       "      <td>15.47배  l  23,717원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.73배</td>\n",
       "      <td>+0.84%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0   매출액  영업이익 당기순이익  영업이익률   순이익률 ROE(지배주주) 부채비율 당좌비율  유보율 EPS(원)  ...  \\\n",
       "1  5260  2444  1724  46.46  32.78       NaN  NaN  NaN  NaN   1282  ...   \n",
       "\n",
       "0 외국인보유주식수(B) 외국인소진율(B/A)           투자의견l목표주가             52주최고l최저  \\\n",
       "1    28093033      20.81%  3.93매수  l  364,325  396,240  l  136,008   \n",
       "\n",
       "0   PERlEPS(2020.09)          추정PERlEPS   PBRlBPS (2020.09) 배당수익률 동일업종 PER  \\\n",
       "1  99.19배  l  3,700원  85.00배  l  4,325원  15.47배  l  23,717원   NaN  109.73배   \n",
       "\n",
       "0 동일업종 등락률  \n",
       "1   +0.84%  \n",
       "\n",
       "[1 rows x 31 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터프레임 형태로 종목정보를 수집해 왔습니다.\n",
    "# 다른 종목도 아래와 같은 형태로 모아서 concat 으로 연결해 준다면 \n",
    "# 종목끼리 정보를 비교해 볼 수 있을것 같습니다.\n",
    "get_item_info(item_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SQx8B2iupSVd"
   },
   "source": [
    "## tqdm은?\n",
    "* 오래 걸리는 작업의 진행 상태를 표시합니다.\n",
    "* https://tqdm.github.io/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "rkPkaDV8pSVd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/corazzon/opt/anaconda3/lib/python3.7/site-packages/tqdm/std.py:701: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  from pandas import Panel\n"
     ]
    }
   ],
   "source": [
    "# tqdm은 total quality data management의 약자입니다.\n",
    "# tqdm.auto 에서 tqdm을 불러옵니다.\n",
    "from tqdm.auto import tqdm\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "KWDNC-R7pSVd"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2feb66c335bd4b959800957269a39bf7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/79 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DataFrame의 종목코드 column에 있는 종목코드들값을 get_item_info 함수에 적용되어\n",
    "# result에 series형태로 저장됩니다.\n",
    "result = df[\"종목코드\"].progress_apply(get_item_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0  매출액 영업이익 당기순이익 영업이익률 순이익률 ROE(지배주주) 부채비율 당좌...\n",
       "1     0  매출액 영업이익 당기순이익  영업이익률   순이익률 ROE(지배주주) 부채비율...\n",
       "2     0  매출액 영업이익 당기순이익 영업이익률 순이익률 ROE(지배주주) 부채비율 당좌...\n",
       "3     0    매출액  영업이익 당기순이익 영업이익률 순이익률 ROE(지배주주) 부채비율...\n",
       "4     0   매출액 영업이익 당기순이익  영업이익률 순이익률 ROE(지배주주) 부채비율 ...\n",
       "                            ...                        \n",
       "74    0  매출액 영업이익 당기순이익 영업이익률 순이익률 ROE(지배주주) 부채비율 당좌...\n",
       "75    0  매출액 영업이익 당기순이익 영업이익률 순이익률 ROE(지배주주) 부채비율 당좌...\n",
       "76    0  매출액 영업이익 당기순이익 영업이익률 순이익률 ROE(지배주주) 부채비율 당좌...\n",
       "77    0  매출액 영업이익 당기순이익 영업이익률 순이익률 ROE(지배주주) 부채비율 당좌...\n",
       "78    0  매출액 영업이익 당기순이익 영업이익률 순이익률 ROE(지배주주) 부채비율 당좌...\n",
       "Name: 종목코드, Length: 79, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## concat 을 통한 데이터 프레임 합치기\n",
    "* 수집한 종목마다의 정보를 하나의 데이터프레임으로 만듭니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "xuFeHo_RpSVd"
   },
   "outputs": [],
   "source": [
    "# result를 list로 만들고 concat을 통해 DataFrame으로 만듭니다.\n",
    "# concat으로 데이터프레임을 합쳐줄때는 리스트 형태로 각 데이터프레임을 넣어주어야 합니다.\n",
    "df_item_info = pd.concat(result.tolist())\n",
    "df_item_info = df_item_info.reset_index()\n",
    "df_item_info[\"종목코드\"] = df[\"종목코드\"]\n",
    "del df_item_info[\"index\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>매출액</th>\n",
       "      <th>영업이익</th>\n",
       "      <th>당기순이익</th>\n",
       "      <th>영업이익률</th>\n",
       "      <th>순이익률</th>\n",
       "      <th>ROE(지배주주)</th>\n",
       "      <th>부채비율</th>\n",
       "      <th>당좌비율</th>\n",
       "      <th>유보율</th>\n",
       "      <th>EPS(원)</th>\n",
       "      <th>...</th>\n",
       "      <th>PBRlBPS (2020.09)</th>\n",
       "      <th>배당수익률</th>\n",
       "      <th>동일업종 PER</th>\n",
       "      <th>동일업종 등락률</th>\n",
       "      <th>PERlEPS(2019.12)</th>\n",
       "      <th>PBRlBPS (2019.12)</th>\n",
       "      <th>배당수익률l2020.12</th>\n",
       "      <th>PERlEPS</th>\n",
