3.1 FinanceDataReader를 통한 개별종목 수집하기-output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JooJ7cGP5HMS"
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/corazzon/finance-data-analysis/blob/main/3.1%20FinanceDataReader%EB%A5%BC%20%ED%86%B5%ED%95%9C%20%EA%B0%9C%EB%B3%84%EC%A2%85%EB%AA%A9%20%EC%88%98%EC%A7%91%ED%95%98%EA%B8%B0-output.ipynb)\n",
    "\n",
    "## 라이브러리 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "0HuA8Gtr5HMT"
   },
   "outputs": [],
   "source": [
    "# 데이터 분석을 위해 pandas 불러오기\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yx1gbnsI5HMQ"
   },
   "source": [
    "* [FinanceData/FinanceDataReader: Financial data reader](https://github.com/FinanceData/FinanceDataReader)\n",
    "* [FinanceDataReader 사용자 안내서 | FinanceData](https://financedata.github.io/posts/finance-data-reader-users-guide.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Szu-zewh5HMR"
   },
   "outputs": [],
   "source": [
    "# colab 을 사용한다면 런타임이 초기화 될때마다 다시 설치해 주어야 합니다.\n",
    "# 로컬 컴퓨터에 설치했다면 이전에 설치된 라이브러리를 사용해 주시면 됩니다.\n",
    "# !pip install -U finance-datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "RmRqR4HT5HMT"
   },
   "outputs": [],
   "source": [
    "# FinanceDataReader 를 fdr 별칭으로 가져오기\n",
    "import FinanceDataReader as fdr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VNyU_0nM7gDr"
   },
   "source": [
    "## 개별 종목의 일별 시세 수집\n",
    "* 종목코드로 수집이 가능합니다.\n",
    "* 종목명을 찾아서 일별 시세를 수집해 주는 함수를 만들어 볼 예정입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "-MWybAcD5HMT"
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>35980</td>\n",
       "      <td>36240</td>\n",
       "      <td>35880</td>\n",
       "      <td>36100</td>\n",
       "      <td>93012</td>\n",
       "      <td>0.001665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>36280</td>\n",
       "      <td>36620</td>\n",
       "      <td>36020</td>\n",
       "      <td>36480</td>\n",
       "      <td>147153</td>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>36500</td>\n",
       "      <td>36520</td>\n",
       "      <td>36100</td>\n",
       "      <td>36160</td>\n",
       "      <td>159435</td>\n",
       "      <td>-0.008772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>36060</td>\n",
       "      <td>36060</td>\n",
       "      <td>35540</td>\n",
       "      <td>35560</td>\n",
       "      <td>219349</td>\n",
       "      <td>-0.016593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>36180</td>\n",
       "      <td>36440</td>\n",
       "      <td>36040</td>\n",
       "      <td>36200</td>\n",
       "      <td>177619</td>\n",
       "      <td>0.017998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Volume    Change\n",
       "Date                                                    \n",
       "2017-01-02  35980  36240  35880  36100   93012  0.001665\n",
       "2017-01-03  36280  36620  36020  36480  147153  0.010526\n",
       "2017-01-04  36500  36520  36100  36160  159435 -0.008772\n",
       "2017-01-05  36060  36060  35540  35560  219349 -0.016593\n",
       "2017-01-06  36180  36440  36040  36200  177619  0.017998"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 삼성전자, 2017년~현재 일별 시세 받아오기\n",
    "# 삼성전자의 종목 번호 '005930'\n",
    "\n",
    "df = fdr.DataReader('005930', \"2017\", \"2021\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fccde426b10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tQpWBoihxp7ouEDZqdit+eRdW01N8fLynjOU7DvHyqt1NUj3EksYxA+4swA2UmzYsn8umNzhP/u6yqeRnhZu/XIU30CblizeqDBRFiTs19UEyPcXgXVdL78zAddm8+C8f8p2nVvDkkh0xlTEQIVHeOROdWNvDVhnUh2oZC0k+4ZTRfTljXD9mjWnqCp9uFV286xi4aNbSHkRNfQB/0JCdpl+7kjj4A0HqAsEwM5E7I0hLaXhQZqeH37fVdbFNXBdpkfeCKYOZt25fKBiu3h9evvLRr81o9nzp9rMlSlrOxFBJSky47O8LOfqOefEWQ1HCcN1KMzwzA1cJpHvMRI0HMbF2x/QHwmcGv710CilJzpPcTaLnKgV3ZiMiNJeE2TWL9WlkPooXqgx6EKuLnIpRb67fF1ZiUFFiSX0gGJZ3yFUG3jUD98E/JD8j1JbayJwSiPEt3FgZpCRJaAbgtyakunYUtnePGdM/vmkoXFQZ9ECufXQp//4o/pWVlJ7JefctYNztc9lfXsPGfeUhc4/XTOR65wzv0+CX/5WTCsPOE+sF5MZmovMnDQw99N0ZgTtDaKy4IrG3tBpIHGWgxuMeyk+eX8P0wnyO6pcYN6LSc3CTtl3y14XsKKni5RtOBpquCQCMH9grtD15SB6D8zLYddh5iFbFfM3AUVB3fe5ovnj8cICQmejSvy3kyzOH89ii7UC4yas5rj1lJGXVfi6fMbTVvrFAlUEPYljvTHaUNNSZXbmzlNSkJIZ5Rl+KEivce3HFTieqN8ezJnDtKSPpk53GRVPDs9n3zkoNKYNYB6C5yiDF1zDqT/bMAFxFAG1bB+jfK51fXzK5CyXsHGom6kGkJfs4ql82X7XT7RufXcWpv3mnSdENRYklb33sJDzO8iiDjNQkrjx+WCj62KVvdsNDNubKwJqCkpMaZGrOHNQdK/eqMuhB1AeCTBjYizs+OzGsfVWRKgMldoxrlLd//qdO+uq+OWmtHpvpURiHq5rWO+gI727Yz2MLt0UspuPFjSFI8iiowBHkiKHKoAdR5w9GzJv+0xfXxUEapadSF2HhNzXJx6A2ROJ63UkPd8HMoNYf4Cv//IjbX1zHrS+sabZf0aEqfvqik4PIm0jPF2EC8Kcrj+m0XPFAlUEPoi5gQt4P91/RcMOO6JsVL5GUHkikwvZzv39Km0wr3vQUXWEmKvPkOtpSHJ6BdPfhat7d4JiwfvjMKj7cfBAgTGlNHpLHg1dN5+cXNMy2J3gWvbsTuoB8BPPge1tYWXSYs8b356JjBlPjSQZ2wZRBzBpTwIV/ej+UP15Rok1ZTT1Fh6qZUdibJdtKeOmGk+ibncagvIzWD6bBewegtKrzyqC8puEcXtdWgLP/8B4VtX6W334WOw42OF4MbCTrWRP6h5QGJEbVso6gyuAIpbymnrte+xiAV1fv4cKpg6is85Od1nDD52akMDg/g4raxMgEqRz5/H3+ZsDJ0vnMN09o9/F7bH4iEaf4zfrdZUwY1PGR+P7yhuL1vRt5ALm/i5m/eivMtBUpnYtXmbUlxiAR6Z5SK63iltlzufOV9RgT7rEBzo3tHR0pSjQ5bEfz3ztzdIeO32aLybjrtv9YsKVT8qy3qaj75aQ1OyiKtMbRGG/m0fQ2xBgkIqoMjlBqG9ll//nBNqCpMshJT6GiRmcGSmyorg8wOC+jw5k6z500EICrT3CCvkYWdG69y40q7t8rvVVvIgj3JPLirV3QKz3+hWo6gpqJjjCCQYPPJxEX6cAZAXlxZgaqDJSuparOz5KtJcwaUxC2MFxTH2hTdG5zfH/2aL41axSl1fU8snA7vbNad0dtCdddNDM1KWwGECkj6vdmj+aSY4c0e65Ljh3C0Pzuu/6mM4MjiL++u5mRt75GTX2AWn/kUU7jPCi90pOpqPOHcsEoSlfw7NIivvLPj7j1hbVh7TX1wSYLte3B5xMyUpNCi7R1zdznbcWtUZCZmhQ2gNrrqWXs8v0zR4fVMG7Mby+d0mHzVyKgyuAI4tdzPwEcj43mZgaNb+bMtGSMgRUahax0AfvLarj64SXstKkmnmpUgKa6LtApZeDiKoPG5tD24g8EEXEyprq/mRdX7uL7/14Z1u/nF0zsllHF7UGVwRGCd2Rf5w82+yNpbPMsqXSiOL/80OLoCdcCr6zezQ1PLufeNz+Ny/WVruVfH25j/qfF/OP9raE2771ZXR8IK1jTUdJCM4NOKoOgIdknpCb7qAsE2Xagku89vbJJipbJQ3I7dZ3ugCqDI4SP9zYU6K7zB0M2zx+cOYb8zOYXtNzBTqwzQK7fXcYP/r2SBxds5ZXVe7j3zY1hpq33Nx5g+8HKFs6gJCLpEUb9NZ7vtaa+a2YGyT5BpG2ePi0RCBqSfT5Sk3zsPlzNab99N2K/iYNUGSjdhPve3BjargsEeXvDftJTfHzt5EKW335Ws8dNGuzc5I0XlqPN9U8s44UVu/hkT4MSG3vb3FBe+C89tJhZv3k3pjIpnac6gkeOd6DR2QVkFxEhNcnX6ZlBfaBhZlAfoVrOeZMGcP7kgd02kKw9HPmfsIfw+vp9oe3SqnpeXrmb8yYNJCc9JWTrPHN8/ybHfWbyIFKShOMKewNEdK87UFEbsRh4Z/BZmRqbs6pqw6+/05NyW0l8vJG6LnPX7mWTrWFQ3UUzA3DWDTq7ZhAIBklKkogP+8lDcvnLF4/lz1dO69Q1uguqDI4AGj+oX1y1m/JaP5dNbyiaseqOs/nrlyLf1KP75VDrD3DtI0sZd/tcXlq1O7SvuLyW6b98k/PuW9ClMje3FlfrD4TZmIsOVXfpdZXoss1j2ps1pgCA2/67ljN/Px9wFpAjmZI6QloXKIN6ayaKFFX8/LdO7NS5uxuqDI4AymzCrilD8wB4cvEOCvtkcvyI3qE+uRkpzQb6pKX4qKkP8ubHzuzi3U8a8qy4BUg27Ctv1+wgGDTc8vxqHn5/K2f87l32N3LV83m0wViPu2t1fYDFW0tC7zVVRvfBGMN2z8wgr9FaVSBoqKoLkJXWVcogqdNmooA1E+VmNF1XS+6maSU6Ss/6tEcoJTav+1nj+4XaLp0+tM2ucKlJvjC/6udX7OLiv3wAwBUPLAq1Nxe7EIntJVU8tWQnd76yni3Flby2Zk/Yfm8k9Ozx/fjZZycATjptb4qBilpNldFdOFBRR0WtP1SvoG92wzrUUf2y2VNajT9oGNJFgVmuB1AkjDG8v/EAppV6A/6gIckn9GqkDH5y7rgukbE7ocogAdlTWs1f393c5pH4IeseOnlIXqitpUjJxqSlJIVsui7Ldziudd4fW029s13rD3DXq+vZfbh5E07jCM7M1PBpeKGn1KbXhjz/0+KwFAM/+Pcqpv/yjbZ+FCUO7Cmt5oYnl3PcXW8CcPOccbxw/YmcN2lAqE9GShKf7isHYFRBdpdc11lAjjxA+d/avXzpocU8vnhHxP0u+8tryElPbjIz8M6qewqqDBKQu179mF/P/YTHPTVVW+KQTf6Vn5nKvV+Yyj0XT6J/r9YLhbg091B/ulHAkDszeGLRDh5csJVHPtzW7Dkbj9gyG5kGvF4nyUk+qjzvs9PCf5gHKuo6bQ5QosdjC7fzyuqGmd/g/AyOGZYf5p1TXlPPul2O51hnsox6SU1u3pvIzbe1YsehZo+vqPWzeEsJp4zuG1IG2WnJbLzrXI4Zlt8lMnYnVBkkGP5AMPTD+su7m9p0jDszyM9K4aJjBnP5jGHtuubAZipMPfDeFvrlpIXcTt2Zwbx1ewHo14LCafwjTWpksnLPBc5i8kmj+obel9fUk9woOG5HicYcJCILNx/kmaVFYW159sF6XGFvvnHqSM4c35+KWj9rd5cyom9WxMXajpCW7Au7j7zk23TU+8tqI+4HWPBpMXWBIGeO7x9SBlV1/g4n0evutOlTi8g2EVkjIitFZKlt6y0ib4jIRvs337aLiNwvIptEZLWITPOc52rbf6OIXO1pP9aef5M99siO+24Bd6oNzoi4NZsnNKwZNM7H3lZ++pkJoe1RHhPNoao6ThzVJ1TFyZ0ZFFc4P7CWRuvuvpG2ilrjmcLWAw0Pd8FZ/P7xOWMB2Fde2+SzbI/gsqjEnyseXMSBivAHrmt/T/IJt5w3npEFWRyoqGPeun1M7KJZAUB2ejKVdZEdDFwT674IOYbAWVNYsq2EjJQkjh2eH1IGPTlFV3tU4OnGmKnGmOn2/U+At4wxo4G37HuAc4HR9nUd8FdwlAdwB3A8MAO4w1Ugts/XPcfN6fAn6ua4Jp+b5owlEDQRg3gas7roMH2yUjvsvz26fw7HFTpfxS8uPJqb5jgP5dLqejJSk0LpA9xRmLse0JJsdQFn34/Ods71vadXho7bX14T8lJKTfIxw9pn3bxJ63eXNlnQK9OaCwnPxdMGMyQ/o4nraL6nZvDofjmND+swvdJTms2466am3ldWw5biCio9Xmm/eGU9I255ja0HKhlZkEVyki+iN1FPozPzoQuBR+z2I8BFnvZHjcMiIE9EBgLnAG8YY0qMMYeAN4A5dl8vY8wi4wyDH/Wcq0fhjma+f+Zo8jKcH9DBirpWj9u8v5KjB+d2KpFWgTUF5Wel0temBQ4aJ71Auq07e9VDizn9t++GCpS892lxs+dzZwZeN8INdgHx3Q0Nx2345ZyQfXZIvlMtanNxJTnp4aaEB9/b2qZZkhI7vAGK4wbk8LtLpzD/x6c36Xfl8Q1my8bupp2hV0Zys3WQ3Uj2sho/Z/xuPlc+2OAV95DNm7Rw88HQ2lpmNy1I05W0VRkY4HURWSYi19m2/sYYd9VoL+CGtw4GdnqOLbJtLbUXRWhvgohcJyJLRWRpcXHzD6LuihsvkJuRwlQbM+BdmIvEK6t3s2FfeadHznd/bjK/v2wK4wf2IiW5QalkpDTMDMpq/Gw9UBmaEazcebjZB/SNz64GCHuoH7TmhA82HQDgsWtmhCmwPM/orLE5Yf2eMjY28nhS4suGveWh7bs+dzQiErH4S25GCieM7AM4D/Cuol9OOoeq6sKUUjBoWLXzMPX+8PtyVVFpaNt1fa31B+mb7Qy6RIS+2al894yjuky+7kZblcHJxphpOCagb4vIqd6ddkQf9WGbMeYBY8x0Y8z0goKCaF8u5hy2yiAvM4UJg3qRkZLUrM3T5b8rdgEwo7BzrnC5mSlcPM1xR/XORjJSkkhLbn7UFCmfS0Wtn4paP32z05g6NJ/Z45z4BzdHTXF5LdOH53PK6PDv0Fst6tyjBzY5764WXFmV6LNxXzkzf/UWRYccE986WzJywU2nc+zwlu+/YdYE2FWpKABG9M3CmPD1p7+9t5kL//xBWOCiS2lVw2DLpcCTk2vpbWfxQ2vW7Im0SRkYY3bZv/uBF3Bs/vusiQf71w1b3QUM9Rw+xLa11D4kQnuPo7Q6/GbNSktqNStjcXktY/pnc/OcrguSqfTkB8pITYqYPsBdY4gkX7EtMn7reeNI8gk/v9BZgL7x2VUs2VrCh5sPRrTRemcRx4/ozcNfmc5r3z2FD39yBgB7DresGJXoUV5Tz1l/eI+9ZTVc+8hS/IEg6/eUkpOWHDLvtcRVJw7noqmDmgwAOoMbj7KluEEZrN7pzAD+s7yoSf+z751PaXV9WPS7NzCup9OqMhCRLBHJcbeBs4G1wEuA6xF0NfCi3X4JuMp6Fc0ESq05aR5wtojk24Xjs4F5dl+ZiMy0XkRXec7VozhkvYLcB2Vqko8nF+/g6oeXROxvjOHjPeWcOroAXzO1WTuCN1goPSUplDvei2vnr/cHufm51XzjsaWhfa4y6Jfj2GPd0WCtP8hlf18IQCCCeSk9JYlvnz6KF799EslJPs4Y158Jg3rRLycNnzjBTUp8WLylYaT9yd5ybnx2FY8v2sHwvpltWquaOCiXey8/pkkN7s4woq+rDBrMh8FG99Vt549nwU3OOsa+slqeWBweuzM4r3VF1lNoyzfTH3jBfuHJwJPGmLki8hHwjIhcA2wHLrP9XwPOAzYBVcBXAYwxJSLyC+Aj2+9OY4x7h10P/AvIAP5nXz2OzdYmPryPc5O7mRTnN7NQW+sPUhcIhnyqu4rR/XM4fWwB72woJj0lKap5zccAACAASURBVOLiWoEdUdUFgvx7qbMUtPtwNYPyMkLKwJ2CN44+BkImqcb8+JymM5zkJB/9e6WzW2cGcaOx59h/VzrJDNfuKovUPSZkpibTOyuVvWU1/OuDrZw5oX9YuuzUZB/XnjIybF3rP8uKwpTX+IFd5+ra3WlVGRhjtgBTIrQfBGZHaDfAt5s518PAwxHalwJHt0HeIw5jDFsPVDKibxbrd5fRv1daaOraWvCLm8StsedNV+B6WVTX+cnLbKps3NlLnT8YigR9bNF2bp4zLhT3kJ/l9MlITeIbs0by9/lOzqG+2WlcMGVQu+QZmJveqZnB8h2HeGjBVu67fGqPS0DWFTTnRuzGkcSL9GSfHfHv4PdvfMrxdqEaYKpNz+J9+G+2JqWRfbO47/JjWqxp3NPQX0UcWbK1hC88sIgzfjefxVtLeH7FLiZ4RiqtZex0faezIoy8O8swmzuoojYQ0UPE9TCqCwRDs4TFWw4CUGsfHN61hlvOHR/a/u2lk9stz8C8DPaWdnxm8KNnVvHqmj1hi41K23G/01saJXB77Nrj4yFOiLSUpNDvoKzGT30gGHJfnV7YkFJi3vdPDSVDBPjqySOY1ANKWbaHrn+KKG3GtZ8DXG6zg5Z5gmj2tPLwcwNuutIO6/K1k0ZQXuPnqhOGA/DXL06jvNbP8SN6s6OkKnTtP77VUK7SVV5ujvlIaw0AUzwJ9drKoNx03vp4H8Gg4fIHF/HNWSM5Y1zTYj3N0Scrla0HKtl6oJLR/bsu8Kmn4AYcfnHmcF5fv49l252cPwVxXoBNS/aFuVW/u6GYU0b35VefmxSWZmXsgBzyM1P42cvrARjdr2uS5R1J6MwgwfjGqSND2951ucKfvBoKpCmtqscYExoRdVWuFy/pKUncPGdcSNGcO2kgl00fyvA+WZwyuiDkkfHflbuptQ8KNzmYqwxSW6if0F4G5mZQUx9k56Eqlmwt4btPrWzX8a7p7acvrgtrr/MHYx7MZowJK+DTHXDNROnJvpAiAOJeDjItJalJjE1BThpDe2c2MQf2zXYcEaDrkuUdSagySDDOmtAw2p1/4+k8f31DtaXyGj8fbSthyp2vM3ft3lBelq4qFtIeTjyqwTbryrG7tIY7XlzLp3vLSUv2Netl0pySaAl3lLfaBg95T72/rIbTfvNOkzTcXlzvJbdugz8Q5IfPrGTSz+Zx+4tr2y1PZ7j+ieWMvPU1ymrqeXThNvxtKOpe6w+0qV9b8QeCrN1V2npHS3lNPekpPpKTfKFc/09fN7PL5Okoacm+UPyAS0Ez9bx9PmHL3eez7Z7z6ZWu6Scao8ogSlTV+Tlc1XIqCdc/+3/fOyXU5n2ADuuTybRh+dxz8STAcdl81UYkv7fxABU2HiAaM4PW6JWewm3nO+sAQUMos+kjC7czd93eZk1E0LEKUgOtC+B3nloBhFdKm7duL9sOVvHAe5ubPb6sUdqC5TsO8/zyXdT6gzFPgve/tU7W1wv++D4/fXEdK3cebvWYaXe+wTcfX96h6/kDwSb1JW54cgWf+eP73P2/j1s81hjDnS+v58EFW0Omom/OGsW2e85npmexNl44ZqLwtbV4m666K6oMosQ5977H1DtbLsqSmZrEnIkDWnVvy7Cund96Yhn/sjUE6gNBSq2yyYnTKMe7QHz04PDFuLQujDQFZ83Ai3dR2/W6emZpEf/8YCt1/iC3PL86bOTrzWFT6w+EUmP0zU6LW2nNbVYJNbc2tGLHIWb95h3+/M4mKusCobKk7eXm/6xh/E/nhmYWc9fuZa5NQ+56eDXHp/sqePiDrR26biyIFBDZUmp1pXl0ATlK7Cxp3Q2y1h8k3drP37/59GbNKu4Nv83jCbNpfwXGOC6e/XvFZyTkHf0Pa+SiF2lm8PINJ7O8hWIjLdE4UtS189fUB9ji+b/8/OX1bNpfwVNLduIPGH5z6RSMMewvb0izXF7jp9KOlPv3SgvLaBkPXlm9m896XG0/2lbCpMG5vLOhmO0Hq/jNvA0AoTw67cWNxn13QzFnTujPNx9fFrb/QEVts5G4b6x3lMYt546LiqNCZ4l0nx2t6wEdQmcGUcC7IPn71zdEDBozxnC4qp5sGyMwJD+z2WhIVxmkJvs4dng+XzmxkJU7D/Of5UWkJDVvm4823lHZmP45vPGDUzl/spNTKNKPdNKQXK4+sbBD1/L5hDPG9eML052MJoeq6tlTWs242+fywHvho9snbKlDN1ai6FA1JZV1HDvccTUsr/GHFMDA3HQ+3VfBxn3lRJMH3tvMhJ/OjWinT/Xkfiour+XSvy3kR8+sarKwHSkPVFuYbF0o3990IKJJatb/e6fZY9/4eD9ThubxjVmj+NLM4R26fjSJlDerT5aaiTqCKoMocLCyYa3g/rc3cfXDS3h93V4+2VtGrT/AY4u28+Hmg5RW17epHmy2XSCuqQ+Sn5nKGI9rZOPCIrHE+8BPT/Exun9OyJyT2kJyu47y8FeO49eXTOZuu4Zywt1vt9i/1h+grKaeU+zDzl2cr6jxhxa9vzt7NABPf7Qz8km6iF+99glVdQE+88f3w9pHFWSFZd10U5K8/cl+DlbW0ccTXV5TH6C6LsDXH13K5uK2Z3B1TWSLthzk5udWN9lfWRc5oKzWH2B10WFOHd034v5EID2CZ1o8HCqOBBJv3ncEsC1CYNN1jy2L0BOOaoO/89D8BhNM76wUZo0toLBPJtsOVnHm+Lb72nc13ipT7ghtQK4zu4mm6WVEM1Gvn582hDW7DvPpPudBWVMfZNchx1x30dRBobTgH20r4b43N5Ka7GPS4FwK+2SGmZFiSXZ6SsgVFxpKmFbXO+savbNSeek7J/PQgq08/MFWlmwr4Y31+3hj/T623XN+q+cPBg0lNgvtJ56U073Sk3nvptOZeucbzfrcH6yowxgYlMD5eyLNDDTCvGOoMogC2xp5p4hAc67sban81Mdjz83PSmVwXgbv/vh06vzBJrWCY8mEgQ2Lxu4IbfxA5/O4lcyiQSRlcPfFk7hixjDO+N27obaa+kBoVHzZ9KGhtB13vuIEHqVa99eCnDT2t5IqvLMMzsvg+JG9eX65k5D32pNHcNrYftz/9sZQdC+ER50frKijT7bzfbvukvs8i83VdYGQc0EkDlfVRXRiuO388XzlxEKSk3wcPbhXKKFgY0qsYurTxbmvuhJvzMqUoXmsaoNnlhIZVaFRwJtF0deCIgDatPjr9Zzp7ckTlJrs69Jspe1l7IAGRebmeJk4KPoh/v0a+ZFPGZrHFTOcalpXn1AIwIBe6VTU+kNV2XplpDTxLXersfXLSae4vJaXV+1utnJWZ4PESqvrw64/pn8OJ4/uS3pKUtjMwLu9dPuhkP3bXTy+6T8NZp7WTITvbNgfsb1vdlpo9JyZktzsLC6aQY3R4J6LJ7VptqRERpVBFPh0XwWj+2Wz6JbZ3HhO88UyHvnajHYv/nZ1htKuYrjNZeQmsIvmmraI8IInGO/przcEP119YiHb7jmfE0b14X9r9/Lowm2AUzCouYdaQU4aWw5U8p2nVvCntzc22b92Vykjb32NhZsPdkjespp6Kmr9DMprGIG7njlpyT7Ka+pDi8V1/vDAsj5WCbh1or18uPlAi9fduK8Cn8DiW2fzyndODrV7PYcK+2ayfndZxIC2GjetSAcixmPFHk/Bo0g5tJS2k7jfcjelpj7Aml2HGTMghwG56RHNOLedP561Pz+HWWPaX+hjQIL6UHttt6//4FTei1ALtytxYwtmjSmIaCr59umjAPjQPsBzM1KaZHd9+QbnAdnPMzuLlG57rg0SW7CxY6VW59njxw5ocHl0r3lcYT6biyt53HpAuXmeXFyX3eF9svjHVdND7RMH9eLv87dETKVxoKKWW19Yw5biSnLSU+jfKz0sDqRvTsOA4oRRfSiv9YeyeXpxzVctVbqLN5keBe+Lk1fdkYIqgy7mN/M2sK+sNuQC6Ra297qNXnvKyA5PvRMh6rM1xvTPiXpq4ImDevF/543n/suPibj/qH45oWC+JJ+QnZZMcpKPn18wMdTHzVo5KLfhu4n0vbjeYR0tmv7csiLG9M/m1NF9+czkgXx2yiCmWzfXa08eyeh+2fzzg61M+OlcFm0JL9d44dSGcuCFdq1k8pBcPnfMYLYcqIxo1nr4/a08uXgHc9ftjfh5vK6X7pqVm8qjpj7AIpt91p0ZRPLYSRS8j3+dGXSO7mEM7EYs33GIE0b24VQ76r942mCmDc9jVEE29721MeTR0l5+c8lkcjNS4p4YrDFv/2hWXIKRRISve5L6RWJwXjof7ykjNyMlZI67+sRC7ngpPFndnKMH8LeUaXzz8eURy3jutIvh7208wA1njG63rJV1fobmOxXB/nTltLB9Pp9wsLIutFj7gq1p/dTXZ1JSWReWZ+eoftn8+vOTmF7YOxSvcKCitkm9iRU7GhZRvbOh3IwUSqvr6e0xNTaUjnSUwa0vrOH55bt484ezQi6viTwz8JKkM4NOocqgiymv8TOwf4MpJznJx1F29PX9M8d0+LyXTh/aeqc4MLINcRLxorBP2wqvpKckcfYEp9Tnb+Zt4PV1e/nvt09i4/4KPv+XDym3C6lLtpYQCJp2j0Br64Mt2t1LKpvmsJo2PC/iQ/gLxzkL5W496JLK8JlBWU19WFbRfI+iePmGk1m7uzRM/szUZAbmpofqPLiJAM/8/fxQn0ReM/CSlKTKoDN0j2+5G1FeU09OmmZETAQ+f6xTWrPxw/bLM4dz9Qnh0bRer6xVRaUs2VrC6qLSkCJw6cgicq0/2OLo+gcRBgmtZXZ1H9C1ficQ7fonlrGluIIfP7sqbHbjXQ8Z1ieT8yYNbHKukQVZbD5QyWMLt0XM/BqN4knRQGcGnaN7fMvdiPIaf1TKUCrtZ/zAXvy/z09uMrL9xUWtV1h9cMFWPjul4cG56qdnM/v383l80XZObmdEbq0/0GIW1+/OPoo/vPlp6P20YXmtepm556vzB3nz4328tsZZpJ63