       "      <th>PBRlBPS</th>\n",
       "      <th>종목코드</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.45배  l  3,290원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.42배</td>\n",
       "      <td>+0.03%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>076080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>275</td>\n",
       "      <td>64</td>\n",
       "      <td>61</td>\n",
       "      <td>23.27</td>\n",
       "      <td>22.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.44배</td>\n",
       "      <td>+2.24%</td>\n",
       "      <td>46.82배  l  929원</td>\n",
       "      <td>7.26배  l  5,995원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.15배  l  3,891원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.29배</td>\n",
       "      <td>-0.54%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>054090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>85750</td>\n",
       "      <td>-1439</td>\n",
       "      <td>-2403</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1558</td>\n",
       "      <td>...</td>\n",
       "      <td>1.58배  l  179,631원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.88배</td>\n",
       "      <td>+4.60%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>096770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1673</td>\n",
       "      <td>184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>296</td>\n",
       "      <td>...</td>\n",
       "      <td>4.28배  l  13,170원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.14배</td>\n",
       "      <td>+2.96%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>020150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     매출액  영업이익 당기순이익  영업이익률   순이익률 ROE(지배주주) 부채비율 당좌비율  유보율 EPS(원)  ...  \\\n",
       "0    NaN   NaN   NaN    NaN    NaN       NaN  NaN  NaN  NaN    NaN  ...   \n",
       "1    275    64    61  23.27  22.18       NaN  NaN  NaN  NaN    600  ...   \n",
       "2    NaN   NaN   NaN    NaN    NaN       NaN  NaN  NaN  NaN    NaN  ...   \n",
       "3  85750 -1439 -2403  -1.68   -2.8       NaN  NaN  NaN  NaN  -1558  ...   \n",
       "4   1673   184   NaN  10.97    NaN       NaN  NaN  NaN  NaN    296  ...   \n",
       "\n",
       "    PBRlBPS (2020.09) 배당수익률 동일업종 PER 동일업종 등락률 PERlEPS(2019.12)  \\\n",
       "0    1.45배  l  3,290원   NaN    7.42배   +0.03%              NaN   \n",
       "1                 NaN   NaN   64.44배   +2.24%  46.82배  l  929원   \n",
       "2    1.15배  l  3,891원   NaN   86.29배   -0.54%              NaN   \n",
       "3  1.58배  l  179,631원   NaN  -12.88배   +4.60%              NaN   \n",
       "4   4.28배  l  13,170원   NaN   60.14배   +2.96%              NaN   \n",
       "\n",
       "  PBRlBPS (2019.12) 배당수익률l2020.12 PERlEPS PBRlBPS    종목코드  \n",
       "0               NaN           NaN     NaN     NaN  076080  \n",
       "1  7.26배  l  5,995원           NaN     NaN     NaN  101360  \n",
       "2               NaN           NaN     NaN     NaN  054090  \n",
       "3               NaN           NaN     NaN     NaN  096770  \n",
       "4               NaN           NaN     NaN     NaN  020150  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_item_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "kAqFjUXNSzZh"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79, 13)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 합쳐줄 해당 종목/테마 데이터프레임을 봅니다.\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KHGtu04tpSVd"
   },
   "source": [
    "## merge 를 통한 데이터프레임 합치기\n",
    "* 업종/테마 목록과 개별 종목 정보를 수집한 데이터프레임을 합쳐줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>종목명</th>\n",
       "      <th>종목명.1</th>\n",
       "      <th>현재가</th>\n",
       "      <th>전일비</th>\n",
       "      <th>등락률</th>\n",
       "      <th>매수호가</th>\n",
       "      <th>매도호가</th>\n",
       "      <th>거래량</th>\n",
       "      <th>거래대금</th>\n",
       "      <th>전일거래량</th>\n",
       "      <th>조회일자</th>\n",
       "      <th>종목명_전처리</th>\n",
       "      <th>종목코드</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>웰크론한텍 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>+20.79%</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>4770.0</td>\n",
       "      <td>32171066.0</td>\n",
       "      <td>146663.0</td>\n",
       "      <td>6941573.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>웰크론한텍</td>\n",
       "      <td>076080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>이엔드디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>+9.02%</td>\n",
       "      <td>43450.0</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>999308.0</td>\n",
       "      <td>41912.0</td>\n",
       "      <td>475248.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>이엔드디</td>\n",
       "      <td>101360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>삼진엘앤디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>+8.51%</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>4470.0</td>\n",
       "      <td>65461738.0</td>\n",
       "      <td>310756.0</td>\n",
       "      <td>45874433.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>삼진엘앤디</td>\n",
       "      <td>054090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SK이노베이션</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>283000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>+7.60%</td>\n",
       "      <td>283000.0</td>\n",
       "      <td>283500.0</td>\n",
       "      <td>3363732.0</td>\n",
       "      <td>950235.0</td>\n",
       "      <td>2014564.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>SK이노베이션</td>\n",
       "      <td>096770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>일진머티리얼즈</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>56400.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>+7.22%</td>\n",
       "      <td>56300.0</td>\n",
       "      <td>56400.0</td>\n",
       "      <td>1661973.0</td>\n",
       "      <td>91832.0</td>\n",
       "      <td>399206.0</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>일진머티리얼즈</td>\n",
       "      <td>020150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       종목명     종목명.1       현재가      전일비      등락률      매수호가      매도호가  \\\n",
       "0  웰크론한텍 *  테마 편입 사유    4765.0    820.0  +20.79%    4765.0    4770.0   \n",
       "1   이엔드디 *  테마 편입 사유   43500.0   3600.0   +9.02%   43450.0   43500.0   \n",
       "2  삼진엘앤디 *  테마 편입 사유    4465.0    350.0   +8.51%    4465.0    4470.0   \n",
       "3  SK이노베이션  테마 편입 사유  283000.0  20000.0   +7.60%  283000.0  283500.0   \n",
       "4  일진머티리얼즈  테마 편입 사유   56400.0   3800.0   +7.22%   56300.0   56400.0   \n",
       "\n",
       "          거래량      거래대금       전일거래량        조회일자  종목명_전처리    종목코드  \n",
       "0  32171066.0  146663.0   6941573.0  2021-01-09    웰크론한텍  076080  \n",
       "1    999308.0   41912.0    475248.0  2021-01-09     이엔드디  101360  \n",
       "2  65461738.0  310756.0  45874433.0  2021-01-09    삼진엘앤디  054090  \n",
       "3   3363732.0  950235.0   2014564.0  2021-01-09  SK이노베이션  096770  \n",
       "4   1661973.0   91832.0    399206.0  2021-01-09  일진머티리얼즈  020150  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "LwUqTFqmpSVd"
   },
   "outputs": [],
   "source": [
    "# DateFrame df와 df_item_info를 공통되는 column을 통해 합칩니다.\n",
    "# \"종목코드\" 컬럼이 두 데이터프레임에 있기 때문에 \"종목코드\" 컬럼을 기준으로 merge됩니다.\n",
    "df_info = df.merge(df_item_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "dV3RGBsqpSVd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((79, 13), (79, 37), (79, 49))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape의 column수를 통해 데이터(종목코드)가 합쳐 졌음을 알 수 있습니다.\n",
    "# df, df_item_info, df_info 확인하기\n",
    "df.shape, df_item_info.shape, df_info.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "BgKt3j-tpSVd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>종목명</th>\n",
       "      <th>종목명.1</th>\n",
       "      <th>현재가</th>\n",
       "      <th>전일비</th>\n",
       "      <th>등락률</th>\n",
       "      <th>매수호가</th>\n",
       "      <th>매도호가</th>\n",
       "      <th>거래량</th>\n",
       "      <th>거래대금</th>\n",
       "      <th>전일거래량</th>\n",