zklmN3FQL9btLuNHZzXvzeYysm82jy3a3qyPfkfXSmKBd/ncp0PbTqFPrS7EHwhSVReIWxZRpSmXHdd+89rIvlks3HyAcyY60d3v3HgauZkpTB6SS9HhKgJBw+qiw+RmpLTJTObMDJp/UokIy247k/mfFnNcYe9Q2caWcNeOav1B9tmAudyMBpPQK985GWNoUxyKu27gcueFE8OKAMUr91V70ZlB51Bd2oVEs0C9Eju+cNxQKusCoTxB7sg4NyOFkoo65tz7Hp/7y4ec8bv5LZ0mhLNm0PLouk92GhdPG8LQ3pltGky4Zqea+gDFNpWG6wr643PGIiJtDkj0RnSnJvm46oRC3v7RLEb2zUro6GMID+hUb6LOoU+tLsSNdlVl0D35/WVTGFmQzfaDzmKqW/TGtd/nZqSwu5W61I3ZXFxBdX2A9C72AnNnGj98ZhVXHu8sKi/Z5rilHjOsfR5rAzy1Im6a45iVRhZk8/aNp3WBpNHl+BG9eWqJE6MRz2j8IwGdGbQDYwzPLSviz+9sCmsvra6npj7AqiLH5prIHjZK81w8bQhTh+aFAvvc/EquSaYt5pvGzLazh652v/W6GD9pA9aKbFK+IXnti/FwK4PNmTiAa09p2V030bjomIY4DA066xw6hG0H9765kfvectIVfPWkQjJsXpkpP3+dSYNzQ/bpob0TN8uj0jquH/6CjU66B/fBW1bdvkys3uR3kSpydYbGi7reZIgD89oXpd4nO41nv3kCE1qpuJeo3H3xJG59YU2L6zJK6+h/rx08t6wotP32J/v5zB/fZ8ZdbwKwZldpyGbb1T98JbY0dgN1U4okt9OP3fXZB5pNgNdRMlOTWXXH2Zw4yolI9wYzpnQghfNxhb0TspJZW7hixjC23n1+hz630kD3/PbjRGHfTHbZxFg3PLmiyf5im0UyQ5VBt8abfuG8SQNC3jTfmz2awXkZvLpmD0u2ljR3eAjXzJSe4uPcowd0uZy5nkysWt1L6SyqDNrBgfKmkaJenl1aRJJPdITSzfF6/swobMgWmpWWzNUnFnLupAF8+R9L2LCvnJr6QLMzwR0lVWSlJrH25+dEzT3TPe3Rg3sxc2RvRvdvvT6GokRCn1pt5HBVHRv2lfOlmcPC2gty0rh5zjgG5aZTUlmns4IjAO/MIFLK8H456XzR3gflNc2vI+wsqWJo78yo+ukftFXMxg/sxbWnjOxQJlxFAVUGbcb11Dh+RB9uPW9cqP3NH87iW6eNCo3I2lKsRklsvKkgTh0d+eHqug/vKGma+tll56GqUArqaOEqpWM6mABRUVzUTNRG3BiC/r3S+eyUQVxz8kiMMaGKUa6L8zkTu942rMQWEWHOxAEc1S+72WJC7iLz5/+6sNnqWoer6skfGt2grQunDuaCKYO6TZSwkri0eWYgIkkiskJEXrHvR4jIYhHZJCL/FpFU255m32+y+ws957jFtm8QkXM87XNs2yYR+UnXfbyuY0+pMzPIt77mST4JK7ztFlQ/+aj25a1REpO/ffnYFqvUeWsV7/JU23J56+N97C+vjYmHjioCpStoj5noe8DHnve/Bv5gjDkKOARcY9uvAQ7Z9j/YfojIBOByYCIwB/iLVTBJwJ+Bc4EJwBW2b8KwcV85//fCWgbmpjcbUOauFTTOLa8cmczxeAd96R+Lm+y/5pGlsRRHUTpNm5SBiAwBzgf+Yd8LcAbwnO3yCHCR3b7Qvsfun237Xwg8bYypNcZsBTYBM+xrkzFmizGmDnja9o0Zn+wto6ymeT/wn764jrpAkC/NHN5s/pM/fGEq1582inED1JujJ9ArPYXnvnkC0LRusZfDVS17oClKotDWmcG9wE2Ae9f3AQ4bY9y5chHgxoUPBnYC2P2ltn+ovdExzbU3QUSuE5GlIrK0uLhj9WgbY4xhzr0LOP6utyLWk/3vil0s3HKQ608bxbdPP6rZ8wztnclNc8ZpfpQexPTC3pwyui99c5o6DbgpLX48p/UU0oqSCLSqDETkM8B+Y8yyGMjTIsaYB4wx040x0wsKusaFzk0xUF0fYMQtr3HDk8vD9n//3ysBuPy4YU2OVZTstGQqGxXAKamsY29ZDbecO46BuZqaROketGVmcBJwgYhswzHhnAHcB+SJiLs6NgTYZbd3AUMB7P5c4KC3vdExzbXHhOKK8CyUr6zeA8C63aWccPdb5GWm0C8njWF9ousiqHRPstKS2bS/gk/2loXaymzqiYIIMwZFSVRaVQbGmFuMMUOMMYU4C8BvG2O+CLwDXGK7XQ28aLdfsu+x+982jv3lJeBy6200AhgNLAE+AkZb76RUe42XuuTTtYHK2kCTtvpAkGeXFrGntIbDVfUc54lCVRQvFTbobM69C0JrBzV+zVGldD864/d2M/C0iPwSWAE8ZNsfAh4TkU1ACc7DHWPMOhF5BlgP+IFvG2MCACJyAzAPSAIeNsasI8oc/6s3OX1sPy6c6ixPPHjVdD7dV85v5m3gYEVdyJUUGrJYKkpjLpw6iLnrnJKTY277H8eP6M1im7covZsUklcUaKcyMMa8C7xrt7fgeAI17lMDXNrM8XcBd0Vofw14rT2ydJZ9ZbU8/dFOzralDQty0gjaBeRVRYfZUtwQWdonW5WBEplzJw3k5RtO5rN/eh8gpAhA/f+V7kWPHLoEgw1eQ1V1zpQ+MzWJMf1zEIE/vPEpWw40KIN+Oe3LD6/0LCYNyeWj/zuzSXt1XVMTpKIkKj1SGZR7vD9WF5WS7BP65aQxom8WPz5nLJ/sLSfgURiNC4YrSmMKctL43aVTONWTKC7aeYkUpSvpkcqgtKohwGzVzsNMGpIbihw+f9LA0L63fzSLf371uLA0xorSHJ8/dghnTXDMjjNH9ubowblxlkhR2k7PVAaeqlOLt5YwypNiYnCe4xc+cVAvRhZkc/rYfhpIprQZ904ZN6B7lpBUei49Mmvpml2lYe+9j/rkJB8LbjpdfcSVDnHRMYP5aFsJXztpRLxFUZR2ocoA+HGj7JRD1dardJDstGTuu/yYeIuhKO2mR5qJNhdXkONJLdyvl3oLKYrSs+lxM4OfvbSOJVtL+Py0IZRW1/O1kwrjLZKiKErc6XHK4F8fbgNg7IBsrjt1VHyFURRFSRB6lJnIGzvQX01DiqIoIXqUMkjyCaeNdYKCfJoqQFEUJUSPUgYAn5k8CICxWpFMURQlRI9bM7jk2CGcNb4/ubawvaIoitIDZwaAKgJFUZRG9EhloCiKooSjykBRFEVRZaAoiqKoMlAURVFQZaAoiqKgykBRFEUBxBjTeq8ERESKge0dPLwvcKALxekKElEml0SVTeVqHypX+0hUuaDjsg03xhRE2tFtlUFnEJGlxpjp8ZbDSyLK5JKosqlc7UPlah+JKhdERzY1EymKoiiqDBRFUZSeqwweiLcAEUhEmVwSVTaVq32oXO0jUeWCKMjWI9cMFEVRlHB66sxAURRF8aDKQFEURVFloCiKoqgyiCkicqWITLHbCVd3U0QS6n4QkQtEZFS85egu6P3VfvQeayDhvpzOICIXicgv4i1HY0TkTBFZANwLHANgEmTl3v4YfhhvObzY/9dC4CFgYLzl8ZKI95jeX+0nUe+xeN5f3b7spR0B+YCvAj8BhovI68aYBQkgVzrwCNAP+CVwIZBp9ycZYwJxlC8Z+BHwLWCYiLxtjFkZL7ns/ysLeArIAW4Dvg8MB94XEZ8xJhhruTyyJdQ9pvdXh2RKyHssUe6vbj8zMA4BYBPOqOh6IO4jNytXNfCEMeY0Y8w84EPgy3Z/3H6o9vp+YAMwDvgh8Pd4ymX/XxXA4/b/9RYwD+cBR7wUgUe2hLrH9P7qkEwJeY8lyv3VbZWBiHxXRB4UkWtt03xjTLkx5kEgS0Susf1i+hk9cn0dwBjzom1PArYC60RkaCxlaiTbPSJymW161RhTY4y5F+gnIlfafjErEu2R6VIAY8y/bbsPOATsFJG0WMkTQbaEusf0/uqUXAl1jyXc/WWM6XYv4CvAImAOMB+4BRjl2X8usA7Ij7NctwIjPfsnAR8BOTGWS4AfAB8AlwAfW1n7efp8DtiVADIVePqcCHyi95jeX0faPZaI91d3nRnMBn5tjJmLY5dMB77o7jTG/A/ni79ORHLcEUEc5EoFvuSRaw1QA1weI3nc6xrgdOA2Y8xzOD+QycA5nj4vAJ+KyI3gLLDFQaYpOD8Ot8+HQJGIXBBNWZohEe8xvb86L1ei3GMJd391K2XgmS6tAD4DYIxZCiwEBovISZ7uNwN3AxuBAXGSa5GV62TbT3BslOnRcv1rfF6PbEuBU6xsc3H+LxNFZKyn+7eA/ycie4HBcZDpUyvTONuvF/AJUN9VsrRB1oS7xxLp/mpBtrjdX+2UK673WCLeXy4JrQxE5CTx+ACbhgWeDwCfiJxq368F9gCD7HFHAX8B/gtMM8b8MY5y7ca6rtmRSj+g0m5HgwzvG49sm4AcEZlk388HcnG8KhCRqcCDwH9w/mePxFGmbNuvDBgC9O9CWcKwtvaQwkqEe6ydMsX0/mpBtnjeXx2RK5b3WOg5mwj3V3MkpDIQkWki8jrwNs4X57a78m7Esad9QRxXtSKcL7PQ7i8FbjDGXGyM2R1nuQZ45AK40RjzcFfJ5JFhpoj8B/iziJzt+XG47sNLAD9wtogkG2PW44zO3AIZB4HrjTGXdtX/rAtkArjcGPOvrpCnkWwniMiDwA9EJMd9eHpki/k91kGZYnV/nSQijwC3iUhvj2zuYnDM768ukguicI+JyAwR+S6EeynF+xnWEgmlDEQkRUT+jpOe9X6cKe9pdl+S559aDiwA0oDf2i8+H+eGwxhTbIzZmGhyWdnqukouj3yn4Ywinsdx5/sSkC+O37TfXncTzpR5FI4vM0AttnSoMWantTkngkzb3PMYY2q6SiaPbLOAP+Eo9UHArSJytr2e33aL9T3WaZls32jcXyNxvst3cHzyfyEi59nr1du/Mb2/ukCube55uvoeE5HvAy/gKKhzbVuSvVZcnmFtIaGUAc4/5j3gFGPMKzgPkvFWowcAROTnwJM4mvN2nH/gAvu+S6ee3UAul8nAR8aYJ4DHgRSgwr3xROSXIvIQsAxHmc0QkWVACY5iSzSZXo+STC7HAh8YY57CCdbqD1whIv1d2Yj9d5mIMrnMAD62o+cbgZXAZ0VkoCtbHO6vzsoVzXtsK856wLewCsh44ivi/KxoHhNjl6rGL2AmMMad3TXadw3wN3cfzgPmScJdsHxEwZUuUeVqLJt9PxXnBr8D2Ae8CzwMfAHHde5J4ChP/2wg70iXqQXZzgceBQbZ9/cDTwDfAMbE4rtMRJk85/4scAMw074fiWPjHmbfTwDuwfHOOTlW32U3kivJvtKB14Dver6zSbH8Ltv1OeJ2YcgDXsWZLt0GZNl2AXx2+yj7IMl393n/gT1JrmZky/bsm4HzsP28fX8NzoLdlGjKlogytSYbMBrHJPM6zoLmC8CPceztRFO2RJTJc+6BwMs4o9TbcVwbz7H7fgv8yG4n4UQ63wHkRlu2biqXWzhsNrAK6Bvh+Kh9lx15xdNMlIUzhfyO3T4VQqHZQbvQss32meXuA2cRxkQvdDxR5Yok2ynuDmPMEqAAa6PFsT3n4URYRlO2RJSpOdnc73IjToqEu4FnjTGfw1nMO809MIb/r0SQyWU6sMAYc4ox5hfAfcB1dt8CYJKIHG8ck8cu4FRjTGkMZOsuct0LfBPCEgW+g+MC/B0rzwz7V6L8XbabWKdquEpEZolIL2PMLpwF2WdwAmWOFxHXrcr9R7kh4jVuO3R9DpFElaudsqXh5Ka53h46G+jtytiVsiWiTG2UbYYrmzGmzhjzjjHmaXvoNGCue54Y/r/iIlMj2U6zB8o69gAABNNJREFU39VbwGOe3Qdx/PIBFuP4xv9eRLKBicB2EcmMhmzdVK4SnNlByGvIXv+XwM0iUgpMs8+RhMgq6yXqykAcBorIO8DVOFF2fxWRvsbJW1IFvImziHIGOFrVeulUWhlnuu1HulwdkG22laEWeAnIFpH3gCtwXNP2H6kydVC2Mxode7JdVDwFeOVIlqkF2a7EMedlGmP2SINb5kArH8aYvcaY+3Aevg/jeIf92n4OlaupXEF73FE4awQfACcbY/6WiIoAiO6aAZBk/47ByRQIjl3vj8Dzjfr+AEeD5uL8k9321J4iVydkywMybFsGnnw1R6pMXfBdumtBg4DzjnSZ2iObp8/LwJl2u5/9m0x0HDaONLl6u/IBp0fju+zyzxqtGw74FfBrHLv6Z4FHPPt9wF5glqctG8fmtgRncXZQT5GrC2T7yMo2+EiXqYu/yyFHukwdlQ0n79HDwDDgLpxF0C5PmnaEy9U7Gt9ltF5dbiYSJ3hmGc50aRNOXu564HSxiyfGsaP9zL5czsexLa8CJpkujrpLVLm6SLaVVrZdR7JMXSib+10WHckydVC2n9vD0nEya76Fk1LiTGPMIZWrXXKVdKVcUScKGvUU4Mue93/BCb74CrDMo1UH4CyiFdq2C3G8AKKi9RJVrkSVLRFlSmTZElGmTsg2BMct+FFgqsqVGHJF+xWNf2QmjreNa0f7InC33V4JfMduTweeitkHTVC5ElW2RJQpkWVLRJk6KNvTKldiyhXtV5ebiYwxVcaYWtMQfn0WUGy3v4qTxuEVnDqky6FpiuNokKhyJapsiShTIsuWiDJ1ULZlsZJN5Uosklvv0jHEScxkcHKsvGSby3GqMx0NbDXWnmysmo0FiSpXosqWiDIlsmyJKFOiy6ZyJQbRjDMI4iQnOwBMtpr0diBojHnfRGFhsZvLlaiyJaJMiSxbIsqU6LKpXIlANG1QOEFZQeB94Jpo27y6u1yJKlsiypTIsiWiTIkum8oV/5ebTCkqiMgQnMRRvzdONGpCkKhyQWLKlogyuSSibIkok0uiyqZyxZ+oKgNFURSle5BoxW0URVGUOKDKQFEURVFloCiKoqgyUBRFUVBloCiKoqDKQFHahIgERGSliKwTkVUi8iOx1axaOKZQRK6MlYyK0hlUGShK26g2xkw1xkzEyVVzLk7h9ZYoxKmKpSgJj8YZKEobEJEKY0y25/1InAI+fYHhOLVws+zuG4wxH4rIImA8sBV4BLgfuAenyH0a8GdjzN9j9iEUpQVUGShKG2isDGzbYWAsTvKyoDGmRkRG46Soni4ipwE3GmM+Y/tfh1Om8ZfiFFT/ALjUGLM1ph9GUSIQtaylitKDSAH+JCJTgQBOvdxInI2T8OwS+z4XGI0zc1CUuKLKQFE6gDUTBYD9OGsH+4ApOOtwNc0dhlMYZV5MhFSUdqALyIrSTkSkAPgb8Cfj2FlzgT3GqYv7ZZxi6uCYj3I8h84DviUiKfY8Y0QkC0VJAHRmoChtI0NEVuKYhPw4C8a/t/v+AvxHRK4C5gKVtn01EBCRVcC/gPtwPIyW28pYxcBFsfoAitISuoCsKIqiqJlIURRFUWWgKIqioMpAURRFQZWBoiiKgioDRVEUBVUGiqIoCqoMFEVRFOD/A0zbj9uofIzGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Close\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h7T4gW_W-FmZ"
   },
   "source": [
    "## 상장종목 목록 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "gWgVQdSx5HMT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2612, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 이전 강의에서 저장해 놓았던 csv 파일로 분석을 합니다.\n",
    "# colab 사용자의 경우 이미 csv 파일이 지워졌을 수 있기 때문에 파일이 없을 경우 아래 셀에서 다시 load하면 됩니다.\n",
    "df_krx = pd.read_csv(\"krx.csv\")\n",
    "df_krx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "muwUkDSF5HMT"
   },
   "outputs": [],
   "source": [
    "# 위 CSV 파일이 로드되지 않아 당황스러우신가요? 그럼 아래 주석을 풀고 다시 KRX 데이터를 받아오세요.\n",
    "# colab의 경우 일정 시간이 지나면 런타임이 초기화 되기 때문에 다시 로드하면 데이터를 가져올 수 있습니다.\n",
    "# df_krx = fdr.StockListing('KRX')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hbBmLbpY7vKY"
   },
   "source": [
    "## 상장종목 목록 사용하기\n",
    "* 종목명과 심볼을 찾기 위해 상장종목 목록을 사용합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "U0n_cZGA5HMT"
   },
   "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>Name</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": [
       "   Symbol    Name\n",
       "0  060310      3S\n",
       "1  095570  AJ네트웍스\n",
       "2  006840   AK홀딩스\n",
       "3  054620  APS홀딩스\n",
       "4  265520   AP시스템"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame 에 어떤 데이터가 들어있는 head()로 일부 데이터만 가져와 봅니다.\n",
    "df_krx[[\"Symbol\", \"Name\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"red\">빅히트 사명이 하이브로 변경되었습니다. 하이브로 실습해 주세요.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "OJtBeoeo5HMT",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'352820'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_krx의 Name column 에서 '하이브'에 해당하는 row를 전부 가져오는데 Name column만 가져와\n",
    "# list 형태로 index 번호는 빼고 ['하이브'] 만 가져오게 되고 [0]을 통해 list에 있는 string만 가져오게 됩니다.\n",
    "# 하이브 (series) -> ['하이브'] (list) -> '하이브' (string)\n",
    "# item_name = \"하이브\"\n",
    "# df_krx.loc[df_krx[\"Name\"] == item_name, \"Symbol\"].tolist()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "2YfQlf9U5HMU"
   },
   "outputs": [],
   "source": [
    "# '히트'라는 이름을 가진 상장사가 있는지 확인해 보기\n",
    "# df_krx.loc[df_krx[\"Name\"] == \"히트\", \"Symbol\"].tolist()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vaM5_Hkd5HMU"
   },
   "source": [
    "## 종목명으로 종목 코드를 받아오는 함수 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "6EB27Rkx5HMU"
   },
   "outputs": [],
   "source": [
    "# 종목명으로 종목코드 받아오는 함수 만들기\n",
    "# df_krx의 Name column에서 같은 값이 있으면 \n",
    "# row의 Symbol column의 값을 list로 name_list에 저장하기\n",
    "# item_code_by_item_name 함수를 만듭니다.\n",
    "\n",
    "def item_code_by_item_name(item_name):\n",
    "    \"\"\"\n",
    "    종목명을 받아 종목코드를 찾아 반환하는 함수\n",
    "    \"\"\"\n",
    "    item_code_list = df_krx.loc[df_krx[\"Name\"] == item_name, \"Symbol\"].tolist()\n",
    "    if len(item_code_list) > 0:\n",
    "        item_code = item_code_list[0]\n",
    "        return item_code\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'352820'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_code_by_item_name(\"하이브\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "oMwl2Dzq5HMU"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 네이버로 등록된 상장사는 없습니다.\n",
    "# 잘못 입력된 텍스트에 대해 예외처리를 하는지 확인합니다.\n",
    "item_code_by_item_name(\"네이버\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "p8uKKoLUPwVK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'035420'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 네이버는 NAVER로 등록되어 있습니다.\n",
    "item_code_by_item_name(\"NAVER\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'005930'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 삼성전자\n",
    "item_code_by_item_name(\"삼성전자\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "jMv5iz9a5HMU"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'068270'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 셀트리온과 카카오게임즈도 확인하여 봅니다.\n",
    "item_code_by_item_name(\"셀트리온\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'293490'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_code_by_item_name(\"카카오게임즈\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fdr.DataReader(item_code_by_item_name(\"LG화학\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wwe4qoor5HMU"
   },
   "source": [
    "## 종목명으로 일별시세를 받아오는 함수 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "id": "GRQLNGdv5HMU"
   },
   "outputs": [],
   "source": [
    "# item_code_by_item_name : 종목명으로 종목번호 가져오기\n",
    "# find_item_list : 종목번호로 해당연도 데이터 가져오기\n",
    "# find_item_list는 year 값이 없으면 기본으로 2020년 데이터를 선택\n",
    "\n",
    "def find_item_list(item_name, year=2020):\n",
    "    \"\"\"\n",
    "    종목명을 넘겨주면 일별시세를 반환하는 함수\n",
    "    내부에서 종목명으로 종목코드를 반환하는 함수(item_code_by_item_name)로\n",
    "    종목의 시세를 수집합니다.\n",
    "    \"\"\"\n",
    "    item_code = item_code_by_item_name(item_name)\n",
    "    if item_code:\n",
    "        df_day = fdr.DataReader(item_code, str(year))\n",
    "        return df_day\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>155821</td>\n",
       "      <td>157623</td>\n",
       "      <td>155220</td>\n",
       "      <td>155621</td>\n",
       "      <td>30777</td>\n",
       "      <td>0.002583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>155620</td>\n",
       "      <td>155620</td>\n",
       "      <td>153617</td>\n",
       "      <td>153618</td>\n",
       "      <td>51766</td>\n",
       "      <td>-0.012871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>155220</td>\n",
       "      <td>162230</td>\n",
       "      <td>154419</td>\n",
       "      <td>162230</td>\n",
       "      <td>120359</td>\n",
       "      <td>0.056061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>162230</td>\n",
       "      <td>162230</td>\n",
       "      <td>158424</td>\n",
       "      <td>158425</td>\n",
       "      <td>68724</td>\n",
       "      <td>-0.023454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>158424</td>\n",
       "      <td>160427</td>\n",
       "      <td>157423</td>\n",
       "      <td>160027</td>\n",
       "      <td>50269</td>\n",
       "      <td>0.010112</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>282000</td>\n",
       "      <td>282500</td>\n",
       "      <td>280000</td>\n",
       "      <td>281000</td>\n",
       "      <td>537848</td>\n",
       "      <td>-0.003546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>281500</td>\n",
       "      <td>285000</td>\n",
       "      <td>280500</td>\n",
       "      <td>283000</td>\n",
       "      <td>506090</td>\n",
       "      <td>0.007117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>285000</td>\n",
       "      <td>293000</td>\n",
       "      <td>284000</td>\n",
       "      <td>292500</td>\n",
       "      <td>1190327</td>\n",
       "      <td>0.033569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>291500</td>\n",
       "      <td>294000</td>\n",