       "      <th>...</th>\n",
       "      <th>추정PERlEPS</th>\n",
       "      <th>PBRlBPS (2020.09)</th>\n",
       "      <th>배당수익률</th>\n",
       "      <th>동일업종 PER</th>\n",
       "      <th>동일업종 등락률</th>\n",
       "      <th>PERlEPS(2019.12)</th>\n",
       "      <th>PBRlBPS (2019.12)</th>\n",
       "      <th>배당수익률l2020.12</th>\n",
       "      <th>PERlEPS</th>\n",
       "      <th>PBRlBPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>웰크론한텍 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>+20.79%</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>4770.0</td>\n",
       "      <td>32171066.0</td>\n",
       "      <td>146663.0</td>\n",
       "      <td>6941573.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A  l  N/A</td>\n",
       "      <td>1.45배  l  3,290원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.42배</td>\n",
       "      <td>+0.03%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>이엔드디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>+9.02%</td>\n",
       "      <td>43450.0</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>999308.0</td>\n",
       "      <td>41912.0</td>\n",
       "      <td>475248.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A  l  N/A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.44배</td>\n",
       "      <td>+2.24%</td>\n",
       "      <td>46.82배  l  929원</td>\n",
       "      <td>7.26배  l  5,995원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>삼진엘앤디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>+8.51%</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>4470.0</td>\n",
       "      <td>65461738.0</td>\n",
       "      <td>310756.0</td>\n",
       "      <td>45874433.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A  l  N/A</td>\n",
       "      <td>1.15배  l  3,891원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.29배</td>\n",
       "      <td>-0.54%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SK이노베이션</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>283000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>+7.60%</td>\n",
       "      <td>283000.0</td>\n",
       "      <td>283500.0</td>\n",
       "      <td>3363732.0</td>\n",
       "      <td>950235.0</td>\n",
       "      <td>2014564.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-13.00배  l  -21,785원</td>\n",
       "      <td>1.58배  l  179,631원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.88배</td>\n",
       "      <td>+4.60%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>일진머티리얼즈</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>56400.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>+7.22%</td>\n",
       "      <td>56300.0</td>\n",
       "      <td>56400.0</td>\n",
       "      <td>1661973.0</td>\n",
       "      <td>91832.0</td>\n",
       "      <td>399206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.00배  l  1,119원</td>\n",
       "      <td>4.28배  l  13,170원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.14배</td>\n",
       "      <td>+2.96%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       종목명     종목명.1       현재가      전일비      등락률      매수호가      매도호가  \\\n",
       "0  웰크론한텍 *  테마 편입 사유    4765.0    820.0  +20.79%    4765.0    4770.0   \n",
       "1   이엔드디 *  테마 편입 사유   43500.0   3600.0   +9.02%   43450.0   43500.0   \n",
       "2  삼진엘앤디 *  테마 편입 사유    4465.0    350.0   +8.51%    4465.0    4470.0   \n",
       "3  SK이노베이션  테마 편입 사유  283000.0  20000.0   +7.60%  283000.0  283500.0   \n",
       "4  일진머티리얼즈  테마 편입 사유   56400.0   3800.0   +7.22%   56300.0   56400.0   \n",
       "\n",
       "          거래량      거래대금       전일거래량  ...             추정PERlEPS  \\\n",
       "0  32171066.0  146663.0   6941573.0  ...           N/A  l  N/A   \n",
       "1    999308.0   41912.0    475248.0  ...           N/A  l  N/A   \n",
       "2  65461738.0  310756.0  45874433.0  ...           N/A  l  N/A   \n",
       "3   3363732.0  950235.0   2014564.0  ...  -13.00배  l  -21,785원   \n",
       "4   1661973.0   91832.0    399206.0  ...     50.00배  l  1,119원   \n",