       "      <td>285500</td>\n",
       "      <td>293000</td>\n",
       "      <td>947178</td>\n",
       "      <td>0.001709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>291500</td>\n",
       "      <td>292000</td>\n",
       "      <td>286500</td>\n",
       "      <td>287500</td>\n",
       "      <td>657299</td>\n",
       "      <td>-0.018771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>983 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close   Volume    Change\n",
       "Date                                                         \n",
       "2017-01-02  155821  157623  155220  155621    30777  0.002583\n",
       "2017-01-03  155620  155620  153617  153618    51766 -0.012871\n",
       "2017-01-04  155220  162230  154419  162230   120359  0.056061\n",
       "2017-01-05  162230  162230  158424  158425    68724 -0.023454\n",
       "2017-01-06  158424  160427  157423  160027    50269  0.010112\n",
       "...            ...     ...     ...     ...      ...       ...\n",
       "2020-12-28  282000  282500  280000  281000   537848 -0.003546\n",
       "2020-12-29  281500  285000  280500  283000   506090  0.007117\n",
       "2020-12-30  285000  293000  284000  292500  1190327  0.033569\n",
       "2021-01-04  291500  294000  285500  293000   947178  0.001709\n",
       "2021-01-05  291500  292000  286500  287500   657299 -0.018771\n",
       "\n",
       "[983 rows x 6 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_item_list(\"NAVER\", 2017)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6UCk0UYn9yXR"
   },
   "source": [
    "### 종목명으로 일별 시세를 잘 가져오는지 확인하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "id": "fFnQ4fR75HMU"
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>321000</td>\n",
       "      <td>321000</td>\n",
       "      <td>313500</td>\n",
       "      <td>314000</td>\n",
       "      <td>139496</td>\n",
       "      <td>-0.011024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>317000</td>\n",
       "      <td>318500</td>\n",
       "      <td>310500</td>\n",
       "      <td>311000</td>\n",
       "      <td>165030</td>\n",
       "      <td>-0.009554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>308000</td>\n",
       "      <td>311000</td>\n",
       "      <td>306500</td>\n",
       "      <td>307000</td>\n",
       "      <td>138154</td>\n",
       "      <td>-0.012862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>306000</td>\n",
       "      <td>313500</td>\n",
       "      <td>305000</td>\n",
       "      <td>311000</td>\n",
       "      <td>133788</td>\n",
       "      <td>0.013029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>306000</td>\n",
       "      <td>307000</td>\n",
       "      <td>301500</td>\n",
       "      <td>302000</td>\n",
       "      <td>225471</td>\n",
       "      <td>-0.028939</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>818000</td>\n",
       "      <td>824000</td>\n",
       "      <td>807000</td>\n",
       "      <td>814000</td>\n",
       "      <td>264888</td>\n",
       "      <td>-0.004890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>817000</td>\n",
       "      <td>827000</td>\n",
       "      <td>805000</td>\n",
       "      <td>813000</td>\n",
       "      <td>392849</td>\n",
       "      <td>-0.001229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>813000</td>\n",
       "      <td>824000</td>\n",
       "      <td>805000</td>\n",
       "      <td>824000</td>\n",
       "      <td>339889</td>\n",
       "      <td>0.013530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>849000</td>\n",
       "      <td>900000</td>\n",
       "      <td>835000</td>\n",
       "      <td>889000</td>\n",
       "      <td>858451</td>\n",
       "      <td>0.078883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>891000</td>\n",
       "      <td>893000</td>\n",
       "      <td>875000</td>\n",
       "      <td>878000</td>\n",
       "      <td>276665</td>\n",
       "      <td>-0.012373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close  Volume    Change\n",
       "Date                                                        \n",
       "2020-01-02  321000  321000  313500  314000  139496 -0.011024\n",
       "2020-01-03  317000  318500  310500  311000  165030 -0.009554\n",
       "2020-01-06  308000  311000  306500  307000  138154 -0.012862\n",
       "2020-01-07  306000  313500  305000  311000  133788  0.013029\n",
       "2020-01-08  306000  307000  301500  302000  225471 -0.028939\n",
       "...            ...     ...     ...     ...     ...       ...\n",
       "2020-12-28  818000  824000  807000  814000  264888 -0.004890\n",
       "2020-12-29  817000  827000  805000  813000  392849 -0.001229\n",
       "2020-12-30  813000  824000  805000  824000  339889  0.013530\n",
       "2021-01-04  849000  900000  835000  889000  858451  0.078883\n",
       "2021-01-05  891000  893000  875000  878000  276665 -0.012373\n",
       "\n",
       "[250 rows x 6 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LG화학의 데이터를 가져옵니다.\n",
    "find_item_list(\"LG화학\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "id": "r93FJRnk5HMU"
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>270000</td>\n",
       "      <td>351000</td>\n",
       "      <td>253500</td>\n",
       "      <td>258000</td>\n",
       "      <td>6557212</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-16</th>\n",
       "      <td>253000</td>\n",
       "      <td>253000</td>\n",
       "      <td>199000</td>\n",
       "      <td>200500</td>\n",
       "      <td>4504395</td>\n",
       "      <td>-0.222868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-19</th>\n",
       "      <td>198500</td>\n",
       "      <td>202500</td>\n",
       "      <td>187000</td>\n",
       "      <td>189000</td>\n",
       "      <td>1977996</td>\n",
       "      <td>-0.057357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-20</th>\n",
       "      <td>190000</td>\n",
       "      <td>193500</td>\n",
       "      <td>176000</td>\n",
       "      <td>182500</td>\n",
       "      <td>1811616</td>\n",
       "      <td>-0.034392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-21</th>\n",
       "      <td>180500</td>\n",
       "      <td>185500</td>\n",
       "      <td>177000</td>\n",
       "      <td>179000</td>\n",
       "      <td>1658643</td>\n",
       "      <td>-0.019178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-22</th>\n",
       "      <td>176500</td>\n",
       "      <td>185000</td>\n",
       "      <td>175500</td>\n",
       "      <td>180000</td>\n",
       "      <td>1193994</td>\n",
       "      <td>0.005587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-23</th>\n",
       "      <td>183000</td>\n",
       "      <td>184000</td>\n",
       "      <td>171500</td>\n",
       "      <td>172500</td>\n",
       "      <td>905547</td>\n",
       "      <td>-0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>173000</td>\n",
       "      <td>173500</td>\n",
       "      <td>156000</td>\n",
       "      <td>156000</td>\n",
       "      <td>987951</td>\n",
       "      <td>-0.095652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-27</th>\n",
       "      <td>154500</td>\n",
       "      <td>166000</td>\n",
       "      <td>152500</td>\n",
       "      <td>162500</td>\n",
       "      <td>1118487</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-28</th>\n",
       "      <td>161500</td>\n",
       "      <td>163500</td>\n",
       "      <td>157500</td>\n",
       "      <td>158500</td>\n",
       "      <td>581195</td>\n",
       "      <td>-0.024615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-29</th>\n",
       "      <td>155500</td>\n",
       "      <td>161500</td>\n",
       "      <td>154500</td>\n",
       "      <td>157000</td>\n",
       "      <td>518248</td>\n",
       "      <td>-0.009464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-30</th>\n",
       "      <td>149000</td>\n",
       "      <td>151500</td>\n",
       "      <td>142000</td>\n",
       "      <td>142000</td>\n",
       "      <td>1142310</td>\n",
       "      <td>-0.095541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>142500</td>\n",
       "      <td>146500</td>\n",
       "      <td>141000</td>\n",
       "      <td>143500</td>\n",
       "      <td>412881</td>\n",
       "      <td>0.010563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03</th>\n",
       "      <td>144500</td>\n",
       "      <td>152000</td>\n",
       "      <td>144500</td>\n",
       "      <td>152000</td>\n",
       "      <td>689435</td>\n",
       "      <td>0.059233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04</th>\n",
       "      <td>153500</td>\n",
       "      <td>153500</td>\n",
       "      <td>147000</td>\n",
       "      <td>149000</td>\n",
       "      <td>329773</td>\n",
       "      <td>-0.019737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05</th>\n",
       "      <td>150500</td>\n",
       "      <td>173000</td>\n",
       "      <td>150000</td>\n",
       "      <td>164000</td>\n",
       "      <td>1565701</td>\n",
       "      <td>0.100671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06</th>\n",
       "      <td>164500</td>\n",
       "      <td>166000</td>\n",
       "      <td>159500</td>\n",
       "      <td>162000</td>\n",
       "      <td>413106</td>\n",
       "      <td>-0.012195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>164000</td>\n",
       "      <td>169500</td>\n",
       "      <td>161000</td>\n",
       "      <td>163000</td>\n",
       "      <td>429226</td>\n",
       "      <td>0.006173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-10</th>\n",
       "      <td>168500</td>\n",
       "      <td>171500</td>\n",
       "      <td>166500</td>\n",
       "      <td>168000</td>\n",
       "      <td>491183</td>\n",
       "      <td>0.030675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-11</th>\n",
       "      <td>170500</td>\n",
       "      <td>172000</td>\n",
       "      <td>163000</td>\n",
       "      <td>164000</td>\n",
       "      <td>295999</td>\n",
       "      <td>-0.023810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-12</th>\n",
       "      <td>164000</td>\n",
       "      <td>164000</td>\n",
       "      <td>156000</td>\n",
       "      <td>157500</td>\n",
       "      <td>326268</td>\n",
       "      <td>-0.039634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-13</th>\n",
       "      <td>156500</td>\n",
       "      <td>162500</td>\n",
       "      <td>156000</td>\n",
       "      <td>160000</td>\n",
       "      <td>224605</td>\n",
       "      <td>0.015873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-16</th>\n",
       "      <td>155500</td>\n",
       "      <td>172000</td>\n",
       "      <td>154500</td>\n",
       "      <td>171500</td>\n",
       "      <td>1162127</td>\n",
       "      <td>0.071875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-17</th>\n",
       "      <td>169000</td>\n",
       "      <td>183000</td>\n",
       "      <td>165500</td>\n",
       "      <td>179500</td>\n",
       "      <td>925185</td>\n",
       "      <td>0.046647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-18</th>\n",
       "      <td>181500</td>\n",
       "      <td>185500</td>\n",
       "      <td>178000</td>\n",
       "      <td>181500</td>\n",
       "      <td>418979</td>\n",
       "      <td>0.011142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-19</th>\n",
       "      <td>181500</td>\n",
       "      <td>182500</td>\n",
       "      <td>176000</td>\n",
       "      <td>180000</td>\n",
       "      <td>287615</td>\n",
       "      <td>-0.008264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-20</th>\n",
       "      <td>182000</td>\n",
       "      <td>193000</td>\n",
       "      <td>178000</td>\n",
       "      <td>184500</td>\n",
       "      <td>822190</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-23</th>\n",
       "      <td>185000</td>\n",
       "      <td>186000</td>\n",
       "      <td>178000</td>\n",
       "      <td>180000</td>\n",
       "      <td>404421</td>\n",
       "      <td>-0.024390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-24</th>\n",
       "      <td>181000</td>\n",
       "      <td>181500</td>\n",
       "      <td>174000</td>\n",
       "      <td>181000</td>\n",
       "      <td>387623</td>\n",
       "      <td>0.005556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-25</th>\n",
       "      <td>183500</td>\n",
       "      <td>184500</td>\n",
       "      <td>172500</td>\n",
       "      <td>174000</td>\n",
       "      <td>441052</td>\n",
       "      <td>-0.038674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-26</th>\n",
       "      <td>177000</td>\n",
       "      <td>181500</td>\n",
       "      <td>175000</td>\n",
       "      <td>176000</td>\n",
       "      <td>343663</td>\n",
       "      <td>0.011494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-27</th>\n",
       "      <td>178000</td>\n",
       "      <td>178500</td>\n",
       "      <td>175000</td>\n",
       "      <td>178000</td>\n",
       "      <td>128351</td>\n",
       "      <td>0.011364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>178000</td>\n",
       "      <td>181000</td>\n",
       "      <td>176500</td>\n",
       "      <td>179500</td>\n",
       "      <td>177706</td>\n",
       "      <td>0.008427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>182500</td>\n",
       "      <td>185000</td>\n",
       "      <td>179500</td>\n",
       "      <td>181000</td>\n",
       "      <td>328175</td>\n",
       "      <td>0.008357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-02</th>\n",
       "      <td>183000</td>\n",
       "      <td>183000</td>\n",
       "      <td>180000</td>\n",
       "      <td>182000</td>\n",
       "      <td>195743</td>\n",
       "      <td>0.005525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-03</th>\n",
       "      <td>183500</td>\n",
       "      <td>190500</td>\n",
       "      <td>182000</td>\n",
       "      <td>189500</td>\n",
       "      <td>666358</td>\n",
       "      <td>0.041209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-04</th>\n",
       "      <td>189000</td>\n",
       "      <td>189500</td>\n",
       "      <td>185000</td>\n",
       "      <td>187500</td>\n",