       "\n",
       "    PBRlBPS (2020.09) 배당수익률 동일업종 PER 동일업종 등락률 PERlEPS(2019.12)  \\\n",
       "0    1.45배  l  3,290원   NaN    7.42배   +0.03%              NaN   \n",
       "1                 NaN   NaN   64.44배   +2.24%  46.82배  l  929원   \n",
       "2    1.15배  l  3,891원   NaN   86.29배   -0.54%              NaN   \n",
       "3  1.58배  l  179,631원   NaN  -12.88배   +4.60%              NaN   \n",
       "4   4.28배  l  13,170원   NaN   60.14배   +2.96%              NaN   \n",
       "\n",
       "  PBRlBPS (2019.12) 배당수익률l2020.12 PERlEPS PBRlBPS  \n",
       "0               NaN           NaN     NaN     NaN  \n",
       "1  7.26배  l  5,995원           NaN     NaN     NaN  \n",
       "2               NaN           NaN     NaN     NaN  \n",
       "3               NaN           NaN     NaN     NaN  \n",
       "4               NaN           NaN     NaN     NaN  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 일부 데이터만 가져와서 확인합니다.\n",
    "df_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JAwE_fFBpSVd"
   },
   "source": [
    "## 파일명 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "4_wShqVEpSVd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'theme_no_64_2021-01-09.csv'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 파일 이름을 url의 정보를 이용하여 만듭니다.\n",
    "# 업종_no_번호_날짜.csv, 혹은 테마_no_번호.csv 형태로 만듭니다.\n",
    "# file_name\n",
    "\n",
    "file_name_1 = url.split(\"=\")[1].replace(\"&\", \"_\")\n",
    "file_name_2 = url.split(\"=\")[2]\n",
    "file_name = f\"{file_name_1}_{file_name_2}_{today}.csv\"\n",
    "file_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "bS_wUocUpSVd"
   },
   "outputs": [],
   "source": [
    "# csv 파일로 저장합니다.\n",
    "df_info.to_csv(file_name, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "s-AYqnfKpSVd"
   },
   "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>종목명</th>\n",
       "      <th>종목명.1</th>\n",
       "      <th>현재가</th>\n",
       "      <th>전일비</th>\n",
       "      <th>등락률</th>\n",
       "      <th>매수호가</th>\n",
       "      <th>매도호가</th>\n",
       "      <th>거래량</th>\n",
       "      <th>거래대금</th>\n",
       "      <th>전일거래량</th>\n",
       "      <th>...</th>\n",
       "      <th>추정PERlEPS</th>\n",
       "      <th>PBRlBPS (2020.09)</th>\n",
       "      <th>배당수익률</th>\n",
       "      <th>동일업종 PER</th>\n",
       "      <th>동일업종 등락률</th>\n",
       "      <th>PERlEPS(2019.12)</th>\n",
       "      <th>PBRlBPS (2019.12)</th>\n",
       "      <th>배당수익률l2020.12</th>\n",
       "      <th>PERlEPS</th>\n",
       "      <th>PBRlBPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>웰크론한텍 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>+20.79%</td>\n",
       "      <td>4765.0</td>\n",
       "      <td>4770.0</td>\n",
       "      <td>32171066.0</td>\n",
       "      <td>146663.0</td>\n",
       "      <td>6941573.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A  l  N/A</td>\n",
       "      <td>1.45배  l  3,290원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.42배</td>\n",
       "      <td>+0.03%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>이엔드디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>+9.02%</td>\n",
       "      <td>43450.0</td>\n",
       "      <td>43500.0</td>\n",
       "      <td>999308.0</td>\n",
       "      <td>41912.0</td>\n",
       "      <td>475248.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A  l  N/A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.44배</td>\n",
       "      <td>+2.24%</td>\n",
       "      <td>46.82배  l  929원</td>\n",
       "      <td>7.26배  l  5,995원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>삼진엘앤디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>+8.51%</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>4470.0</td>\n",
       "      <td>65461738.0</td>\n",
       "      <td>310756.0</td>\n",
       "      <td>45874433.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A  l  N/A</td>\n",
       "      <td>1.15배  l  3,891원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.29배</td>\n",