       "      <td>258344</td>\n",
       "      <td>-0.010554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-07</th>\n",
       "      <td>188500</td>\n",
       "      <td>188500</td>\n",
       "      <td>180000</td>\n",
       "      <td>180500</td>\n",
       "      <td>254368</td>\n",
       "      <td>-0.037333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-08</th>\n",
       "      <td>180000</td>\n",
       "      <td>183500</td>\n",
       "      <td>177000</td>\n",
       "      <td>182000</td>\n",
       "      <td>203373</td>\n",
       "      <td>0.008310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-09</th>\n",
       "      <td>183000</td>\n",
       "      <td>183000</td>\n",
       "      <td>180000</td>\n",
       "      <td>180000</td>\n",
       "      <td>156872</td>\n",
       "      <td>-0.010989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-10</th>\n",
       "      <td>179000</td>\n",
       "      <td>181500</td>\n",
       "      <td>177000</td>\n",
       "      <td>181500</td>\n",
       "      <td>388533</td>\n",
       "      <td>0.008333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-11</th>\n",
       "      <td>182000</td>\n",
       "      <td>182000</td>\n",
       "      <td>179000</td>\n",
       "      <td>180500</td>\n",
       "      <td>143946</td>\n",
       "      <td>-0.005510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-14</th>\n",
       "      <td>179500</td>\n",
       "      <td>179500</td>\n",
       "      <td>175000</td>\n",
       "      <td>175500</td>\n",
       "      <td>197662</td>\n",
       "      <td>-0.027701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-15</th>\n",
       "      <td>175500</td>\n",
       "      <td>175500</td>\n",
       "      <td>170500</td>\n",
       "      <td>172000</td>\n",
       "      <td>152204</td>\n",
       "      <td>-0.019943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-16</th>\n",
       "      <td>173000</td>\n",
       "      <td>174000</td>\n",
       "      <td>171000</td>\n",
       "      <td>173000</td>\n",
       "      <td>97010</td>\n",
       "      <td>0.005814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-17</th>\n",
       "      <td>169000</td>\n",
       "      <td>169000</td>\n",
       "      <td>160500</td>\n",
       "      <td>161500</td>\n",
       "      <td>1122966</td>\n",
       "      <td>-0.066474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-18</th>\n",
       "      <td>162500</td>\n",
       "      <td>164000</td>\n",
       "      <td>160500</td>\n",
       "      <td>162000</td>\n",
       "      <td>162232</td>\n",
       "      <td>0.003096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-21</th>\n",
       "      <td>162000</td>\n",
       "      <td>162000</td>\n",
       "      <td>157000</td>\n",
       "      <td>158500</td>\n",
       "      <td>161286</td>\n",
       "      <td>-0.021605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-22</th>\n",
       "      <td>157500</td>\n",
       "      <td>158500</td>\n",
       "      <td>154000</td>\n",
       "      <td>154500</td>\n",
       "      <td>166164</td>\n",
       "      <td>-0.025237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-23</th>\n",
       "      <td>154500</td>\n",
       "      <td>158000</td>\n",
       "      <td>152500</td>\n",
       "      <td>153000</td>\n",
       "      <td>149781</td>\n",
       "      <td>-0.009709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-24</th>\n",
       "      <td>153000</td>\n",
       "      <td>158500</td>\n",
       "      <td>151500</td>\n",
       "      <td>158000</td>\n",
       "      <td>132612</td>\n",
       "      <td>0.032680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>158500</td>\n",
       "      <td>159000</td>\n",
       "      <td>155000</td>\n",
       "      <td>156500</td>\n",
       "      <td>115532</td>\n",
       "      <td>-0.009494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>156500</td>\n",
       "      <td>160500</td>\n",
       "      <td>155000</td>\n",
       "      <td>158500</td>\n",
       "      <td>110675</td>\n",
       "      <td>0.012780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>158500</td>\n",
       "      <td>161000</td>\n",
       "      <td>157500</td>\n",
       "      <td>160000</td>\n",
       "      <td>117495</td>\n",
       "      <td>0.009464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>160000</td>\n",
       "      <td>160500</td>\n",
       "      <td>156000</td>\n",
       "      <td>158000</td>\n",
       "      <td>152926</td>\n",
       "      <td>-0.012500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>157000</td>\n",
       "      <td>157500</td>\n",
       "      <td>155500</td>\n",
       "      <td>156000</td>\n",
       "      <td>125137</td>\n",
       "      <td>-0.012658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close   Volume    Change\n",
       "Date                                                         \n",
       "2020-10-15  270000  351000  253500  258000  6557212       NaN\n",
       "2020-10-16  253000  253000  199000  200500  4504395 -0.222868\n",
       "2020-10-19  198500  202500  187000  189000  1977996 -0.057357\n",
       "2020-10-20  190000  193500  176000  182500  1811616 -0.034392\n",
       "2020-10-21  180500  185500  177000  179000  1658643 -0.019178\n",
       "2020-10-22  176500  185000  175500  180000  1193994  0.005587\n",
       "2020-10-23  183000  184000  171500  172500   905547 -0.041667\n",
       "2020-10-26  173000  173500  156000  156000   987951 -0.095652\n",
       "2020-10-27  154500  166000  152500  162500  1118487  0.041667\n",
       "2020-10-28  161500  163500  157500  158500   581195 -0.024615\n",
       "2020-10-29  155500  161500  154500  157000   518248 -0.009464\n",
       "2020-10-30  149000  151500  142000  142000  1142310 -0.095541\n",
       "2020-11-02  142500  146500  141000  143500   412881  0.010563\n",
       "2020-11-03  144500  152000  144500  152000   689435  0.059233\n",
       "2020-11-04  153500  153500  147000  149000   329773 -0.019737\n",
       "2020-11-05  150500  173000  150000  164000  1565701  0.100671\n",
       "2020-11-06  164500  166000  159500  162000   413106 -0.012195\n",
       "2020-11-09  164000  169500  161000  163000   429226  0.006173\n",
       "2020-11-10  168500  171500  166500  168000   491183  0.030675\n",
       "2020-11-11  170500  172000  163000  164000   295999 -0.023810\n",
       "2020-11-12  164000  164000  156000  157500   326268 -0.039634\n",
       "2020-11-13  156500  162500  156000  160000   224605  0.015873\n",
       "2020-11-16  155500  172000  154500  171500  1162127  0.071875\n",
       "2020-11-17  169000  183000  165500  179500   925185  0.046647\n",
       "2020-11-18  181500  185500  178000  181500   418979  0.011142\n",
       "2020-11-19  181500  182500  176000  180000   287615 -0.008264\n",
       "2020-11-20  182000  193000  178000  184500   822190  0.025000\n",
       "2020-11-23  185000  186000  178000  180000   404421 -0.024390\n",
       "2020-11-24  181000  181500  174000  181000   387623  0.005556\n",
       "2020-11-25  183500  184500  172500  174000   441052 -0.038674\n",
       "2020-11-26  177000  181500  175000  176000   343663  0.011494\n",
       "2020-11-27  178000  178500  175000  178000   128351  0.011364\n",
       "2020-11-30  178000  181000  176500  179500   177706  0.008427\n",
       "2020-12-01  182500  185000  179500  181000   328175  0.008357\n",
       "2020-12-02  183000  183000  180000  182000   195743  0.005525\n",
       "2020-12-03  183500  190500  182000  189500   666358  0.041209\n",
       "2020-12-04  189000  189500  185000  187500   258344 -0.010554\n",
       "2020-12-07  188500  188500  180000  180500   254368 -0.037333\n",
       "2020-12-08  180000  183500  177000  182000   203373  0.008310\n",
       "2020-12-09  183000  183000  180000  180000   156872 -0.010989\n",
       "2020-12-10  179000  181500  177000  181500   388533  0.008333\n",
       "2020-12-11  182000  182000  179000  180500   143946 -0.005510\n",
       "2020-12-14  179500  179500  175000  175500   197662 -0.027701\n",
       "2020-12-15  175500  175500  170500  172000   152204 -0.019943\n",
       "2020-12-16  173000  174000  171000  173000    97010  0.005814\n",
       "2020-12-17  169000  169000  160500  161500  1122966 -0.066474\n",
       "2020-12-18  162500  164000  160500  162000   162232  0.003096\n",
       "2020-12-21  162000  162000  157000  158500   161286 -0.021605\n",
       "2020-12-22  157500  158500  154000  154500   166164 -0.025237\n",
       "2020-12-23  154500  158000  152500  153000   149781 -0.009709\n",
       "2020-12-24  153000  158500  151500  158000   132612  0.032680\n",
       "2020-12-28  158500  159000  155000  156500   115532 -0.009494\n",
       "2020-12-29  156500  160500  155000  158500   110675  0.012780\n",
       "2020-12-30  158500  161000  157500  160000   117495  0.009464\n",
       "2021-01-04  160000  160500  156000  158000   152926 -0.012500\n",
       "2021-01-05  157000  157500  155500  156000   125137 -0.012658"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 빅히트의 데이터를 가져옵니다.\n",
    "# stock_daily 변수에 담습니다.\n",
    "stock_daily = find_item_list(\"빅히트\")\n",
    "stock_daily"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 개별종목 시세 시각화\n",
    "### pandas 의 plot으로 시각화 하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcce1e0d090>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stock_daily 변수에 할당된 수집된 데이터의 종가 그래프를 그립니다.\n",
    "stock_daily[\"Close\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcce1e887d0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 전체 데이터프레임을 plot() 으로 시각화 하면 수치 데이터 전체를 선그래프로 그립니다.\n",
    "stock_daily.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 서브플롯 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "id": "0TThx3Vq5HMU"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 수집한 종목의 선그래프를 Pandas 로 그립니다.\n",
    "# 스케일이 다르기 때문에 subplots를 사용해 그려봅니다.\n",
    "g = stock_daily.plot(subplots=True, figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 히스토그램 그리기\n",
    "h = stock_daily.hist(figsize=(10, 8), bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2축 그래프 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcce17cc510>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# secondary_y 옵션을 통한 종가와 거래량에 대한 2축 그래프 그리기\n",
    "stock_daily[[\"Close\", \"Volume\"]].plot(secondary_y=\"Volume\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 과학적 기수법\n",
    "* 참고 : E notation\n",
    "[과학적 기수법 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EA%B3%BC%ED%95%99%EC%A0%81_%EA%B8%B0%EC%88%98%EB%B2%95)\n",
    "\n",
    "과학적 기수법, 과학적 표기법(scientific notation, scientific form, standard index form, standard form)은 너무 크거나 너무 작은 숫자들을 십진법으로 편하게 작성하여 표현하는 방법이다. 과학자, 수학자, 공학자들이 공통적으로 사용하는데, 부분적인 이유는 특정한 산술을 단순화시켜 주기 때문이다. 과학 계산기에서는 \"SCI\" 디스플레이 모드라는 이름으로 알려져 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1,000,000.0'"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format(1e6, \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1,000,000'"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format(10**6,\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>5.600000e+01</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>173276.785714</td>\n",
       "      <td>178107.142857</td>\n",
       "      <td>167866.071429</td>\n",
       "      <td>171205.357143</td>\n",
       "      <td>6.998942e+05</td>\n",
       "      <td>-0.007993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21667.722044</td>\n",
       "      <td>28720.552395</td>\n",
       "      <td>17344.763703</td>\n",
       "      <td>17475.899453</td>\n",
       "      <td>1.066315e+06</td>\n",
       "      <td>0.045925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>142500.000000</td>\n",
       "      <td>146500.000000</td>\n",
       "      <td>141000.000000</td>\n",
       "      <td>142000.000000</td>\n",
       "      <td>9.701000e+04</td>\n",
       "      <td>-0.222868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>158250.000000</td>\n",
       "      <td>162375.000000</td>\n",
       "      <td>155875.000000</td>\n",
       "      <td>158500.000000</td>\n",
       "      <td>1.651810e+05</td>\n",
       "      <td>-0.024100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>173000.000000</td>\n",
       "      <td>174750.000000</td>\n",
       "      <td>168500.000000</td>\n",
       "      <td>172250.000000</td>\n",
       "      <td>3.656430e+05</td>\n",
       "      <td>-0.008264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>182000.000000</td>\n",
       "      <td>184125.000000</td>\n",
       "      <td>177000.000000</td>\n",
       "      <td>180500.000000</td>\n",
       "      <td>8.430292e+05</td>\n",
       "      <td>0.010853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>270000.000000</td>\n",
       "      <td>351000.000000</td>\n",
       "      <td>253500.000000</td>\n",
       "      <td>258000.000000</td>\n",
       "      <td>6.557212e+06</td>\n",
       "      <td>0.100671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open           High            Low          Close  \\\n",
       "count      56.000000      56.000000      56.000000      56.000000   \n",
       "mean   173276.785714  178107.142857  167866.071429  171205.357143   \n",
       "std     21667.722044   28720.552395   17344.763703   17475.899453   \n",
       "min    142500.000000  146500.000000  141000.000000  142000.000000   \n",
       "25%    158250.000000  162375.000000  155875.000000  158500.000000   \n",
       "50%    173000.000000  174750.000000  168500.000000  172250.000000   \n",
       "75%    182000.000000  184125.000000  177000.000000  180500.000000   \n",
       "max    270000.000000  351000.000000  253500.000000  258000.000000   \n",
       "\n",
       "             Volume     Change  \n",
       "count  5.600000e+01  55.000000  \n",
       "mean   6.998942e+05  -0.007993  \n",
       "std    1.066315e+06   0.045925  \n",
       "min    9.701000e+04  -0.222868  \n",
       "25%    1.651810e+05  -0.024100  \n",
       "50%    3.656430e+05  -0.008264  \n",
       "75%    8.430292e+05   0.010853  \n",
       "max    6.557212e+06   0.100671  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_daily.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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