       "      <td>-0.54%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SK이노베이션</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>283000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>+7.60%</td>\n",
       "      <td>283000.0</td>\n",
       "      <td>283500.0</td>\n",
       "      <td>3363732.0</td>\n",
       "      <td>950235.0</td>\n",
       "      <td>2014564.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-13.00배  l  -21,785원</td>\n",
       "      <td>1.58배  l  179,631원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.88배</td>\n",
       "      <td>+4.60%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>일진머티리얼즈</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>56400.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>+7.22%</td>\n",
       "      <td>56300.0</td>\n",
       "      <td>56400.0</td>\n",
       "      <td>1661973.0</td>\n",
       "      <td>91832.0</td>\n",
       "      <td>399206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.00배  l  1,119원</td>\n",
       "      <td>4.28배  l  13,170원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.14배</td>\n",
       "      <td>+2.96%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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",
       "      <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>74</th>\n",
       "      <td>하나기술 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>143400.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>-3.69%</td>\n",
       "      <td>143400.0</td>\n",
       "      <td>143500.0</td>\n",
       "      <td>331289.0</td>\n",
       "      <td>48566.0</td>\n",
       "      <td>380067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A  l  N/A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.14배</td>\n",
       "      <td>+2.96%</td>\n",
       "      <td>91.81배  l  1,562원</td>\n",
       "      <td>20.25배  l  7,082원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>씨아이에스 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>-3.70%</td>\n",
       "      <td>10350.0</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>10248736.0</td>\n",
       "      <td>109844.0</td>\n",
       "      <td>21750003.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A  l  N/A</td>\n",
       "      <td>9.11배  l  1,142원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186.32배</td>\n",
       "      <td>+4.94%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>이아이디</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>311.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-3.72%</td>\n",
       "      <td>311.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>32436621.0</td>\n",
       "      <td>10203.0</td>\n",
       "      <td>36861303.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A  l  N/A</td>\n",
       "      <td>1.45배  l  215원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.88배</td>\n",
       "      <td>+4.60%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>나라엠앤디 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>13150.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>-4.71%</td>\n",
       "      <td>13150.0</td>\n",
       "      <td>13200.0</td>\n",
       "      <td>1655695.0</td>\n",
       "      <td>22094.0</td>\n",
       "      <td>3432023.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A  l  N/A</td>\n",
       "      <td>2.24배  l  5,878원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-31.86배</td>\n",
       "      <td>-0.28%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>신성델타테크 *</td>\n",
       "      <td>테마 편입 사유</td>\n",
       "      <td>10950.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>-6.41%</td>\n",
       "      <td>10950.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>3368126.0</td>\n",
       "      <td>36828.0</td>\n",
       "      <td>5580965.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N/A  l  N/A</td>\n",
       "      <td>2.02배  l  5,415원</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.67배</td>\n",
       "      <td>+0.45%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         종목명     종목명.1       현재가      전일비      등락률      매수호가      매도호가  \\\n",
       "0    웰크론한텍 *  테마 편입 사유    4765.0    820.0  +20.79%    4765.0    4770.0   \n",
       "1     이엔드디 *  테마 편입 사유   43500.0   3600.0   +9.02%   43450.0   43500.0   \n",
       "2    삼진엘앤디 *  테마 편입 사유    4465.0    350.0   +8.51%    4465.0    4470.0   \n",
       "3    SK이노베이션  테마 편입 사유  283000.0  20000.0   +7.60%  283000.0  283500.0   \n",
       "4    일진머티리얼즈  테마 편입 사유   56400.0   3800.0   +7.22%   56300.0   56400.0   \n",
       "..       ...       ...       ...      ...      ...       ...       ...   \n",
       "74    하나기술 *  테마 편입 사유  143400.0   5500.0   -3.69%  143400.0  143500.0   \n",
       "75   씨아이에스 *  테마 편입 사유   10400.0    400.0   -3.70%   10350.0   10400.0   \n",
       "76      이아이디  테마 편입 사유     311.0     12.0   -3.72%     311.0     312.0   \n",
       "77   나라엠앤디 *  테마 편입 사유   13150.0    650.0   -4.71%   13150.0   13200.0   \n",
       "78  신성델타테크 *  테마 편입 사유   10950.0    750.0   -6.41%   10950.0   11000.0   \n",
       "\n",
       "           거래량      거래대금       전일거래량  ...             추정PERlEPS  \\\n",
       "0   32171066.0  146663.0   6941573.0  ...           N/A  l  N/A   \n",
       "1     999308.0   41912.0    475248.0  ...           N/A  l  N/A   \n",
       "2   65461738.0  310756.0  45874433.0  ...           N/A  l  N/A   \n",
       "3    3363732.0  950235.0   2014564.0  ...  -13.00배  l  -21,785원   \n",
       "4    1661973.0   91832.0    399206.0  ...     50.00배  l  1,119원   \n",
       "..         ...       ...         ...  ...                   ...   \n",
       "74    331289.0   48566.0    380067.0  ...           N/A  l  N/A   \n",
       "75  10248736.0  109844.0  21750003.0  ...           N/A  l  N/A   \n",
       "76  32436621.0   10203.0  36861303.0  ...           N/A  l  N/A   \n",
       "77   1655695.0   22094.0   3432023.0  ...           N/A  l  N/A   \n",
       "78   3368126.0   36828.0   5580965.0  ...           N/A  l  N/A   \n",
       "\n",
       "     PBRlBPS (2020.09)  배당수익률  동일업종 PER  동일업종 등락률   PERlEPS(2019.12)  \\\n",
       "0     1.45배  l  3,290원    NaN     7.42배    +0.03%                NaN   \n",
       "1                  NaN    NaN    64.44배    +2.24%    46.82배  l  929원   \n",
       "2     1.15배  l  3,891원    NaN    86.29배    -0.54%                NaN   \n",
       "3   1.58배  l  179,631원    NaN   -12.88배    +4.60%                NaN   \n",
       "4    4.28배  l  13,170원    NaN    60.14배    +2.96%                NaN   \n",
       "..                 ...    ...       ...       ...                ...   \n",
       "74                 NaN    NaN    60.14배    +2.96%  91.81배  l  1,562원   \n",
       "75    9.11배  l  1,142원    NaN   186.32배    +4.94%                NaN   \n",
       "76      1.45배  l  215원    NaN   -12.88배    +4.60%                NaN   \n",
       "77    2.24배  l  5,878원    NaN   -31.86배    -0.28%                NaN   \n",
       "78    2.02배  l  5,415원    NaN    15.67배    +0.45%                NaN   \n",
       "\n",
       "    PBRlBPS (2019.12)  배당수익률l2020.12  PERlEPS  PBRlBPS  \n",
       "0                 NaN            NaN      NaN      NaN  \n",
       "1    7.26배  l  5,995원            NaN      NaN      NaN  \n",
       "2                 NaN            NaN      NaN      NaN  \n",
       "3                 NaN            NaN      NaN      NaN  \n",
       "4                 NaN            NaN      NaN      NaN  \n",
       "..                ...            ...      ...      ...  \n",
       "74  20.25배  l  7,082원            NaN      NaN      NaN  \n",
       "75                NaN            NaN      NaN      NaN  \n",
       "76                NaN            NaN      NaN      NaN  \n",
       "77                NaN            NaN      NaN      NaN  \n",
       "78                NaN            NaN      NaN      NaN  \n",
       "\n",
       "[79 rows x 49 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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